Clinical AI systems routinely train on health data structurally distorted by documentation workflows, billing incentives, and terminology fragmentation. Prior work has characterised the mechanisms of this distortion: the three-forces model of documentary enactment, the reification feedback loop through which AI may amplify coding artefacts, and terminology governance failures that allow semantic drift to accumulate. Yet translating these insights into implementable software architecture remains an open problem. This paper proposes seven ontology-aware design patterns in Gang-of-Four pattern language for building clinical AI pipelines resilient to ontological distortion. The patterns address data ingestion validation (Ontological Checkpoint), low-frequency signal preservation (Dormancy-Aware Pipeline), continuous drift monitoring (Drift Sentinel), parallel representation maintenance (Dual-Ontology Layer), feedback loop interruption (Reification Circuit Breaker), terminology evolution management (Terminology Version Gate), and pluggable regulatory compliance (Regulatory Compliance Adapter). Each pattern is specified with Problem, Forces, Solution, Consequences, Known Uses, and Related Patterns. We illustrate their composition in a reference architecture for a primary care AI system and provide a walkthrough tracing all seven patterns through a diabetes risk prediction scenario. This paper does not report empirical validation; it offers a design vocabulary grounded in theoretical analysis, subject to future evaluation in production systems. Three patterns have partial precedent in existing systems; the remaining four have not been formally described. Limitations include the absence of runtime benchmarks and restriction to the German and EU regulatory context.
Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their design and development. To tackle this gap, we propose VA-Blueprint, a methodology and knowledge base that systematically reviews and categorizes the fundamental building blocks of urban VA systems, a domain particularly rich and representative due to its intricate data and unique problem sets. Applying this methodology to an initial set of 20 systems, we identify and organize their core components into a multi-level structure, forming an initial knowledge base with a structured blueprint for VA system development. To scale this effort, we leverage a large language model to automate the extraction of these components for other 81 papers (completing a corpus of 101 papers), assessing its effectiveness in scaling knowledge base construction. We evaluate our method through interviews with experts and a quantitative analysis of annotation metrics. Our contributions provide a deeper understanding of VA systems' composition and establish a practical foundation to support more structured, reproducible, and efficient system development. VA-Blueprint is available at https://urbantk.org/va-blueprint.
A $k$-regular graph on $v$ vertices is a {\em divisible design graph} if there exist integers $λ_1,λ_2,m,n$ such that the vertex set can be partitioned into $m$ classes of size $n$ and any two different vertices from the same class have $λ_1$ common neighbours, and any two vertices from different classes have $λ_2$ common neighbours. In this paper, a new construction that produces divisible design graphs is provided.
Detailed routing remains one of the most complex and time-consuming steps in modern physical design due to the challenges posed by shrinking feature sizes and stricter design rules. Prior detailed routers achieve state-of-the-art results by leveraging iterative pathfinding algorithms to route each net. However, runtimes are a major issue in detailed routers, as converging to a solution with zero design rule violations (DRVs) can be prohibitively expensive. In this paper, we propose leveraging reinforcement learning (RL) to enable rapid convergence in detailed routing by learning from previous designs. We make the key observation that prior detailed routers statically schedule the cost weights used in their routing algorithms, meaning they do not change in response to the design or technology. By training a conservative Q-learning (CQL) model to dynamically select the routing cost weights which minimize the number of algorithm iterations, we find that our work completes the ISPD19 benchmarks with 1.56x average and up to 3.01x faster runtime than the baseline router while maintaining or improving the DRV count in all cases. We also find that this learning shows signs of generalization across technologies, meaning that learning designs in one technology can translate to improved outcomes in other technologies.
Leo Guo, Hirak Kansara, Siamak F. Khosroshahi
et al.
Finite element (FE) simulations of structures and materials are getting increasingly more accurate, but also more computationally expensive as a collateral result. This development happens in parallel with a growing demand of data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. In parallel, the mesh width of an FE model can be exploited to evaluate an objective at a lower or higher fidelity (cost & accuracy) level. The multi-fidelity setting applied to BO, called multi-fidelity BO (MFBO), has also seen previous success. However, BO and MFBO have not seen a direct comparison with when faced with with a real-life engineering problem, such as metamaterial design for deformation and absorption qualities. Moreover, sampling quality and assessing design parameter sensitivity is often an underrepresented part of data-driven design. This paper aims to address these shortcomings by employing Sobol' samples with variance-based sensitivity analysis in order to reduce design problem complexity. Furthermore, this work describes, implements, applies and compares the performance BO with that MFBO when maximizing the energy absorption (EA) problem of spinodoid cellular structures is concerned. The findings show that MFBO is an effective way to maximize the EA of a spinodoid structure and is able to outperform BO by up to 11% across various hyperparameter settings. The results, which are made open-source, serve to support the utility of multi-fidelity techniques across expensive data-driven design problems.
