Hours Of Running Time Research Articles

Evaluating a quantum-classical quantum Monte Carlo algorithm with Matchgate shadows

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins , ] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling postprocessing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical postprocessing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm. Published by the American Physical Society 2024

Ferroelectric Based Low Power MOSFET for DC/RF Applications: Machine Learning Assisted Statistical Variation Analysis

The analog/radio-frequency (RF) performance of a ferroelectric-based substrate metal oxide semiconductor field effect transistor (FE-MOSFET) with dielectric spacer was designed and proposed. The utilization of gate side wall spacers aims to mitigate short-channel effects (SCEs), and improve overall device performance. Simulation results demonstrate enhanced performance metrics, including improved transconductance (80%), reduced gate leakage (95.4%), and enhanced cutoff frequency (25%), making this design a promising candidate for next-generation high-performance analog and RF applications. Additionally, a novel machine learning (ML)-assisted approach is proposed for investigating the spacer-based FE-MOSFET to reduce the computational cost of numerical TCAD device simulations with the help of conventional- artificial neural network (C-ANN). This method is reported for the first-time ML-based C-ANN for Fe-based low-power MOSFET, matches the similar accuracy of physics-based TCAD with the fastest learning rate and fastest computational speed (in 95–100 s). An ML-based prediction replacement for physics-based TCAD is developed to save around 8–10 h of runtime for each iteration. Because ML predictions can never be 100% accurate, it is essential to ensure approximately zero mean-square error in the final results.

Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.

Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging due to the significant burden of estimating kinship between all pairs of individuals across datasets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us datasets. On a dataset of 200K individuals split between two parties, SF-Relate detects 94.9% of third-degree relatives, and 99.9% of second-degree or closer relatives, within 15 hours of runtime. Our work enables secure identification of relatives across large-scale genomic datasets.

Development and validation of the COVID-19 Hospitalized Patient Deterioration Index.

To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19. A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020. Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model. The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients. COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.

Impact of a generalised SVG-based large-scale super-resolution algorithm on the design of light-weight medical image segmentation DNNs

ABSTRACT Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network’s input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms.

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization.

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses.

Experimental quantum key distribution certified by Bell's theorem.

Cryptographic key exchange protocols traditionally rely on computational conjectures such as the hardness of prime factorization1 to provide security against eavesdropping attacks. Remarkably, quantum key distribution protocols such as the Bennett-Brassard scheme2 provide information-theoretic security against such attacks, a much stronger form of security unreachable by classical means. However, quantum protocols realized so far are subject to a new class of attacks exploiting a mismatch between the quantum states or measurements implemented and their theoretical modelling, as demonstrated in numerous experiments3-6. Here we present the experimental realization of a complete quantum key distribution protocol immune to these vulnerabilities, following Ekert's pioneering proposal7 to use entanglement to bound an adversary's information from Bell's theorem8. By combining theoretical developments with an improved optical fibre link generating entanglement between two trapped-ion qubits, we obtain 95,628 key bits with device-independent security9-12 from 1.5 million Bell pairs created during eight hours of run time. We take steps to ensure that information on the measurement results is inaccessible to an eavesdropper. These measurements are performed without space-like separation. Our result shows that provably secure cryptography under general assumptions is possible with real-world devices, and paves the way for further quantum information applications based on the device-independence principle.

High-speed fluorescence image-enabled cell sorting.

Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.

