A natural framework for arbitrary multi-scale computer science and systems biology efficient computational modeling

The aim of the present paper is to provide the first concise overview of a natural framework for arbitrary multi-scale computer science and systems biology computational modeling. To grasp a more reliable representation of reality and to get more effective modeling techniques, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergically articulated by natural coupling. After a brief introduction about traditional modeling vs. fresh QFT approach, we go to the root of the problem directly. We present key points solution to arbitrary multi-scale modeling problems. The first attempt to identify basic principles to get stronger modeling solution for scientific application has been developing at Politecnico di Milano University since the 1990s. The fundamental principles on computational information conservation theory (CICT), for arbitrary multi-scale system modeling from basic generator and relation through discrete paths denser and denser to one another, towards a never ending 'blending quantum continuum,' are recalled. A computational example is presented and discussed. This paper is a relevant contribute towards arbitrary multi-scale computer science and systems biology modeling, to show how computational information conservation approach can offer stronger and more effective system modeling algorithms for more reliable simulation

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.Comment: CVPR 201

Why don't people use character-level machine translation?

We present a literature and empirical survey that critically assesses the state of the art in character-level modeling for machine translation (MT). Despite evidence in the literature that character-level systems are comparable with subword systems, they are virtually never used in competitive setups in WMT competitions. We empirically show that even with recent modeling innovations in character-level natural language processing, character-level MT systems still struggle to match their subword-based counterparts. Character-level MT systems show neither better domain robustness, nor better morphological generalization, despite being often so motivated. However, we are able to show robustness towards source side noise and that translation quality does not degrade with increasing beam size at decoding time.Comment: 16 pages, 4 figures; Findings of ACL 2022, camera-read

Open modeling for designing community ecosystems

The paper proposes an open approach to modeling to cater for the emerging trend to complex adaptive systems. Such systems are seen as collections of people, programs, computers and other physical objects that must coexist and work towards a vision in a continually changing environment. The information system here is perceived as a network of physical, knowledge and other kinds of entities connected into a network that emerges as the environment evolves. The paper describes a community oriented approach to model such systems where each community is seen as a collection of such entities. The communities themselves are connected to create a system of systems or a community ecosystem where the communities collaborate to realize a continually emerging vision. The paper describes an open modeling approach for such ecosystems to provide designers a systematic way to design community coordination. It first uses living systems and complexity as metaphors to design community structures that ensure collaboration persists over a long time. The modeling methods provide a flexible approach to show networks of community collaborating within their context. An open approach is to provide users with a flexible method to create community networks using semantics natural to the user and emphasizing perspectives to visualize the complex relationships within such systems

Modeling sustainable food systems

The processes underlying environmental, economic, and social unsustainability derive in part from the food system. Building sustainable food systems has become a predominating endeavor aiming to redirect our food systems and policies towards better-adjusted goals and improved societal welfare. Food systems are complex social-ecological systems involving multiple interactions between human and natural components. Policy needs to encourage public perception of humanity and nature as interdependent and interacting. The systemic nature of these interdependencies and interactions calls for systems approaches and integrated assessment tools. Identifying and modeling the intrinsic properties of the food system that will ensure its essential outcomes are maintained or enhanced over time and across generations, will help organizations and governmental institutions to track progress towards sustainability, and set policies that encourage positive transformations. This paper proposes a conceptual model that articulates crucial vulnerability and resilience factors to global environmental and socio-economic changes, postulating specific food and nutrition security issues as priority outcomes of food systems. By acknowledging the systemic nature of sustainability, this approach allows consideration of causal factor dynamics. In a stepwise approach, a logical application is schematized for three Mediterranean countries, namely Spain, France, and Italy

Joint Expansion Planning for Natural Gas and Electric Transmission with Endogenous Market Feedbacks

The recent and rapid shift towards the increased use of natural gas for power generation has convinced both power grid operators and regulators that additional coordination between electric power and natural gas transmission is needed to ensure the reliable operation of both systems. We report on an ongoing modeling effort for joint gas-grid expansion planning. We develop a Combined Electricity and Gas Expansion (CEGE) planning model that determines least-cost network expansions for power and gas transmission in a way that endogenizes the effects of expansion decisions on locational costs for electric power and natural gas deliveries. The CEGE model, which leverages recent advances in convex approximations for large-scale nonlinear systems, is illustrated on a new gas-grid test system topologically similar to the Northeastern United States. We show that the CEGE model is computationally tractable, and how the model might be used to jointly plan infrastructures to avoid extreme events such as the coincident gas-electric peaks experienced during the 2014 polar vortex

Statistical theory of normal grain growth revisited

In this paper, we discuss three physically relevant problems concerning the normal grain growth process.These are: Infinite vs finite size of the system under study (a step towards more realistic modeling); conditions of fine-grained structure formation, with possible applications to thin films and biomembranes, and interesting relations to superplasticity of materials; approach to log-normality, an ubiquitous natural phenomenon, frequently reported in literature. It turns out that all three important points mentioned are possible to be included in a Mulheran-Harding type behavior of evolving grains-containing systems that we have studied previously