The rapid advancements in AI and Machine Learning necessitate a robust computational infrastructure to support cutting-edge research and industrial applications. From the academic and industrial AI community perspective, voiced in the recent ELISE project, the European AI platform is recommended to center around the EuroHPC growing ecosystem. It should be user-driven, easily accessible, powerful, and compliant with European regulations. AI-optimized and dedicated supercomputers for the European AI community are also coming, in addition to upgrading partitions of existing EuroHPC systems to 'AI enabled' stage. Related calls have been initiated in September 2024. Further, conventional EuroHPC systems are suggested to be extended with quantum computing, edge AI, and neuromorphic computing to cater to AI models deployed on network edge devices and sustainability in the long run. The challenges are presented in three case studies, ranging from training Transformers on HPC to LLMs trained federally across three different Euro HPC systems to recent results on hybrid classical-quantum application. This paper concludes with case studies results-informed next steps believed to benefit AI practitioners and the broader AI community.
We introduce graphical complexes of groups, which can be thought of as a generalisation of Coxeter systems with 1-dimensional nerves. We show that these complexes are strictly developable, and we equip the resulting Basic Construction with three structures of non-positive curvature: piecewise linear CAT(0), C(6) graphical small cancellation, and a systolic one. We then use these structures to establish various properties of the fundamental groups of these complexes, such as biautomaticity and Tits Alternative. We isolate an easily checkable condition implying hyperbolicity of the fundamental groups, and we construct some non-hyperbolic examples. We also briefly discuss a parallel theory of C(4)-T(4) graphical complexes of groups and outline their basic properties.
This paper presents the FactoryML framework that simplifies the deployment and integration of Machine Learning (ML) models in manufacturing factory environments. FactoryML facilitates packaging of ML models into a portable format and it facilitates the communication of deployed ML models in factory environments via Programmable Logic Controllers. In general FactoryML reduces the barrier to take learned models from a research and development side into an operational setting. The value of FactoryML has been demonstrated in a case study with a Danish company as well.
Secure Multi-Party Computation (MPC) constructions typically allow computation over a finite field or ring. While useful for many applications, certain real-world applications require the usage of decimal numbers. While it is possible to emulate floating-point operations in MPC, fixed-point computation has gained more traction in the practical space due to its simplicity and efficient realizations. Even so, current protocols for fixed-point MPC still require computing a secure truncation after each multiplication gate. In this paper, we show a new paradigm for realizing fixed-point MPC. Starting from an existing MPC protocol over arbitrary, large, finite fields or rings, we show how to realize MPC over a residue number system (RNS). This allows us to leverage certain mathematical structures to construct a secure algorithm for efficient approximate truncation by a static and public value. We then show how this can be used to realize highly efficient secure fixed-point computation. In contrast to previous approaches, our protocol does not require any multiplications of secret values in the underlying MPC scheme to realize truncation but instead relies on preprocessed pairs of correlated random values, which we show can be constructed very efficiently, when accepting a small amount of leakage and robustness in the strong, covert model. We proceed to implement our protocol, with SPDZ [28] as the underlying MPC protocol, and achieve significantly faster fixed-point multiplication.
Smartphones are often equipped with inertial sensors capable of measuring individuals' physical activities. Their role in monitoring the patients' physical activities in telemedicine, however, needs to be explored. The main objective of this study was to explore the correlation between a participant's daily step counts and the daily step counts reported by their smartphone. This prospective observational study was conducted on patients undergoing lower limb orthopedic surgery and a group of non-patients. The data collection period was from 2 weeks before until four weeks after the surgery for the patients and two weeks for the non-patients. The participants' daily steps were recorded by physical activity trackers employed 24/7, and an application recorded the number of daily steps registered by the participants' smartphones. We compared the cross-correlation between the daily steps time-series taken from the smartphones and physical activity trackers in different groups of participants. We also employed mixed modeling to estimate the total number of steps. Overall, 1067 days of data were collected from 21 patients (11 females) and 10 non-patients (6 females). The cross-correlation coefficient between the smartphone and physical activity tracker was 0.70 [0.53–0.83]. The correlation in the non-patients was slightly higher than in the patients (0.74 [0.60–0.90] and 0.69 [0.52–0.81], respectively). Considering the ubiquity, convenience, and practicality of smartphones, the high correlation between the smartphones and the total daily step time-series highlights the potential usefulness of smartphones in detecting the change in the step counts in remote monitoring of the patient's physical activity.
