Today’s generative models can synthesize magnetic resonance images (MRIs) of the brain at specific ages. However, such models can neither map the aging process longitudinally within subjects, nor accommodate its variability across subjects. Such approaches also cannot predict anatomic features of aging in ways that can be validated retrospectively or trusted prospectively. We introduce a three-dimensional hybrid ControlNet + diffusion model that uses the baseline T1-weighted MRIs of healthy adults to predict individual neuroanatomic aging trajectories, as reflected by follow-up MRIs. The approach captures individual anatomical changes with an average predicted voxelwise intensity error of 15% and structural similarity index of 93%. Unlike methods relying on qualitative validation, our approach quantifies the fidelity of prospective MRI synthesis using FreeSurfer volumetrics. Because brain atrophy reflects risk for Alzheimer’s disease (AD), our model’s ability to generate individual-specific prospective MRIs suggests its clinical potential to assist AD risk estimation.

With the advent of hundreds of cores on a chip to accelerate applications, the operating system (OS) needs to exploit the existing parallelism provided by the underlying hardware resources to determine the right amount of processes to be mapped on the multi-core systems. However, the existing OS is not scalable and is oblivious to applications. We address these issues by adopting a multi-layer network representation of the dynamic application-to-OS-to-architecture dependencies, namely the NetworkedOS. We adopt a compile-time analysis and construct a network representing the dependencies between dynamic instructions translated from the applications and the kernel and services. We propose an overlapping partitioning scheme to detect the clusters or processes that can potentially run in parallel to be mapped onto cores while reducing the number of messages transferred. At run time, processes are mapped onto the multi-core systems, taking into consideration the process affinity. Our experimental results indicate that NetworkedOS achieves performance improvement as high as 7.11x compared to Linux running on a 128-core system and 2.01x to Barrelfish running on a 64-core system.

In recent years, there has been increasing attention on the capabilities of large-scale models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstract language comprehension, reflecting cognitive-like behaviors. However, current research on emergent abilities in large models predominantly focuses on the relationship between model performance and size, leaving a significant gap in the systematic quantitative analysis of the internal structures and mechanisms driving these emergent abilities. Drawing inspiration from neuroscience research on brain network structure and self-organization, we propose (i) a general network representation of large models, (ii) a new analytical framework — *Neuron-based Multifractal Analysis (NeuroMFA)* - for structural analysis, and (iii) a novel structure-based metric as a proxy for emergent abilities of large models. By linking structural features to the capabilities of large models, *NeuroMFA* provides a quantitative framework for analyzing emergent phenomena in large models. Our experiments show that the proposed method yields a comprehensive measure of the network's evolving heterogeneity and organization, offering theoretical foundations and a new perspective for investigating emergence in large models.

With AI advancement and increasing circuit complexity, efficient chip design through Electronic Design Automation (EDA) is critical. Fast and accurate congestion prediction in chip layout and routing can significantly enhance automated design performance. Existing congestion modeling methods are limited by **(i)** ineffective processing and fusion of multi-view circuit data information, and **(ii)** insufficient reliability and interpretability in the prediction process. To address these challenges, We propose **M**ulti-view **I**nterpretable **H**ypergraph for **C**hip (**MIHC**), a trustworthy 'multi-view hypergraph neural network'-based framework that **(i)** processes both graph and image information in unified hypergraph representations, capturing topological and geometric circuit data, and **(ii)** implements a novel subgraph Information Bottleneck mechanism identifying critical congestion-correlated regions to guide predictions. This represents the first attempt to incorporate such interpretability into congestion prediction through informative graph reasoning. Experiments show our model reduces NMAE by 16.67% and 8.57% in cell-based and grid-based predictions on ISPD2015, and 5.26% and 2.44% on CircuitNet-N28, respectively, compared to state-of-the-art methods. Rigorous cross-design generalization experiments further validate our method’s capability to handle entirely unseen circuit designs.

Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58.2\%$ over CPU execution and by up to $60.24\%$ compared to other commonly used baselines.

This paper proposes a novel approach for identifying software bugs by building on a meaningful combination of word embeddings, graph-based text representations and graph attention networks. Existing approaches aim to advance each of the above components individually, without considering an integrative approach. As a result, they ignore information that is related to either the structure of a given text or an individual word of the text. Instead, our approach seamlessly incorporates both semantic and structural characteristics into a graph, which are then fed to a graph attention network in order to classify GitHub issues as bugs or features. Our experimental results demonstrate a significant improvement in terms of accuracy, precision and recall of the proposed approach compared to a list of classical and graph-based machine learning models. The dataset for the experiments reported in this paper has been retrieved from the kaggle.com platform and concerns GitHub issues with short-text attributes.

The highly competitive environment of post-genomics biomedical research is pushing towards the production of highly qualified results and their utilization in clinical practice. Due to this pressure, an important factor of scientific progress has been underestimated namely, the reproducibility of the results. To this end, it is critical to design and implement computational platforms that enable seamless and unhindered access to distributed bio-data, software and computational infrastructures. Furthermore, the need to support collaboration and form synergistic activities between the engaged researchers is also raised, as the mean to achieve consensus on respective scientific discoveries. The aforementioned needs and requirements underlie the motivations for the development of OpenBio-C workflow environment, and determine its fundamental objectives. The insufficiencies of current workflow editing and execution environments are explored, and the key-directions to advance over an environment that supports reproducibility are stated. The basic components and functionality of the OpenBio-C initial implementation (beta version) are detailed.

Project Management is a complex practice that is associated with a series of challenges to organizations and experts worldwide. Aiming to advance this practice, this paper proposes a hybrid approach that builds on the synergy between contemporary Machine Learning and Operations Research tools. The proposed approach integrates the predictive orientation of Machine Learning techniques with the prescriptive nature of Operations Research algorithms. It can aid the planning, monitoring and execution of common PM tasks such as resource allocation, task assignment, and task duration estimation. The applicability of our approach is demonstrated through two realistic examples.