Embracing Semi-supervised Domain Adaptation for Federated Knowledge Transfer

Given rapidly changing machine learning environments and expensive data labeling, semi-supervised domain adaptation (SSDA) is imperative when the labeled data from the source domain is statistically different from the partially labeled target data. Most prior SSDA research is centrally performed, requiring access to both source and target data. However, data in many fields nowadays is generated by distributed end devices. Due to privacy concerns, the data might be locally stored and cannot be shared, resulting in the ineffectiveness of existing SSDA. This paper proposes an innovative approach to achieve SSDA over multiple distributed and confidential datasets, named by Federated Semi-Supervised Domain Adaptation (FSSDA). FSSDA integrates SSDA with federated learning based on strategically designed knowledge distillation techniques, whose efficiency is improved by performing source and target training in parallel. Moreover, FSSDA controls the amount of knowledge transferred across domains by properly selecting a key parameter, i.e., the imitation parameter. Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios. Extensive experiments demonstrate the effectiveness and efficiency of FSSDA design

Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator

Spiking Neural Networks (SNNs) are energy-efficient artificial neural network models that can carry out data-intensive applications. Energy consumption, latency, and memory bottleneck are some of the major issues that arise in machine learning applications due to their data-demanding nature. Memristor-enabled Computing-In-Memory (CIM) architectures have been able to tackle the memory wall issue, eliminating the energy and time-consuming movement of data. In this work we develop a scalable CIM-based SNN architecture with our fabricated two-layer memristor crossbar array. In addition to having an enhanced heat dissipation capability, our memristor exhibits substantial enhancement of 10% to 66% in design area, power and latency compared to state-of-the-art memristors. This design incorporates an inter-spike interval (ISI) encoding scheme due to its high information density to convert the incoming input signals into spikes. Furthermore, we include a time-to-first-spike (TTFS) based output processing stage for its energy-efficiency to carry out the final classification. With the combination of ISI, CIM and TTFS, this network has a competitive inference speed of 2μs/image and can successfully classify handwritten digits with 2.9mW of power and 2.51pJ energy per spike. The proposed architecture with the ISI encoding scheme can achieve ∼10% higher accuracy than those of other encoding schemes in the MNIST dataset

Michigan Tech Open Education Week Panel Discussion

Join Open Education experts at Michigan Technological University for a discussion on OE in celebration of OE Week 2024. Moderated by Annelise Doll and Dory Shaffer

TEMPORAL CHANGES IN VIDEO GAME PLAY AND SPATIAL VISUALIZATION SKILLS BY GENDER AMONG ENGINEERING STUDENTS

Spatial thinking is considered to be a higher-level skill that is critical to success in engineering and in computer science. Further, there are significant gender differences in 3-D spatial skills among first-year engineering students, favoring males. Playing 3-D video games is viewed by many as one activity that may lead to highly developed spatial skills; video gaming is also viewed as a means for attracting students to the field of computer science. In this study, engineering students were administered a test of 3-D mental rotation during first-year orientation and also completed a background questionnaire de-signed to assess the amount of time spent playing typical 3-D video games during their childhood. Both instruments were administered in 2009 as well as in 2019. Through this research, it was found that men have higher spatial skill levels and spend more time playing 3-D video games compared to their female counterparts. Further, the amount of time spent playing 3-D video games increased significantly for males but not for females between 2009 and 2019. Playing 3-D video games was significantly correlated with spatial skill levels but appeared to be more important for females than for males in terms of developing these skills. Results from this study will inform video game developers as they create games of broad appeal in fulfillment of a dual role: attracting a diverse body of students to computer science programs while also developing the 3-D spatial skills necessary for success in those fields

Distributed generation hosting capacity analysis: An approach using interval-affine arithmetic and power flow sensitivities

The climate change concerns, the decarbonization policies, and the technological advances allied to cost reduction form a set of favorable circumstances for large deployment of renewable-based distributed generation into the power distribution systems. However, the connection of distributed generation significantly affects the power system and its technical impacts must be evaluated. Therefore, the hosting capacity analysis has gained attention as it outputs the maximum amount of distributed generation that can be safely connected. The deterministic and stochastic methods are commonly found in the literature. However, there is a lack of hosting capacity studies based on interval analysis. Therefore, this paper proposes an interval/affine arithmetic-based hosting capacity framework, considering two performance indexes (overvoltage and ampacity), and uses a different approach for modeling generation and load curves as a finite set of correlated affine combinations. The proposed framework enables faster analysis and outputs results that are comparable to the ones from time series simulation. The proposed method also allows less conservative results, depending on the desired level of overvoltage or overload risk

Vision-Based Detection Method for Construction Site Monitoring by Integrating Data Augmentation and Semisupervised Learning

Training deep learning models for vision-based monitoring of construction sites usually requires a large amount of labeled data. Semisupervised learning methods can efficiently obtain unlabeled data with substantial cost savings. Thus, this paper proposes a semisupervised object detection method for construction site monitoring. Weather as well as strong and weak data augmentation are integrated to cope with the complex construction site conditions (weather changes, camera view shifts, and so on) by integrating semisupervised learning to leverage the valid information in unlabeled construction site images. To validate its effectiveness, the proposed method was tested on the Alberta Construction Image Data Set (ACID), a public data set for the construction research community. The experimental results revealed that the proposed method achieves an average accuracy [mean average precision (mAP)] of 81.1% when trained on only 3% of the labeled images. This study helps to significantly reduce the development cost of vision-based object detection models for construction sites

Simultaneous N2O Reduction and CO Oxidation over Pristine and Doped Molybdenum Phosphide (001) Surfaces: A Density Functional Theory Study

A spin-polarized density functional study has been performed to evaluate the favorability of reduction of N2O by oxidation of CO using pristine and doped molybdenum phosphide (MoP) as a catalytic surface. The stepwise mechanism, which comprises 4 steps, N2O dissociation (the rate-determining step), N2 desorption, CO oxidation, and CO2 desorption, has been explored in detail. Adsorption energy, charge transfer, relative energy, energy band structure, and projected density of states plots offer deeper insights into the simultaneous reduction and oxidation of N2O and CO, respectively. Four 3d transition metals have been considered for doping the MoP surface to further improve its catalytic performance. The energy barriers of N2O dissociation and CO oxidation over pristine and Cr-doped MoP surfaces have been evaluated and compared to those of previously reported catalysts. The activation of the N2O molecule that facilitates the breaking of the N-O bond is identified as the rate-determining step. Low desorption energy for the removal of the final products (N2 and CO2) ensures easy regeneration of the catalyst surface. The study offers ample evidence to exploit the Cr-doped MoP surface for simultaneous abatement of harmful N2O and CO gases by their respective conversion into N2 and CO2

Daily simulated lake temperature from 2040 to 2044 under the RCP 8.5 condition from the GLARM-based downscaling of IPSL

The simulated daily lake temperature of the Great Lakes from 2040 to 2044 under the RCP 8.5 condition from the GLARM-based downscaling of IPS

Daily simulated lake temperature from 2035 to 2039 under the RCP 4.5 condition from the ensemble average of the three GLARM-based downscaling

The simulated daily lake temperature of the Great Lakes from 2035 to 2039 under the RCP 4.5 condition from the ensemble average of the three GLARM-based downscalin

Daily simulated lake temperature from 2045 to 2049 under the RCP 4.5 condition from the GLARM-based downscaling of MPI

The simulated daily lake temperature of the Great Lakes from 2045 to 2049 under the RCP 4.5 condition from the GLARM-based downscaling of MP