Standard cells are essential components of modern digital circuit designs. With process technologies advancing toward 2nm, more routability issues have arisen due to the decreasing number of routing tracks, increasing number and complexity of design rules, and strict patterning rules. The state-of-the-art standard cell design automation framework is able to automatically design standard cell layouts in advanced nodes, but it is still struggling to generate highly competitive Performance-Power-Area (PPA) and routable cell layouts for complex sequential cell designs. Consequently, a novel and efficient methodology incorporating the expertise of experienced human designers to incrementally optimize the PPA of cell layouts is highly necessary and essential. High-quality device clustering, with consideration of netlist topology, diffusion sharing/break and routability in the layouts, can reduce complexity and assist in finding highly competitive PPA, and routable layouts faster. In this paper, we leverage the natural language and reasoning ability of Large Language Model (LLM) to generate high-quality cluster constraints incrementally to optimize the cell layout PPA and debug the routability with ReAct prompting. On a benchmark of sequential standard cells in 2nm, we demonstrate that the proposed method not only achieves up to 19.4% smaller cell area, but also generates 23.5% more LVS/DRC clean cell layouts than previous work. In summary, the proposed method not only successfully reduces cell area by 4.65% on average, but also is able to fix routability in the cell layout designs.
Utkarsh Anand, Diya Parekh, Thakur Pranav G. Singh
et al.
The use of parallel manipulators in aerospace engineering has gained significant attention due to their ability to provide improved stability and precision. This paper presents the design, control, and analysis of 'STEWIE', which is a three-degree-of-freedom (DoF) parallel manipulator robot developed by members of the thrustMIT rocketry team, as a payload stabilization mechanism for their sounding rocket, 'Altair'. The goal of the robot was to demonstrate the attitude control of the parallel plate against the continuous change in orientation experienced by the rocket during its flight, stabilizing the payloads. At the same time, the high gravitational forces (G-forces) and vibrations experienced by the sounding rocket are counteracted. A novel design of the mechanism, inspired by a standard Stewart platform, is proposed which was down-scaled to fit inside a 4U CubeSat within its space constraints. The robot uses three micro servo motors to actuate the links that control the alignment of the parallel plate. In addition to the actuation mechanism, a robust control system for its manipulation was developed for the robot. The robot represents a significant advancement in the field of space robotics in the aerospace industry by demonstrating the successful implementation of complex robotic mechanisms in small, confined spaces such as CubeSats, which are standard form factors for large payloads in the aerospace industry.
With today's technologies it seems easier than ever to augment everyday things with the ability to perceive their environment and to talk to users. Considering conversational user interfaces, tremendous progress has already been made in designing and evaluating task oriented conversational interfaces, such as voice assistants for ordering food, booking a flight etc. However, it is still very challenging to design smart things that can have with their users an informal conversation and emotional exchange, which requires the smart thing to master the usage of social everyday utterances, using irony and sarcasm, delivering good compliments, etc. In this paper, we focus on the experience design of compliments and the Complimenting Mirror design. The paper reports in detail on three phases of a human-centered design process including a Wizard of Oz study in the lab with 24 participants to explore and identify the effect of different compliment types on user experiences and a consequent field study with 105 users in an architecture museum with a fully functional installation of the Complimenting Mirror. In our analyses we argue why and how a "smart" mirror should compliment users and provide a theorization applicable for affective interaction design with things in more general. We focus on subjective user feedback including user concerns and prepositions of receiving compliments from a thing and on observations of real user behavior in the field i.e. transitions of bodily affective expressions comparing affective user states before, during, and after compliment delivery. Our research shows that compliment design matters significantly and using the right type of compliments in our final design in the field test, we succeed in achieving reactive expressions of positive emotions, "sincere" smiles and laughter, even from the seemingly sternest users.
