Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis
et al.
Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key priority, especially in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their use faces challenges of data availability, model transparency, and compliance with national and EU AI regulations including the AI act, ethics guidelines and the ALTAI. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework merges Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data with a neural network-based multi-label classifier that predicts potential combinations of retrofit actions. To support explanation and trustworthiness, an Explainable AI (XAI) layer using SHapley Additive exPlanations (SHAP) clarifies the rationale behind recommendations and guides feature engineering. Two case studies validate performance and generalization: the first leveraging a well-established, large EPC dataset for England and Wales; the second using a small, imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results show that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline. Overall, the proposed framework provides a feasible, interpretable, and trustworthy AI system for building retrofit decision support through assured performance, usability, and transparency to aid stakeholders in prioritizing effective energy investments and support regulation-compliant, data-driven innovation in sustainable energy transition.
Modern Integrated Circuits (ICs) are becoming increasingly complex, and so is their development process. Hardware design verification entails a methodical and disciplined approach to the planning, development, execution, and sign-off of functionally correct hardware designs. This tedious process requires significant effort and time to ensure a bug-free tape-out. The field of Natural Language Processing has undergone a significant transformation with the advent of Large Language Models (LLMs). These powerful models, often referred to as Generative AI (GenAI), have revolutionized how machines understand and generate human language, enabling unprecedented advancements in a wide array of applications, including hardware design verification. This paper presents an agentic AI-based approach to hardware design verification, which empowers AI agents, in collaboration with Humain-in-the-Loop (HITL) intervention, to engage in a more dynamic, iterative, and self-reflective process, ultimately performing end-to-end hardware design and verification. This methodology is evaluated on five open-source designs, achieving over 95% coverage with reduced verification time while demonstrating superior performance, adaptability, and configurability.
Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the "memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0 to 85 degrees Celcius at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes.
Despite rapid progress in large language models (LLMs), their performance on a vast majority of languages remains unsatisfactory. In this paper, we study building language-specific LLMs by adapting monolingual and multilingual LLMs. We conduct systematic experiments on how design choices (base model selection, vocabulary extension, and continued pretraining) impact the adapted LLM, both in terms of efficiency (how many tokens are needed to encode the same amount of information) and end task performance. We find that (1) the initial performance of LLM does not always correlate with the final performance after the adaptation. Adapting an English-centric models can yield better results than adapting multilingual models despite their worse initial performance on low-resource languages. (2) Efficiency can easily improved with simple vocabulary extension and continued pretraining in most LLMs we study, and (3) The optimal adaptation method (choice of the base model, new vocabulary size, training data, initialization strategy) is highly language-dependent, and the simplest embedding initialization works well across various experimental settings. Together, our work lays foundations on efficiently building language-specific LLMs by adapting existing LLMs.
Mohsin Yousufi, Charlotte Alexander, Nassim Parvin
This paper brings attention to epistemic injustice, an issue that has not received much attention in the design of technology and policy. Epistemic injustices occur when individuals are treated unfairly or harmed specifically in relation to their role as knowers or possessors of knowledge. Drawing on the case of making heat complaints in New York City, this paper illustrates how both technological and policy interventions that address epistemic injustice can fail or even exacerbate the situations for certain social groups, and individuals within them. In bringing this case to the workshop, this paper hopes to provide another generative and critical dimension that can be utilised to create better technologies and policies, especially when they deal with diverse and broad range of social groups
The theory of modular flow has proved extremely useful for applying thermodynamic reasoning to out-of-equilibrium states in quantum field theory. However, the standard proofs of the fundamental theorems of modular flow use machinery from Fourier analysis on Banach spaces, and as such are not especially transparent to an audience of physicists. In this article, I present a construction of modular flow that differs from existing treatments. The main pedagogical contribution is that I start with thermal physics via the KMS condition, and derive the modular operator as the only operator that could generate a thermal time-evolution map, rather than starting with the modular operator as the fundamental object of the theory. The main technical contribution is a new proof of the fundamental theorem stating that modular flow is a symmetry. The new proof circumvents the delicate issues of Fourier analysis that appear in previous treatments, but is still mathematically rigorous.
Explainable numerical representations or latent information of otherwise complex datasets are more convenient to analyze and study. These representations assist in identifying clusters and outliers, assess similar data points, and explore and interpolate data. Dataset of three-dimensional (3D) building models possesses inherent complexity in various footprint shapes, distinct roof types, walls, height, and volume. Traditionally, grouping similar buildings or 3D shapes requires matching their known properties and shape metrics with each other. However, this requires obtaining a plethora of such properties to calculate similarity. This study, in contrast, utilizes an autoencoder to compute the shape information in a fixed-size vector form that can be compared and grouped with the help of distance metrics. The study uses 'FoldingNet,' a 3D autoencoder, to generate the latent representation of each building from the obtained LoD 2 CityGML dataset. The efficacy of the embeddings obtained from the autoencoder is further analyzed by dataset reconstruction, latent spread visualization, and hierarchical clustering methods. While the clusters give an overall perspective of the type of build forms, they do not include geospatial information in the clustering. A geospatial model is therefore created to iteratively find the geographical groupings of buildings using cosine similarity approaches in embedding vectors. The German federal states of Brandenburg and Berlin are taken as an example to test the methodology. The output provides a detailed overview of the build forms in the form of semantic topological clusters and geographical groupings. This approach is beneficial and scalable for complex analytics, e.g., in large urban simulations, urban morphological studies, energy analysis, or evaluations of building stock.
