Global Modeling and Prediction of Computer Network Traffic

We develop a probabilistic framework for global modeling of the traffic over a computer network. This model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time--scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain 'space-time' random fields. The model is validated against simulated and real data. It is then applied to predict traffic fluctuations over unobserved links from a limited set of observed links. Further, applications to anomaly detection and network management are briefly discussed

Young drivers’ pedestrian anti-collision braking operation data modelling for ADAS development

Smart cities and smart mobility come from intelligent systems designed by humans. Artificial Intelligence (AI) is contributing significantly to the development of these systems, and the automotive industry is the most prominent example of "smart" technology entering the market: there are Advanced Driver Assistance System (ADAS), Radar/LIDAR detection units and camera-based Computer Vision systems that can assess driving conditions. Actually, these technologies have become consumer goods and services in mass-produced vehicles to provide human drivers with tools for a more comfortable and safer driving. Nevertheless, they need to be further improved for progress in the transition to fully automated driving or simply to increase vehicle automation levels. To this end, it becomes imperative to accurately predict driver’s decisions, model human driving behaviors, and introduce more accurate risk assessment metrics. This paper presents a system that can learn to predict the future braking behavior of a driver in a typically urban vehicle-pedestrian conflict, i.e., when a pedestrian enters a zebra crossing from the curb and a vehicle is approaching. The algorithm proposes a sequential prediction of relevant operational indicators that continuously describe the encounter process. A car driving simulator was used to collect reliable data on braking behaviours of a cohort of 68 licensed university students, who faced the same urban scenario. The vehicle speed, steering wheel angle, and pedal activity were recorded as the participants approached the crosswalk, along with the azimuth angle of the pedestrian and the relative longitudinal distance between the vehicle and the pedestrian: the proposed system employs the vehicle information as human driving decisions and the pedestrian information as explanatory variables of the environmental state. In fact, the pedestrian’s polar coordinates are usually calculated by an on-board millimeter-wave radar which is typically used to perceive the environment around a vehicle. All mentioned information is represented in the form of time series data and is used to train a recurrent neural network in a supervised machine learning process. The main purpose of this research is to define a system of behavioral profiles in non-collision conditions that could be used for enhancing the existing intelligent driving systems, e.g., to reduce the number of warnings when the driver is not on a collision course with a pedestrian. Preliminary experiments reveal the feasibility of the proposed system

A Search for Dark Matter Annihilation with the Whipple 10m Telescope

We present observations of the dwarf galaxies Draco and Ursa Minor, the local group galaxies M32 and M33, and the globular cluster M15 conducted with the Whipple 10m gamma-ray telescope to search for the gamma-ray signature of self-annihilating weakly interacting massive particles (WIMPs) which may constitute astrophysical dark matter (DM). We review the motivations for selecting these sources based on their unique astrophysical environments and report the results of the data analysis which produced upper limits on excess rate of gamma rays for each source. We consider models for the DM distribution in each source based on the available observational constraints and discuss possible scenarios for the enhancement of the gamma-ray luminosity. Limits on the thermally averaged product of the total self-annihilation cross section and velocity of the WIMP, , are derived using conservative estimates for the magnitude of the astrophysical contribution to the gamma-ray flux. Although these limits do not constrain predictions from the currently favored theoretical models of supersymmetry (SUSY), future observations with VERITAS will probe a larger region of the WIMP parameter phase space, and WIMP particle mass (m_\chi).Comment: 33 pages, 12 figures, accepted for publication in the Astrophysical Journa

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Towards Indicators for Assessing Land-Use Change in Planning: Case Study of the Peri-Urban Zone of Thessaloniki.

Expanding urban areas face growing land use conflicts particularly in the peri-urban zone, which is defined as a zone outside the city, occupied both by ‘classical’ rural land uses, and construction of road infrastructure and commercial shopping centers, which result as rapid changes. These changes of the peri-urban zone lead to complex patterns of land uses as evidenced in terms of the intensity and structure. To the extent that modern societies need to understand such patterns in order to formulate appropriate guidance policies, it is interesting to develop a relevant framework of analysis. It is necessary to assess land-use change in order to assist urban planning and related decision-making. The proposed approach explores an analytical framework combining GIS and a system of PSI (pressure-state-impact) indicators aimed at the analysis of urban growth and land use change in the peri-urban zone of Thessaloniki. Thessaloniki is the second largest city of Greece which is located in the Northern part of the country and has approximately one million inhabitants.

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c