Hasil untuk "Architectural drawing and design"

In architecture, tacit knowledge plays a substantial role in both the design process and its reception. The essays in this book explore the tacit dimension of architecture in its aesthetic, material, cultural, design-based, and reflexive understanding of what we build. Tacit knowledge, described in 1966 by Michael Polanyi as what we ‘can know but cannot tell’, often denotes knowledge that escapes quantifiable dimensions of research. Much of architecture’s knowledge resides beneath the surface, in nonverbal instruments such as drawings and models that articulate the spatial imagination of the design process. Awareness of the tacit dimension helps to understand the many facets of the spaces we inhabit, from the ideas of the architect to the more hidden assumptions of our cultures. Beginning in the studio, where students are guided into becoming architects, the book follows a path through the tacit knowledge present in materials, conceptual structures, and the design process, revealing how the tacit dimension leads to craftsmanship and the situated knowledge of architecture-in-the-world.

Pramod Sadalage, M. Fowler

Y. Nirkin, Lior Wolf, Tal Hassner

We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features, a multi-headed weight generating module which generates the weights of each block in the decoder immediately before they are consumed, for efficient memory utilization, and a primary network that is composed of novel dynamic patch-wise convolutions. Despite the usage of less-conventional blocks, our architecture obtains real-time performance. In terms of the runtime vs. accuracy trade-off, we surpass state of the art (SotA) results on popular semantic segmentation benchmarks: PASCAL VOC 2012 (val. set) and real-time semantic segmentation on Cityscapes, and CamVid. The code is available: https://nirkin.com/hyperseg.

Ines Schindler, G. Hosoya, Winfried Menninghaus et al.

Aesthetic perception and judgement are not merely cognitive processes, but also involve feelings. Therefore, the empirical study of these experiences requires conceptualization and measurement of aesthetic emotions. Despite the long-standing interest in such emotions, we still lack an assessment tool to capture the broad range of emotions that occur in response to the perceived aesthetic appeal of stimuli. Elicitors of aesthetic emotions are not limited to the arts in the strict sense, but extend to design, built environments, and nature. In this article, we describe the development of a questionnaire that is applicable across many of these domains: the Aesthetic Emotions Scale (Aesthemos). Drawing on theoretical accounts of aesthetic emotions and an extensive review of extant measures of aesthetic emotions within specific domains such as music, literature, film, painting, advertisements, design, and architecture, we propose a framework for studying aesthetic emotions. The Aesthemos, which is based on this framework, contains 21 subscales with two items each, that are designed to assess the emotional signature of responses to stimuli’s perceived aesthetic appeal in a highly differentiated manner. These scales cover prototypical aesthetic emotions (e.g., the feeling of beauty, being moved, fascination, and awe), epistemic emotions (e.g., interest and insight), and emotions indicative of amusement (humor and joy). In addition, the Aesthemos subscales capture both the activating (energy and vitality) and the calming (relaxation) effects of aesthetic experiences, as well as negative emotions that may contribute to aesthetic displeasure (e.g., the feeling of ugliness, boredom, and confusion).

Qinqin Tang, Renchao Xie, Li Feng et al.

Currently, data processing employs a three-tier computing power architecture encompassing cloud, edge, and end. However, under such an architecture, large-scale, ubiquitous and heterogeneous computing resources distributed in the cloud, edge, and end are isolated from each other, resulting in low resource utilization and poor service performance. Computing Power Networks (CPNs) use the network to connect distributed computing resources to realize the deep integration of computing and networking and provide users with unified computing services through integrated resource orchestration and network control, thus offering a promising solution. Task scheduling is a pivotal technique in CPNs, as it is closely related to the quality of user experience. However, existing studies on task scheduling focus on the selection of computing nodes while ignoring the scheduling of the network, and most of them are unaware of the underlying service intent of applications during the scheduling process. Toward this end, drawing on the idea of Intent-based Networking (IBN), this article proposes a Service Intent-aware Task Scheduling (SIaTS) framework for CPNs. The computing power identification method and the service intent-aware mechanism are designed. An auction-based task scheduling algorithm is developed to achieve the optimal matching of task intent and CPN resources. Numerical results evaluate the performance of the proposed SIaTS.

Giovanni Multari

The restoration of the Pirelli Skyscraper—necessitated after a notorious plane crash in 2002—became a key example of architectural intervention in modern architecture with heritage status. Even two decades after its completion, the process stands out for extraordinary research and analysis efforts—involving studying existing documentation, observing and measuring spaces, materials, and construction techniques. This ongoing investigation reaffirms that essentiality, as thought by Gio Ponti, remains a fundamental principle of architectural design until today. The intervention strategies of Corvino+Multari, accompanied by a technical and scientific team, have breathed life into Ponti’s masterpiece while stimulating a broader reflection on approaches to complex architectural monuments. Engaging critically with its architecture, the project positions itself between restoration and intervention, both philological in its approach and contemporary in spirit. The meticulous restoration of the curtain wall, fixtures, and mosaic tesserae exemplifies this.

Claudia de Biase, Salvatore Losco

The informal city is configured and articulated as a spontaneous, sprawling or illegal city. Each of them present recurring and distinctive characteristics also in relation to specific territorial contexts. After outlining the scientific background of informal and illegal cities, and summarising the Italian specificities of the last ones, the paper focuses on the dualism between the informal and the illegal city found in the analysis of the technical literature on the subject regarding spatial planning. The aim is to bring out affinities and differences between the two city models to contribute to the formulation of the correct contents of urban planning tools for their redevelopment and/or regeneration to transform especially Italian illegal cities into liveable neighbourhoods.

Steven Walton

Major advancements in the capabilities of computer vision models have been primarily fueled by rapid expansion of datasets, model parameters, and computational budgets, leading to ever-increasing demands on computational infrastructure. However, as these models are deployed in increasingly diverse and resource-constrained environments, there is a pressing need for architectures that can deliver high performance while requiring fewer computational resources. This dissertation focuses on architectural principles through which models can achieve increased performance while reducing their computational demands. We discuss strides towards this goal through three directions. First, we focus on data ingress and egress, investigating how information may be passed into and retrieved from our core neural processing units. This ensures that our models make the most of available data, allowing smaller architectures to become more performant. Second, we investigate modifications to the core neural architecture, applied to restricted attention in vision transformers. This section explores how removing uniform context windows in restricted attention increases the expressivity of the underlying neural architecture. Third, we explore the natural structures of Normalizing Flows and how we can leverage these properties to better distill model knowledge. These contributions demonstrate that careful design of neural architectures can increase the efficiency of machine learning algorithms, allowing them to become smaller, faster, and cheaper.

Cenlin Duan, Jianlei Yang, Yikun Wang et al.

Processing-in-memory (PIM) is a transformative architectural paradigm designed to overcome the Von Neumann bottleneck. Among PIM architectures, digital SRAM-PIM emerges as a promising solution, offering significant advantages by directly integrating digital logic within the SRAM array. However, rigid crossbar architecture and full array activation pose challenges in efficiently utilizing traditional value-level sparsity. Moreover, neural network models exhibit a high proportion of zero bits within non-zero values, which remain underutilized due to architectural constraints. To overcome these limitations, we present Dyadic Block PIM (DB-PIM), a groundbreaking algorithm-architecture co-design framework to harness both value-level and bit-level sparsity. At the algorithm level, our hybrid-grained pruning technique, combined with a novel sparsity pattern, enables effective sparsity management. Architecturally, DB-PIM incorporates a sparse network and customized digital SRAM-PIM macros, including input pre-processing unit (IPU), dyadic block multiply units (DBMUs), and Canonical Signed Digit (CSD)-based adder trees. It circumvents structured zero values in weights and bypasses unstructured zero bits within non-zero weights and block-wise all-zero bit columns in input features. As a result, the DB-PIM framework skips a majority of unnecessary computations, thereby driving significant gains in computational efficiency. Results demonstrate that our DB-PIM framework achieves up to 8.01x speedup and 85.28% energy savings, significantly boosting computational efficiency in digital SRAM-PIM systems.

Xusheng Du, Ruihan Gui, Zhengyang Wang et al.

In the early stages of architectural design, shoebox models are typically used as a simplified representation of building structures but require extensive operations to transform them into detailed designs. Generative artificial intelligence (AI) provides a promising solution to automate this transformation, but ensuring multi-view consistency remains a significant challenge. To solve this issue, we propose a novel three-stage consistent image generation framework using generative AI models to generate architectural designs from shoebox model representations. The proposed method enhances state-of-the-art image generation diffusion models to generate multi-view consistent architectural images. We employ ControlNet as the backbone and optimize it to accommodate multi-view inputs of architectural shoebox models captured from predefined perspectives. To ensure stylistic and structural consistency across multi-view images, we propose an image space loss module that incorporates style loss, structural loss and angle alignment loss. We then use depth estimation method to extract depth maps from the generated multi-view images. Finally, we use the paired data of the architectural images and depth maps as inputs to improve the multi-view consistency via the depth-aware 3D attention module. Experimental results demonstrate that the proposed framework can generate multi-view architectural images with consistent style and structural coherence from shoebox model inputs.

Bo Fu, Leo Tenenbaum, David Adler et al.

In recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the design phase to facilitate rapid prototyping. This paper develops AMuLeT, the first tool capable of testing secure speculation countermeasures for speculative leakage early in their design phase in simulators. Our key idea is to leverage model-based relational testing tools that can detect speculative leaks in commercial CPUs, and apply them to micro-architectural simulators to test secure speculation defenses. We identify and overcome several challenges, including designing an expressive yet realistic attacker observer model in a simulator, overcoming the slow simulation speed, and searching the vast micro-architectural state space for potential vulnerabilities. AMuLeT speeds up test throughput by more than 10x compared to a naive design and uses techniques to amplify vulnerabilities to uncover them within a limited test budget. Using AMuLeT, we launch for the first time, a systematic, large-scale testing campaign of four secure speculation countermeasures from 2018 to 2024--InvisiSpec, CleanupSpec, STT, and SpecLFB--and uncover 3 known and 6 unknown bugs and vulnerabilities, within 3 hours of testing. We also show for the first time that the open-source implementation of SpecLFB is insecure.

Rundong Luo, Noah Snavely, Wei-Chiu Ma

We introduce ShadowDraw, a framework that transforms ordinary 3D objects into shadow-drawing compositional art. Given a 3D object, our system predicts scene parameters, including object pose and lighting, together with a partial line drawing, such that the cast shadow completes the drawing into a recognizable image. To this end, we optimize scene configurations to reveal meaningful shadows, employ shadow strokes to guide line drawing generation, and adopt automatic evaluation to enforce shadow-drawing coherence and visual quality. Experiments show that ShadowDraw produces compelling results across diverse inputs, from real-world scans and curated datasets to generative assets, and naturally extends to multi-object scenes, animations, and physical deployments. Our work provides a practical pipeline for creating shadow-drawing art and broadens the design space of computational visual art, bridging the gap between algorithmic design and artistic storytelling. Check out our project page https://red-fairy.github.io/ShadowDraw/ for more results and an end-to-end real-world demonstration of our pipeline!

Pablo N. Pizarro, N. Hitschfeld, I. Sipiran et al.

Over the last few decades, floor plan analysis and recognition has been an open research topic in computer science, aiming to generate the building’s model by automatically extracting meaningful information from diverse sources. Among these, the architectural drawings are one of the most common, typically composed of non-uniform notations, together with their relationship and constraints, defining the structure’s layout and usage. Usually, floor plans encompass a high variability in style and semantics, as there is no standard notation to describe each element. Thus, numerous methodologies have been proposed to recognize, vectorize, and model different objects such as walls, doors, and rooms. In this work, we review different procedures from rule-based and learning-based approaches between the years 1995 to 2021, restricting only those considering the plan data as a rasterized image format. Datasets, scopes, and performed tasks were summarized to guide future development within the construction and design industries.

Yingqiang Ge, Yujie Ren, Wenyue Hua et al.

This paper envisions a revolutionary AIOS-Agent ecosystem, where Large Language Model (LLM) serves as the (Artificial) Intelligent Operating System (IOS, or AIOS)--an operating system"with soul". Upon this foundation, a diverse range of LLM-based AI Agent Applications (Agents, or AAPs) are developed, enriching the AIOS-Agent ecosystem and signaling a paradigm shift from the traditional OS-APP ecosystem. We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language, featured by several main concepts: LLM as OS (system-level), Agents as Applications (application-level), Natural Language as Programming Interface (user-level), and Tools as Devices/Libraries (hardware/middleware-level). We begin by introducing the architecture of traditional OS. Then we formalize a conceptual framework for AIOS through"LLM as OS (LLMOS)", drawing analogies between AIOS and traditional OS: LLM is likened to OS kernel, context window to memory, external storage to file system, hardware tools to peripheral devices, software tools to programming libraries, and user prompts to user commands. Subsequently, we introduce the new AIOS-Agent Ecosystem, where users can easily program Agent Applications (AAPs) using natural language, democratizing the development of software, which is different from the traditional OS-APP ecosystem. Following this, we explore the diverse scope of Agent Applications. We delve into both single-agent and multi-agent systems, as well as human-agent interaction. Lastly, drawing on the insights from traditional OS-APP ecosystem, we propose a roadmap for the evolution of the AIOS-Agent ecosystem. This roadmap is designed to guide the future research and development, suggesting systematic progresses of AIOS and its Agent applications.

