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We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements.

The performance of an application/runtime is usually thought of as a continuous function where, the lower the amount of memory/time used on a given workload, then the better the compiler/runtime is. However, in practice, good performance of an application is conceptually more of a binary function -- either the application responds in under, say 100ms, and is fast enough for a user to barely notice, or it takes a noticeable amount of time, leaving the user waiting and potentially abandoning the task. Thus, performance really means how often the application is fast enough to be usable, leading industrial developers to focus on the 95th and 99th percentile latencies as heavily, or moreso, than average response time. Unfortunately, tracking and optimizing for these high percentile latencies is difficult and often requires a deep understanding of the application, runtime, GC, and OS interactions. This is further complicated by the fact that tail performance is often only seen occasionally, and is specific to a certain workload or input, making these issues uniquely painful to handle. Our vision is to create a language and runtime that is designed to be $\Omega(c)$ in its performance -- that is, it is designed to have an effectively constant time to execute all operations, there is a constant fixed memory overhead for the application footprint, and the garbage-collector performs a constant amount of work per allocation + a (small) bounded pause for all collection/release operations.

Osteoarthritis (OA) is a prevalent degenerative joint disease, with the knee being the most commonly affected joint. Modern studies of knee joint injury and OA often measure biomechanical variables, particularly forces exerted during walking. However, the relationship among gait patterns, clinical profiles, and OA disease remains poorly understood. These biomechanical forces are typically represented as curves over time, but until recently, studies have relied on discrete values (or landmarks) to summarize these curves. This work aims to demonstrate the added value of analyzing full movement curves over conventional discrete summaries. Using data from the Intensive Diet and Exercise for Arthritis (IDEA) study (Messier et al., 2009, 2013), we developed a shape-based representation of variation in the full biomechanical curves. Compared to conventional discrete summaries, our approach yields more powerful predictors of disease progression and relevant clinical traits, as demonstrated by a nested model comparison. Notably, our work is among the first to use movement curves to predict disease progression and to quantitatively evaluate the added value of analyzing full movement curves over conventional discrete summaries

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multi-faceted and multi-block data to study the complex human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact with each other. In this study, we comprehensively analyze the multi-block HCP data using the Data Integration via Analysis of Subspaces (DIVAS) method. We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14\% of the variation in functional connectivity (FC) and roughly 12\% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability, such as alcohol consumption in the substance-use data block. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and Substance Use) shared space, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain's role in physiological responses to increased substance use. Furthermore, our findings have been validated using a subset of genetically relevant siblings or twins not studied in the main analysis., Comment: 38 pages, 6 figures

We describe a representation targeted for anatomic objects which is designed to enable strong locational correspondence within object populations and thus to provide powerful object statistics. The method generates fitted frames on the boundary and in the interior of objects and produces alignment-free geometric features from them. It accomplishes this by understanding an object as the diffeomorphic deformation of an ellipsoid and using a skeletal representation fitted throughout the deformation to produce a model of the target object, where the object is provided initially in the form of a boundary mesh. Via classification performance on hippocampi shape between individuals with a disorder vs. others, we compare our method to two state-of-the-art methods for producing object representations that are intended to capture geometric correspondence across a population of objects and to yield geometric features useful for statistics, and we show improved classification performance by this new representation, which we call the evolutionary s-rep. The geometric features that are derived from each of the representations, especially via fitted frames, is discussed.

Integration, composition, mechanization, and AI assisted development are the driving themes in the future of software development. At their core these concepts are rooted in the increasingly important role of computing in our world, the desire to deliver functionality faster, with higher quality, and to empower more people to benefit from programmatic automation. These themes, and how they impact the human developers driving them, are the foundations for the next generation of programming languages. At first glance the needs of mechanization tools, AI agents, and human developers along with the various goals around development velocity, software quality, and software democratization are a broad and seemingly diverse set of needs. However, at their core is a single challenge that, once resolved, enables us to make radical progress in all of these areas. Our hypothesis is that, fundamentally, software development is a problem of reasoning about code and semantics. This is true for human developers implementing a feature, symbolic tools building models of application behavior, and even for language based AI agents as they perform tasks. While the particular aspects of reasoning that each agent struggles with varies to some degree, they share many common themes and, surprisingly, most mainstream languages extensively employ (anti)features that make this task harder or infeasible! This paper proposes a novel approach to this challenge -- instead of new language features or logical constructs, that add more complexity to what is already a problem of complexity, we propose radical simplification in the form of the Bosque platform and language.

