5,009 research outputs found
Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.
The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures
Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors
Flashpoint: A Low-latency Serverless Platform for Deep Learning Inference Serving
Recent breakthroughs in Deep Learning (DL) have led to high demand for executing inferences in interactive services such as ChatGPT and GitHub Copilot. However, these interactive services require low-latency inferences, which can only be met with GPUs and result in exorbitant operating costs. For instance, ChatGPT reportedly requires millions of U.S. dollars in cloud GPUs to serve its 1+ million users. A potential solution to meet low-latency requirements with acceptable costs is to use serverless platforms. These platforms automatically scale resources to meet user demands. However, current serverless systems have long cold starts which worsen with larger DL models and lead to poor performance during bursts of requests. Meanwhile, the demand for larger and larger DL models make it more challenging to deliver an acceptable user experience cost-effectively. While current systems over-provision GPUs to address this issue, they incur high costs in idle resources which greatly reduces the benefit of using a serverless platform.
In this thesis, we introduce Flashpoint, a GPU-based serverless platform that serves DL inferences with low latencies. Flashpoint achieves this by reducing cold start durations, especially for large DL models, making serverless computing feasible for latency-sensitive DL workloads. To reduce cold start durations, Flashpoint reduces download times by sourcing the DL model data from within the compute cluster rather than slow cloud storage. Additionally, Flashpoint minimizes in-cluster network congestion from redundant packet transfers of the same DL model to multiple machines with multicasting. Finally, Flashpoint also reduces cold start durations by automatically partitioning models and deploying them in parallel on multiple machines. The reduced cold start durations achieved by Flashpoint enable the platform to scale resource allocations elastically and complete requests with low latencies without over-provisioning expensive GPU resources.
We perform large-scale data center simulations that were parameterized with measurements our prototype implementations. We evaluate the system using six state-of-the-art DL models ranging from 499 MB to 11 GB in size. We also measure the performance of the system in representative real-world traces from Twitter and Microsoft Azure. Our results in the full-scale simulations show that Flashpoint achieves an arithmetic mean of 93.51% shorter average cold start durations, leading to 75.42% and 66.90% respective reductions in average and 99th percentile end-to-end request latencies across the DL models with the same amount of resources. These results show that Flashpoint boosts the performance of serving DL inferences on a serverless platform without increasing costs
Superconducting Circuit Architectures Based on Waveguide Quantum Electrodynamics
Quantum science and technology provides new possibilities in processing information, simulating novel materials, and answering fundamental questions beyond the reach of classical methods. Realizing these goals relies on the advancement of physical platforms, among which superconducting circuits have been one of the leading candidates offering complete control and read-out over individual qubits and the potential to scale up. However, most circuit-based multi-qubit architectures only include nearest-neighbor (NN) coupling between qubits, which limits the efficient implementation of low-overhead quantum error correction and access to a wide range of physical models using analog quantum simulation.
This challenge can be overcome by introducing non-local degrees of freedom. For example, photons in a shared channel between qubits can mediate long-range qubit-qubit coupling arising from light-matter interaction. In addition, constructing a scalable architecture requires this channel to be intrinsically extensible, in which case a one-dimensional waveguide is an ideal structure providing the extensible direction as well as strong light-matter interaction.
In this thesis, we explore superconducting circuit architectures based on light-matter interactions in waveguide quantum electrodynamics (QED) systems. These architectures in return allow us to study light-matter interaction, demonstrating strong coupling in the open environment of a waveguide by employing sub-radiant states resulting from collective effects. We further engineer the waveguide dispersion to enter the topological photonics regime, exploring interactions between qubits that are mediated by photons with topological properties. Finally, towards the goals of quantum information processing and simulation, we settle into a multi-qubit architecture where the photon-mediated interaction between qubits exhibits tunable range and strength. We use this multi-qubit architecture to construct a lattice with tunable connectivity for strongly interacting microwave photons, synthesizing a quantum many-body model to explore chaotic dynamics. The architectures in this thesis introduce scalable beyond-NN coupling between superconducting qubits, opening the door to the exploration of many-body physics with long-range coupling and efficient implementation of quantum information processing protocols.</p
Tensor-variate machine learning on graphs
Traditional machine learning algorithms are facing significant challenges as the world enters the era of big data, with a dramatic expansion in volume and range of applications and an increase in the variety of data sources. The large- and multi-dimensional nature of data often increases the computational costs associated with their processing and raises the risks of model over-fitting - a phenomenon known as the curse of dimensionality. To this end, tensors have become a subject of great interest in the data analytics community, owing to their remarkable ability to super-compress high-dimensional data into a low-rank format, while retaining the original data structure and interpretability. This leads to a significant reduction in computational costs, from an exponential complexity to a linear one in the data dimensions.
An additional challenge when processing modern big data is that they often reside on irregular domains and exhibit relational structures, which violates the regular grid assumptions of traditional machine learning models. To this end, there has been an increasing amount of research in generalizing traditional learning algorithms to graph data. This allows for the processing of graph signals while accounting for the underlying relational structure, such as user interactions in social networks, vehicle flows in traffic networks, transactions in supply chains, chemical bonds in proteins, and trading data in financial networks, to name a few.
Although promising results have been achieved in these fields, there is a void in literature when it comes to the conjoint treatment of tensors and graphs for data analytics. Solutions in this area are increasingly urgent, as modern big data is both large-dimensional and irregular in structure. To this end, the goal of this thesis is to explore machine learning methods that can fully exploit the advantages of both tensors and graphs. In particular, the following approaches are introduced: (i) Graph-regularized tensor regression framework for modelling high-dimensional data while accounting for the underlying graph structure; (ii) Tensor-algebraic approach for computing efficient convolution on graphs; (iii) Graph tensor network framework for designing neural learning systems which is both general enough to describe most existing neural network architectures and flexible enough to model large-dimensional data on any and many irregular domains. The considered frameworks were employed in several real-world applications, including air quality forecasting, protein classification, and financial modelling. Experimental results validate the advantages of the proposed methods, which achieved better or comparable performance against state-of-the-art models. Additionally, these methods benefit from increased interpretability and reduced computational costs, which are crucial for tackling the challenges posed by the era of big data.Open Acces
Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing
With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management
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