Game dynamics structure (e.g., endogenous cycle motion) in human subjects game experiments can be predicted by game dynamics theory. However, whether the structure can be controlled by mechanism design to a desired goal is not known. Here, using the pole assignment approach in modern control theory, we demonstrate how to control the structure in two steps: (1) Illustrate an theoretical workflow on how to design a state-depended feedback controller for desired structure; (2) Evaluate the controller by laboratory human subject game experiments and by agent-based evolutionary dynamics simulation. To our knowledge, this is the first realisation of the control of the human social game dynamics structure in theory and experiment.
We introduce a novel convolutional network architecture with an interpretable latent space for modeling guitar amplifiers. Leveraging domain knowledge of popular amplifiers spanning a range of styles, the proposed system intuitively combines or subtracts characteristics of different amplifiers, allowing musicians to design entirely new guitar timbres.
Particle competition and cooperation (PCC) is a graph-based semi-supervised learning approach. When PCC is applied to interactive image segmentation tasks, pixels are converted into network nodes, and each node is connected to its k-nearest neighbors, according to the distance between a set of features extracted from the image. Building a proper network to feed PCC is crucial to achieve good segmentation results. However, some features may be more important than others to identify the segments, depending on the characteristics of the image to be segmented. In this paper, an index to evaluate candidate networks is proposed. Thus, building the network becomes a problem of optimizing some feature weights based on the proposed index. Computer simulations are performed on some real-world images from the Microsoft GrabCut database, and the segmentation results related in this paper show the effectiveness of the proposed method.
Building models are conventionally reconstructed by building roof points planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. The study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.
Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Security problems related to group-oriented communications in IoT-based applications placed in a privacy-sensitive environment have become a major concern along with the development of the technology. Unfortunately, many IoT devices are designed to be portable and light-weight; thus, their functionalities, including security modules, are heavily constrained by the limited energy resources (e.g., battery capacity). To address these problems, we propose a group key management scheme based on a novel physically unclonable function (PUF) design: multistage interconnected PUF (MIPUF) to secure group communications in an energy-constrained environment. Our design is capable of performing key management tasks such as key distribution, key storage and rekeying securely and efficiently. We show that our design is secure against multiple attack methods and our experimental results show that our design saves 47.33% of energy globally comparing to state-of-the-art Elliptic-curve cryptography (ECC)-based key management scheme on average.
Gregory Chang, Shovan Maity, Baibhab Chatterjee
et al.
Emerging health-monitor applications, such as information transmission through multi-channel neural implants, image and video communication from inside the body etc., calls for ultra-low active power (<50$μ$W) high data-rate, energy-scalable, highly energy-efficient (pJ/bit) radios. Previous literature has strongly focused on low average power duty-cycled radios or low power but low-date radios. In this paper, we investigate power performance trade-off of each front-end component in a conventional radio including active matching, down-conversion and RF/IF amplification and prioritize them based on highest performance/energy metric. The analysis reveals 50$Ω$ active matching and RF gain is prohibitive for 50$μ$W power-budget. A mixer-first architecture with an N-path mixer and a self-biased inverter based baseband LNA, designed in TSMC 65nm technology show that sub 50$μ$W performance can be achieved up to 10Mbps (< 5pJ/b) with OOK modulation.
Air conditioning systems are responsible for the major percentage of energy consumption in buildings. Shared spaces constitute considerable office space area, in which most office employees perform their meetings and daily tasks, and therefore the ACs in these areas have significant impact on the energy usage of the entire office building. The cost of this energy consumption, however, is not paid by the shared space users, and the AC's temperature set-point is not determined based on the users' preferences. This latter factor is compounded by the fact that different people may have different choices of temperature set-points and sensitivities to change of temperature. Therefore, it is a challenging task to design an office policy to decide on a particular set-point based on such a diverse preference set. As a result, users are not aware of the energy consumption in shared spaces, which may potentially increase the energy wastage and related cost of office buildings. In this context, this paper proposes an energy policy for an office shared space by exploiting an established temperature control mechanism. In particular, we choose meeting rooms in an office building as the test case and design a policy according to which each user of the room can give a preference on the temperature set-point and is paid for felt discomfort if the set-point is not fixed according to the given preference. On the other hand, users who enjoy the thermal comfort compensate the other users of the room. Thus, the policy enables the users to be cognizant and responsible for the payment on the energy consumption of the office space they are sharing, and at the same time ensures that the users are satisfied either via thermal comfort or through incentives. The policy is also shown to be beneficial for building management. Through experiment based case studies, we show the effectiveness of the proposed policy.
Craigen introduced and studied {\it signed group Hadamard matrices} extensively in \cite{Craigenthesis, Craigen}. Livinskyi \cite{Ivan}, following Craigen's lead, studied and provided a better estimate for the asymptotic existence of signed group Hadamard matrices and consequently improved the asymptotic existence of Hadamard matrices. In this paper, we introduce and study signed group orthogonal designs. The main results include a method for finding signed group orthogonal designs for any $k$-tuple of positive integer and then an application to obtain orthogonal designs from signed group orthogonal designs, namely, for any $k$-tuple $\big(u_1, u_2, ..., u_{k}\big)$ of positive integers, we show that there is an integer $N=N(u_1, u_2, ..., u_k)$ such that for each $n\ge N$, a full orthogonal design (no zero entries) of type $\big(2^nu_1,2^nu_2,...,2^nu_{k}\big)$ exists . This is an alternative approach to the results obtained in \cite{EK}.