Stabilization of Pt on Carbon Supports Doped with Atomically-Dispersed Metal/Metal-Oxide Additives

The shift in focus of Proton Exchange Membrane Fuel Cell (PEMFC) development for use in heavy-duty truck applications has accelerated the need for highly durable and efficient Membrane Electrode Assemblies (MEA) in order to meet the long lifetime requirements (~30,000 hours of runtime or 1 million mile driving distance) and high efficiency to minimize the total cost of ownership dominated by the H2 fuel cost.1 These requirements for heavy-duty truck are far stringent compared to the light-duty vehicle applications (~8,000 hours of run time). This brings into focus the need to develop highly-durable catalyst and membrane materials to satisfy the longer lifetime demanded by heavy-duty trucks.The primary cause of cell voltage loss in an operating fuel cell largely arises from the electrochemical surface area (ECSA) loss of the Pt-based cathode catalyst layer. Two mechanisms, namely Pt migration/coalescence and dissolution/redeposition are responsible for the coarsening of Pt nanoparticles leading to surface area loss.2,3 In this presentation, we will explore the concept of using metal/metal-oxide (M/MOx) based dopants in the carbon support as additives to mitigate the Pt coarsening behavior. Specifically, we evaluate dopants that are in the form of M/MOx nanoclusters and/or atomically-dispersed M in the graphitic carbon lattice (M-Cy). We will discuss fuel cell MEA results by investigating the stability when subjected to accelerated voltage cycling tests along with a suite of ex situ characterizations to understand the chemical nature of the catalyst materials with dopants. Acknowledgements: This work is partially supported by U.S Department of Energy, Office of Energy Efficiency and Renewable Energy under grant DE-EE0008821. References Marcinkoski, J. et al. Hydrogen Class 8 Long Haul Truck Targets. Subsection of the Electrified Powertrain Roadmap. Technical Targets for Hydrogen-Fueled Long-Haul Tractor-Trailer Trucks. DOE Advanced Truck Technologies 1–31 (2019). Available at: https://www.hydrogen.energy.gov/pdfs/19006_hydrogen_class8_long_haul_truck_targets.pdf. Cherevko, S., Kulyk, N. & Mayrhofer, K. J. J. Durability of platinum-based fuel cell electrocatalysts: Dissolution of bulk and nanoscale platinum. Nano Energy 29, 275–298 (2016). Yu, K. et al. Degradation mechanisms of platinum nanoparticle catalysts in proton exchange membrane fuel cells: The role of particle size. Chem. Mater. 26, 5540–5548 (2014). Figure 1

Video Index Point Detection and Extraction Framework Using Custom YoloV4 Darknet Object Detection Model

The trend of learning from videos instead of documents has increased. There could be hundreds and thousands of videos on a single topic, with varying degrees of context, content, and depth of the topic. The literature claims that learners are nowadays less interested in viewing a complete video but prefers the topic of their interests. This develops the need for indexing of video lectures. Manual annotation or topic-wise indexing is not new in the case of videos. However, manual indexing is time-consuming due to the length of a typical video lecture and intricate storylines. Automatic indexing and annotation are, therefore, better and efficient solutions. This research aims to identify the need for automatic video indexing for better information retrieval and ease users navigating topics inside a video. The automatically identified topics are referred to as “Index Points.” 137-layer YoloV4 Darknet Neural Network creates a custom object detector model. The model is trained on approximately 6000 video frames and then tested on a suite of 50 videos of around 20 hours of run time. Shot Boundary detection is performed using Structural Similarity fused with a Binary Search Pattern algorithm which outperformed the state-of-the-art SSIM technique by reducing the processing time to approximately 21% and providing around 96% accuracy. Generation of accurate index points in terms of true positives and false negatives is detected through precision, recall, and F1 score, which varies between 60–80% for every video. The results show that the proposed algorithm successfully generates a digital index with reasonable accuracy in topic detection.

Bi-objective optimization of catheter positions for high-dose-rate prostate brachytherapy.

PurposeBi‐objective simultaneous optimization of catheter positions and dwell times for high‐dose‐rate (HDR) prostate brachytherapy, based directly on dose‐volume indices, has shown promising results. However, optimization with the state‐of‐the‐art evolutionary algorithm MO‐RV‐GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm.MethodsBi‐objective simultaneous optimization was performed with a GPU‐parallelized version of MO‐RV‐GOMEA. This optimization of catheter positions and dwell times was retrospectively applied to the data of 26 patients previously treated with HDR prostate brachytherapy for 8–16 catheters (steps of 2). Optimization of catheter positions using CVT was performed in seconds, after which optimization of only the dwell times using MO‐RV‐GOMEA was performed in 1 min.ResultsSimultaneous optimization of catheter positions and dwell times using MO‐RV‐GOMEA was performed in 5 min. For 16 down to 8 catheters (steps of 2), MO‐RV‐GOMEA found plans satisfying the planning‐aims for 20, 20, 18, 14, and 11 out of the 26 patients, respectively. CVT achieved this for 19, 17, 13, 9, and 2 patients, respectively. The P‐value for the difference between MO‐RV‐GOMEA and CVT was 0.023 for 16 catheters, 0.005 for 14 catheters, and <0.001 for 12, 10, and 8 catheters.ConclusionsWith bi‐objective simultaneous optimization on a GPU, high‐quality catheter positions can now be obtained within 5 min, which is clinically acceptable, but slower than CVT. For 16 catheters, the difference between MO‐RV‐GOMEA and CVT is clinically irrelevant. For 14 catheters and less, MO‐RV‐GOMEA outperforms CVT in finding plans satisfying all planning‐aims.

Sequential Online Dispatch in Design of Experiments for Single- and Multiple-Response Surrogate Modeling

As parallel computing becomes increasingly important in many real-world applications, a batch sequential experimental design (BSED), which adds a batch of computer experiments per iteration and runs these simulations in parallel, is gaining popularity in surrogate modeling. This article proposes sequential online dispatch in design of experiments (SODDE) for single- and multiple-response surrogate modeling when multiple processors work in parallel but not independently. The proposed method includes several unique features: 1) it works with any popular acquisition function to select a single new sample point at each iteration; 2) it minimizes the idle time of all processors; 3) it rapidly updates the surrogate model; and 4) it dynamically reconstructs the surrogate model when a simulation process aborts, minimizing the impact incurred by the abortion. The effectiveness of SODDE is evaluated in one mathematical example and one industrial problem. The latter problem considers blade design of horizontal axis wind turbines (HAWTs). The cost of finding blade geometry that results in the desired aerodynamic behavior of HAWTs is estimated for BSED and SODDE. Relative to BSED, SODDE reduces the costs by up to approximately 31%. Note to Practitioners —Computer simulation models are often used to represent the behavior of complex engineered systems, such as automobiles, aircraft, and wind turbines. Although running computer simulations allows analyzing the performance of these systems, the simulations can still be computationally costly (e.g., hours of runtime for numerically solving a large number of differential equations in one simulation), which makes the performance analysis task challenging. It is important to design an innovative simulation process that replaces the original expensive simulation models by cheap-to-build surrogate models, and at the same time, minimize the computational costs for building the surrogate models. The new method developed in this article allows experimental designers to run batch sequential iterations and run these iterations in parallel. This, in engineering practice, will help meet the need for long and complex experimental designs scenarios in a parallel computing environment, where the computer “up time” (or runtime) needs to be maximized to save time and ultimately, money. Cost analysis results from an industrial problem involving the inverse design of wind turbine blades suggest that the proposed method reduces the computational costs by up to approximately 31% over an existing method.

A column generation and a post optimization VNS heuristic for the vehicle routing problem with multiple time windows

The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of the Vehicle Routing Problem (VRP), where the customers have one or more time windows in which they can be visited. In this paper, we propose a Column Generation (CG) algorithm and a post optimization heuristic based on a Variable Neighborhood Search (VNS) to provide both lower and upper bounds for the cost of optimal solutions to VRPMTW. As in CG algorithms for VRP, the master problem is based on a Weighted Set Covering formulation. However, due to the multiple time windows, the pricing subproblem is an Elementary Shortest Path Problem with Multiple Time Windows and Capacity Constraints, which is more difficult to solve than the classical Elementary Shortest Path Problem with a Single Time Window and Capacity Constraints. Computational experiments were performed on 594 instances generated from classical Solomon instances with up to 17 customers. They showed that CG was able to produce lower bounds, within one hour of running time, for 66.7% of the instances. Besides, the post optimization heuristic was able to improve the solution provided by the VNS heuristic in 28.9%, finding integer optimal solutions for 39.9% of the instances. Moreover, for the instances where lower bounds are known, the average optimality gap was 6.0% on average.

Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass

Cosmological $N$-body simulations play a vital role in studying models for the evolution of the Universe. To compare to observations and make a scientific inference, statistic analysis on large simulation datasets, e.g., finding halos, obtaining multi-point correlation functions, is crucial. However, traditional in-memory methods for these tasks do not scale to the datasets that are forbiddingly large in modern simulations. Our prior paper proposes memory-efficient streaming algorithms that can find the largest halos in a simulation with up to $10^9$ particles on a small server or desktop. However, this approach fails when directly scaling to larger datasets. This paper presents a robust streaming tool that leverages state-of-the-art techniques on GPU boosting, sampling, and parallel I/O, to significantly improve performance and scalability. Our rigorous analysis of the sketch parameters improves the previous results from finding the centers of the $10^3$ largest halos to $\sim 10^4-10^5$, and reveals the trade-offs between memory, running time and number of halos. Our experiments show that our tool can scale to datasets with up to $\sim 10^{12}$ particles while using less than an hour of running time on a single GPU Nvidia GTX 1080.

Wear characteristics of a combustion liner for power generation gas turbine

Wear is the main degradation of the combustor components in gas turbine power generation. One of the most vital components in a gas turbine is the combustion liner which is often observed with severe wear damage. In this study, the combustion liner was made from Hastelloy X (a nickel-based superalloy material). The degradation generally occurred at the mating surfaces of the combustion liner which consisted of the fuel nozzle and a transition piece. The purpose of this study was to determine and characterise the dominant wear mechanisms at two different contact surfaces (connected to stainless steel and Nimonic 263) after being exposed to 8,000 hours of run time at high temperature and vibration. The worn surfaces were analysed and the wear mechanism was discussed.

The Performance of Electricity Producing of Dual Chamber Microbial Fuel Cells (MFCs) Using Wastewater of Tempe Industries

The performance of electricity producing of Dual Chamber Microbial Fuel Cells (MFCs) using wastewater of tempe industries without glucose addition (as control substrate) and with (2% and 4%) glucose addition was observed. The anode chamber contained a waste substrate and a cathode chamber contained a 0.1 M Potassium Permanganate electrolyte solution. The salt bridge was required to stabilize the charge between the cathode and anode chambers, and the electrodes attached to the anode and cathode chambers as the electron catcher. Voltages and currents were measured using multimeter. Optical Density measured at 486 nm wavelengths was performed to estimate bacterial growth activity. All of the cells were observed for 72 hours of running time. The results of Optical Density curves showed an increasing trend of absorbance during 72 hours of running time. These were in agreement with the resulting power density, which tended to increase until the 48th hour and then relatively stable especially for the substrate with 4% glucose addition. These MFCs system could also reduce COD by 1.52%, 9.76%, and 9.64% on control substrate, 2% glucose addition substrate, and 4% glucose addition substrate, respectively.

The Effect of Increasing Surface Area of Graphite Electrode on the Performance of Dual Chamber Microbial Fuel Cells

The effect of increasing surface area of graphite electrode on the performance of dual chamber Microbial Fuel Cells (MFC) was observed. The surface area of graphite electrode (anode and cathode) that was using in this experiment was 29.5cm2 and 44.5cm2 for the A and B reactor, respectively. The anode chamber contained mixed microorganism culture from real wastewater of textile industry and the cathode chamber contained 0.1M potassium permanganate electrolyte solution. The salt bridge was required to stabilize the charge between the anode and cathode chambers, and the electrodes attached to the anode and cathode chambers as the electron catcher. Both, the A and B reactor were observed for 72 hours of running time. The voltage and power density were found to increase with the increase in surface area of the graphite electrode. The highest power density was 93.93mWm-2 and 197.23mWm-2 that obtained at 36 hours and 48 hours on the A and B reactor, respectively. At the end of experiment, these MFCs system could also reduce COD by 28.6% and 15.6% on A and B reactor, respectively.

Optimal and suboptimal resource allocation techniques in cloud computing data centers

Cloud service providers are under constant pressure to improve performance, offer more diverse resource deployment options, and enhance application portability. To achieve these performance and cost objectives, providers need a comprehensive resource allocation system that handles both computational and network resources. A novel methodology is introduced to tackle the problem of allocating sufficient data center resources to client Virtual Machine (VM) reservation requests and connection scheduling requests. This needs to be done while achieving the providers’ objectives and minimizing the need for VM migration. In this work, the problem of resource allocation in cloud computing data centers is formulated as an optimization problem and solved. Moreover, a set of heuristic solutions are introduced and used as VM reservation and connection scheduling policies. A relaxed suboptimal solution based on decomposing the original problem is also presented. The experimentation results for a diverse set of network loads show that the relaxed solution has achieved promising levels for connection request average tardiness. The proposed solution is able to reach better performance levels than heuristic solutions without the burden of long hours of running time. This makes it a feasible candidate for solving problems with a much higher number of requests and wider data ranges compared to the optimal solution.

DACE: a scalable DP-means algorithm for clustering extremely large sequence data.

Advancements in next-generation sequencing technology have produced large amounts of reads at low cost in a short time. In metagenomics, 16S and 18S rRNA gene have been widely used as marker genes to profile diversity of microorganisms in environmental samples. Through clustering of sequencing reads we can determine both number of OTUs and their relative abundance. In many applications, clustering of very large sequencing data with high efficiency and accuracy is essential for downstream analysis. Here, we report a scalable D irichlet Process Means (DP-means) a lgorithm for c lustering e xtremely large sequencing data, termed . With an efficient random projection partition strategy for parallel clustering, DACE can cluster billions of sequences within a couple of hours. Experimental results show that DACE runs between 6 and 80 times faster than state-of-the-art programs, while maintaining overall better clustering accuracy. Using 80 cores, DACE clustered the Lake Taihu 16S rRNA gene sequencing data (∼316M reads, 30 GB) in 25 min, and the Ocean TARA Eukaryotic 18S rRNA gene sequencing data (∼500M reads, 88 GB) into ∼100 000 clusters within an hour. When applied to the IGC gene catalogs in human gut microbiome (∼10M genes), DACE produced 9.8M clusters with 52K redundant genes in 1.5 hours of running time. DACE is available at https://github.com/tinglab/DACE . tingchen@mail.tsinghua.edu.cn or ningchen@mail.tsinghua.edu.cn. Supplementary data are available at Bioinformatics online.

Flipping organic chemistry course: Possibilities and challenges

The flipped classroom approach was applied to an introductory organic chemistry course. A total of 76 video clips (15 hours of running time) were developed and delivered to 41 sophomores (21 females and 20 males) through Youtube in addition to the university's learning management system. The students were asked to preview the lecture contents before each class by watching a pre-class video. For in-class activities, exercise problems were presented to groups of 3-5 students. An instructor and a teaching assistant guided each group to solve problems cooperatively, monitored the students’ group activity and answered their questions. At the end of every chapter, the students were asked to evaluate their group work and personal preparedness for the class and also to write a short reflective journal. The muddiest point of each chapter, i.e., the topic posing the most difficulty to students’ understanding, was surveyed through Google Forms®. The students liked watching the videos before each class and performing student-centered, in-class group activities but a few limitations were also found and reported.