Language Models (LMs) have shown their application for tasks pertinent to code and several code~LMs have been proposed recently. The majority of the studies in this direction only focus on the improvements in performance of the LMs on different benchmarks, whereas LMs are considered black boxes. Besides this, a handful of works attempt to understand the role of attention layers in the code~LMs. Nonetheless, feed-forward layers remain under-explored which consist of two-thirds of a typical transformer model's parameters. In this work, we attempt to gain insights into the inner workings of code language models by examining the feed-forward layers. To conduct our investigations, we use two state-of-the-art code~LMs, Codegen-Mono and Ploycoder, and three widely used programming languages, Java, Go, and Python. We focus on examining the organization of stored concepts, the editability of these concepts, and the roles of different layers and input context size variations for output generation. Our empirical findings demonstrate that lower layers capture syntactic patterns while higher layers encode abstract concepts and semantics. We show concepts of interest can be edited within feed-forward layers without compromising code~LM performance. Additionally, we observe initial layers serve as ``thinking'' layers, while later layers are crucial for predicting subsequent code tokens. Furthermore, we discover earlier layers can accurately predict smaller contexts, but larger contexts need critical later layers' contributions. We anticipate these findings will facilitate better understanding, debugging, and testing of code~LMs.

The European Union's technological sovereignty strategy centers around the RISC-V Instruction Set Architecture, with the European Processor Initiative leading efforts to build production-ready processors. Focusing on realizing a functional RISC-V cloud ecosystem, the Vitamin-V European project developed an OpenStack cluster utilizing genuine hardware. In this poster, we detail the efforts done in porting and setting up the cluster and the many software services required by OpenStack to properly run on real hardware. In this poster, we detail our efforts on building an minimal viable prototype OpenStack cluster using real hardware. The cluster is almost functional, and we expect it to be complete in the next few months., Comment: RISC-V Summit Europe 2024

The practice of programming is undergoing a revolution with the introduction of AI assisted development (copilots) and the creation of new programming languages that are designed explicitly for tooling, analysis, and automation. Integrated Development Environments (IDEs) as they are currently conceptualized have not yet responded to these changes. They are still designed around the idea of a human programmer typing textual code into an editor window with the IDE providing assistance via the integration of various tools for syntax highlighting, compilation, debugging, and (maybe) code version control. This paper presents a vision for transforming the IDE from an Integrated Development Environment to an Intelligent Development Environment. The new IDE will be designed around the idea of a human programmer as the manager or curator of a software project who, rather than manually typing in code to implement a solution, will instead use the IDE to direct AI programming agents and/or automated tools to combine existing APIs, packages, and new code to implement the needed features. In this new model, the fundamental roles of the IDE are to 1) facilitate the communication between the human programmer and the AI agents and automated tools and 2) organize the workflow tasks needed to go from requirements gathering to the final tested and validated deployed feature. This paper presents a vision for the new Intelligent Development Environment based on a range of proof-of-concept high-value scenarios we have experimented with and discusses the challenges that remain to realizing these in a cohesive intelligent development experience.

Among the key contributions of Vitamin-V (2023-2025 Horizon Europe project), we develop a complete RISC-V open-source software stack for cloud services with comparable performance to the cloud-dominant x86 counterpart. In this paper, we detail the software suites and applications ported plus the three cloud setups under evaluation., Comment: RISC-V Summit Europe 2024, 24-28 June 2024

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors., Comment: 5 figures, NeurIPS 2022 Workshop

The manual modeling of complex systems is a daunting task; and although a plethora of methods exist that mitigate this issue, the problem remains very difficult. Recent advances in generative AI have allowed the creation of general-purpose chatbots, capable of assisting software engineers in various modeling tasks. However, these chatbots are often inaccurate, and an unstructured use thereof could result in erroneous system models. In this paper, we outline a method for the safer and more structured use of chatbots as part of the modeling process. To streamline this integration, we propose leveraging scenario-based modeling techniques, which are known to facilitate the automated analysis of models. We argue that through iterative invocations of the chatbot and the manual and automatic inspection of the resulting models, a more accurate system model can eventually be obtained. We describe favorable preliminary results, which highlight the potential of this approach., Comment: This is a preprint version of a paper that will appear at Modelsward 2024

Methodologies for development of complex systems and models include external reviews by domain and technology experts. Among others, such reviews can uncover undocumented built-in assumptions that may be critical for correct and safe operation or constrain applicability. Since such assumptions may still escape human-centered processes like reviews, agile development, and risk analyses, here, we contribute toward making this process more methodical and automatable. We first present a blueprint for a taxonomy and formalization of the problem. We then show that a variety of digital artifacts of the system or model can be automatically checked against extensive reference knowledge. Since mimicking the breadth and depth of knowledge and skills of experts may appear unattainable, we illustrate the basic feasibility of automation with rudimentary experiments using OpenAI's ChatGPT. We believe that systematic handling of this aspect of system engineering can contribute significantly to the quality and safety of complex systems and models, and to the efficiency of development projects. We dedicate this work to Werner Damm, whose contributions to modeling and model-based development, in industry and academia, with a special focus on safety, helped establish a solid foundation to our discipline and to the work of many scientists and professionals, including, naturally, the approaches and techniques described here.

Commonly recognized evolutionarily relevant effects of sexual reproduction include increased diversity, accelerated adaptation, and constrained accumulation of deleterious mutations, along with a secondary effect of species genotype homogenization. Still, strong published arguments prioritize the contribution of biological mechanisms underlying bi-parental reproduction to maintaining species identity above their contribution to diversity. Here, we contribute to the latter position. In an initial mathematical analysis and simulation, we show that in an environment where copying is prone to error, quantitative polygenic traits that are shared within a parents' generation are transmitted to future generations under bi-parental reproduction with less deviation than under asexual reproduction. Furthermore, we abstract away many biological details, and show that this trait conservation is a general statistical effect, driven by the very nature of mixing of parental traits, separately from DNA repair and from the reproductive failures, barriers and disadvantages induced by biological mechanisms. Since survival of ecosystem interaction networks depends on the ability of individuals to replace the networked function of failing, dying or absent members of the same species, more faithful inheritance of common traits helps sustain species and ecosystems. This sustaining effect may have contributed to the very evolution of sexual reproduction., Comment: Relative to V4: Misc edits throughout. Added some citations. Changed article format

Clustering methods are popular for revealing structure in data, particularly in the high-dimensional setting common to contemporary data science. A central statistical question is, "are the clusters really there?" One pioneering method in statistical cluster validation is SigClust, but it is severely underpowered in the important setting where the candidate clusters have unbalanced sizes, such as in rare subtypes of disease. We show why this is the case, and propose a remedy that is powerful in both the unbalanced and balanced settings, using a novel generalization of k-means clustering. We illustrate the value of our method using a high-dimensional dataset of gene expression in kidney cancer patients. A Python implementation is available at https://github.com/thomaskeefe/sigclust., Comment: 23 pages, 11 figures

In his seminal paper ``Computing Machinery and Intelligence'', Alan Turing introduced the ``imitation game'' as part of exploring the concept of machine intelligence. The Turing Test has since been the subject of much analysis, debate, refinement and extension. Here we sidestep the question of whether a particular machine can be labeled intelligent, or can be said to match human capabilities in a given context. Instead, we first draw attention to the seemingly simpler question a person may ask themselves in an everyday interaction: ``Am I interacting with a human or with a machine?''. We then shift the focus from seeking a method for eliciting the answer, and, rather, reflect upon the importance and significance of this Human-or-Machine question and the use one may make of a reliable answer thereto. Whereas Turing's original test is widely considered to be more of a thought experiment, the Human-or-Machine matter as discussed here has obvious practical relevance. While it is still unclear if and when machines will be able to mimic human behavior with high fidelity in everyday contexts, we argue that near-term exploration of the issues raised here can contribute to refinement of methods for developing computerized systems, and may also lead to new insights into fundamental characteristics of human behavior.

Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent field of data integration develops and applies new methods for studying multi-block data and identifying how different data types relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially-shared structure between sub-collections of data types. This work presents a new approach: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex-concave optimization into one algorithm for exploring partially-shared structure. Based on principal angles between subspaces, DIVAS provides built-in inference on the results of the analysis, and is effective even in high-dimension-low-sample-size (HDLSS) situations.

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology help us to reduce costs and increase the speed and accuracy of regions of interest detection and cancer diagnosis. In this work, we propose a patch-based region of interest detection method for melanocytic skin tumor whole-slide images. We work with a dataset that contains 165 primary melanomas and nevi Hematoxylin and Eosin whole-slide images and build a deep-learning method. The proposed method performs well on a hold-out test data set including five TCGA-SKCM slides (accuracy of 93.94\% in slide classification task and intersection over union rate of 41.27\% in the region of interest detection task), showing the outstanding performance of our model on melanocytic skin tumor. Even though we test the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems, such as various tumors' classification and prediction, to help and benefit the clinical evaluation and diagnosis of different tumors., Comment: Accepted to MedNeurIPS 2022