Francesca De Filippi, Cristina Coscia

The Italia Digitale 2026 Plan aims to make the country’s public administration more digitally accessible. The government has allocated 27% of the resources of its National Recovery and Resilience Plan (NRRP) for Italy’s digital transition. Considerable space is dedicated to digitisation of the public administration and the production sector: Mission 1 deals with digitisation, innovation, competitiveness and culture. The overall objective is ‘the country’s innovation in a digital key, thanks to which a real structural change can be triggered’. The contribution analyses some elements of this structural change, and highlights them through the experimentation of the MiraMap collaborative platform for the care of public spaces, within the AXTO project.

Alessandra Cornice, Alessandra Innamorati

Rome stands out for a fragility that stems from the disordered interpenetration of its rural soul with its urban agglomeration. The relationship between city and suburbs still appears complicated today and reverberates on the degree of social cohesion of its inhabitants. Bottom-up experimentation projects are being grafted into the extended area of the capital, reviving disused public property, abandoned land, to eliminate degradation especially in fragile contexts, developing regenerative processes of active citizenship. The purpose of the paper is to underline innovative experiences, in the urban and peri-urban area of Rome, pointing out their actions aimed at sharing common values, developing territorial welfare and protecting the environment.

Adam Fitriawijaya, Taysheng Jeng

Multimodal generative AI and generative design empower architects to create better-performing, sustainable, and efficient design solutions and explore diverse design possibilities. Blockchain technology ensures secure data management and traceability. This study aims to design and evaluate a framework that integrates blockchain into generative AI-driven design drawing processes in architectural design to enhance authenticity and traceability. We employed a scenario as an example to integrate generative AI and blockchain into architectural designs by using a generative AI tool and leveraging multimodal generative AI to enhance design creativity by combining textual and visual inputs. These images were stored on blockchain systems, where metadata were attached to each image before being converted into NFT format, which ensured secure data ownership and management. This research exemplifies the pragmatic fusion of generative AI and blockchain technology applied in architectural design for more transparent, secure, and effective results in the early stages of the architectural design process.

Sahil Goyal, Abhinav Mahajan, Swasti Mishra et al.

Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to generate such designs, which accelerates the rate of content production. An automated way of evaluating their quality becomes critical. Towards this end, we introduce Design-o-meter, a data-driven methodology to quantify the goodness of graphic designs. Further, our approach can suggest modifications to these designs to improve its visual appeal. To the best of our knowledge, Design-o-meter is the first approach that scores and refines designs in a unified framework despite the inherent subjectivity and ambiguity of the setting. Our exhaustive quantitative and qualitative analysis of our approach against baselines adapted for the task (including recent Multimodal LLM-based approaches) brings out the efficacy of our methodology. We hope our work will usher more interest in this important and pragmatic problem setting.

Yihui Li, Xiaoyue Yan, Hao Zhou et al.

In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design.

Christian Skafte Beck Clausen, Bo Nørregaard Jørgensen, Zheng Grace Ma

Facing economic challenges due to the diverse objectives of businesses, and consumers, commercial greenhouses strive to minimize energy costs while addressing CO2 emissions. This scenario is intensified by rising energy costs and the global imperative to curtail CO2 emissions. To address these dynamic economic challenges, this paper proposes an architectural design for an energy economic dispatch testbed for commercial greenhouses. Utilizing the Attribute-Driven De-sign method, core architectural components of a software-in-the-loop testbed are proposed which emphasizes modularity and careful consideration of the multi-objective optimization problem. This approach extends prior research by implementing a modular multi-objective optimization framework in Java. The results demonstrate the successful integration of the CO2 reduction objective within the modular architecture with minimal effort. The multi-objective optimization output can also be employed to examine cost and CO2 objectives, ultimately serving as a valuable decision-support tool. The novel testbed architecture and a modular approach can tackle the multi-objective optimization problem and enable commercial greenhouses to navigate the intricate landscape of energy cost and CO2 emissions management.