Failure probability estimation problem is an crucial task in engineering. In this work we consider this problem in the situation that the underlying computer models are extremely expensive, which often arises in the practice, and in this setting, reducing the calls of computer model is of essential importance. We formulate the problem of estimating the failure probability with expensive computer models as an sequential experimental design for the limit state (i.e., the failure boundary) and propose a series of efficient adaptive design criteria to solve the design of experiment (DOE). In particular, the proposed method employs the deep neural network (DNN) as the surrogate of limit state function for efficiently reducing the calls of expensive computer experiment. A map from the Gaussian distribution to the posterior approximation of the limit state is learned by the normalizing flows for the ease of experimental design. Three normalizing-flows-based design criteria are proposed in this work for deciding the design locations based on the different assumption of generalization error. The accuracy and performance of the proposed method is demonstrated by both theory and practical examples.
Social media captures examples of people's behaviors, actions, beliefs, and sentiments. As a result, it can be a valuable source of information and inspiration for HCI research and design. Social media technologies can improve, inform, and strengthen insights to better understand and represent user populations. To understand the position of social media research and analysis in the design process, this paper seeks to highlight shortcomings of using traditional research methods (e.g., interviews, focus groups) that ignore or don't adequately reflect relevant social media, and this paper speculates about the importance and benefits of leveraging social media for establishing context in supplement with these methods. We present examples that guide our thinking and introduce discussion around concerns related to using social media data.
Ternary content addressable memory (TCAM) has been a critical component in caches, routers, etc., in which density, speed, power efficiency, and reliability are the major design targets. There have been the conventional low-write-power but bulky SRAM-based TCAM design, and also denser but less reliable or higher-write-power TCAM designs using nonvolatile memory (NVM) devices. Meanwhile, some TCAM designs using dynamic memories have been also proposed. Although dynamic design TCAM is denser than CMOS SRAM TCAM and more reliable than NVM TCAM, the conventional row-by-row refresh operations land up with a bottleneck of interference with normal TCAM activities. Therefore, this paper proposes a custom low-power dynamic TCAM using nanoelectromechanical (NEM) relay devices utilizing one-shot refresh to solve the memory refresh problem. By harnessing the unique NEM relay characteristics with a proposed novel cell structure, the proposed TCAM occupies a small footprint of only 3 transistors (with two NEM relays integrated on the top through the back-end-of-line process), which significantly outperforms the density of 16-transistor SRAM-based TCAM. In addition, evaluations show that the proposed TCAM improves the write energy efficiency by 2.31x, 131x, and 13.5x over SRAM, RRAM, and FeFET TCAMs, respectively; The search energy-delay-product is improved by 12.7x, 1.30x, and 2.83x over SRAM, RRAM, and FeFET TCAMs, respectively.
Sheikh Ariful Islam, Love Kumar Sah, Srinivas Katkoori
Offshoring the proprietary Intellectual property (IP) has recently increased the threat of malicious logic insertion in the form of Hardware Trojan (HT). A potential and stealthy HT is triggered with nets that switch rarely during regular circuit operation. Detection of HT in the host design requires exhaustive simulation to activate the HT during pre- and postsilicon. Although the nets with variable switching probability less than a threshold are primarily chosen as a good candidate for Trojan triggering, there is no systematic fine-grained approach for earlier detection of rare nets from word-level measures of input signals. In this paper, we propose a high-level technique to estimate the nets with the rare activity of arithmetic modules from word-level information. Specifically, for a given module, we use the knowledge of internal construction of the architecture to detect "low activity" and "local regions" without resorting to expensive RTL and other low-level simulations. The presented heuristic method abstracts away from the low-level details of design and describes the rare activity of bits (modules) in a word (architecture) as a function of signal statistics. The resulting quick estimates of nets in rare regions allows a designer to develop a compact test generation algorithm without the knowledge of the bit-level activity. We determine the effect of different positions of the breakpoint in the input signal to calculate the accuracy of the approach. We conduct a set of experiments on six adder architectures and four multiplier architectures. The average error to calculate the rare nets between RTL simulation and estimated values are below 2% in all architectures.
Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.
Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems. It becomes more challenging with imperfect channel state information (CSI). In this letter, we propose a deep learning based BF design approach and develop a BF neural network (BFNN) which can be trained to learn how to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed BFNN achieves significant performance improvement and strong robustness to imperfect CSI over the conventional BF algorithms.
The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure.