Transformer Networks for Trajectory Forecasting

Most recent successes on forecasting the people motion are based on LSTM models and all most recent progress has been achieved by modelling the social interaction among people and the people interaction with the scene. We question the use of the LSTM models and propose the novel use of Transformer Networks for trajectory forecasting. This is a fundamental switch from the sequential step-by-step processing of LSTMs to the only-attention-based memory mechanisms of Transformers. In particular, we consider both the original Transformer Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art on all natural language processing tasks. Our proposed Transformers predict the trajectories of the individual people in the scene. These are "simple" model because each person is modelled separately without any complex human-human nor scene interaction terms. In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet. Additionally, its extension which predicts multiple plausible future trajectories performs on par with more engineered techniques on the 5 datasets of ETH + UCY. Finally, we show that Transformers may deal with missing observations, as it may be the case with real sensor data. Code is available at https://github.com/FGiuliari/Trajectory-Transformer.Comment: 18 pages, 3 figure

Human-centric light sensing and estimation from RGBD images: The invisible light switch

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices

MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses

Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution.Comment: 10 pages, 3 figures to appear in CVPR 201

Heritage documentation and management processes: Castiglioni Chapel in Pavia

The project of the reuse of an architectural environment must consider the set of residual services provided by the building. The activation of a documentation process allows you to be aware of what an environment offers. The spatial and material analysis of the current state must be accompanied by a material, technological and tech-system survey. In the case study of the Castiglioni Chapel in Pavia (Italy), the various diagnostic outputs were associated with the detailed survey, with the aim of planning interventions for the site conservation project. Conservation takes place both with physical actions and with museumization processes. The action methodology involved numerous phases such as detailed digital survey, technological analysis, and digitization through three-dimensional models. All the analyses carried out are integrated in an immersive virtual system structured by layers and scenarios. The chapel can be orbited with AR platforms, which allow you to enhance valuable frescoes and see the different levels of analysis in real time. All the processes underway in this project are aimed at making the space a place of knowledge with real and digital use

A systems biology approach to non-coding RNAs: the networks of cancer

A non-coding RNA is a functional RNA molecule that is not translated into a protein. This class of molecules is involved in many cellular processes and includes highly abundant and functionally important RNAs such as transfer RNA (tRNA), ribosomal RNA (rRNA), as well as small interfering RNAs (siRNAs), microRNAs (miRNAs), transcribed ultraconserved regions (T-UCRs) and others. First of all, we investigate the specificity for normal tissues of two selected non-coding RNAs: Transcribed UltraConserved Region and microRNAs. Second, we want to find whether these non-coding RNAs can be candidates as features for the selection of specific cancers, using statistical algorithms and bioinformatics tools. Third, we generate miRNA gene networks in normal and different cancer and leukemia. The term “ultraconserved” refer to genomic regions longer than 200 base pairs that are absolutely conserved (100% homology with no insertions or deletions) in human, mouse, and rat genomes. There are 481 T-UCRs. The reason for this extreme conservation remains a mystery; T-UCRs may play a functional role in the ontogeny and phylogeny of mammals and other vertebrates. Genome-wide profiling revealed that UCRs are frequently located on overlapping exons in genes involved in RNA processing and can be found in introns or at fragile sites and in cancer-associated genomic regions. We investigate the expression of T-UCRs in 374 normal samples from 46 different tissues, grouped by 16 systems. Moreover, we analyzed the specificity of T-UCRs in cancers. Tissue specific T-UCRs can differentiate cell types. We then examine the expression of T-UCRs in human embryonic stem cells, induced pluripotent stem cells, and a series of differentiated cell types (trophoblast, embryoid bodies at 7 and 14 days of differentiation, definitive endoderm, and spontaneous differentiated monolayers). One T-UCR in particular, uc.283 plus, is highly specific for embryonic and induced pluripotent stem cells, as confirmed by real time PCR (RT-PCR). MiRNAs are global regulators of protein output. Each miRNA has been studied for its single contribution to differential expression or to a compact predictive signature. Thus, we propose a study of miRNAs in cancer by applying a systems biology approach. We study miRNA profiles in 4419 human samples (3312 neoplastic, 1107 non-malignant), corresponding to 50 normal tissues (grouped by 17 systems) and 51 cancer types. We calculate tissue specificity and cancer type specificity, a small set of miRNAs were tissue-specific while many others were broadly expressed. Then we find whether non-coding RNAs can be candidates as features for the selection of specific cancers, using statistical algorithms and bioinformatics tools, as decision trees. Afterwards, we build miRNA gene networks by using our very large expression miRNA database. The complexity of our expression database enables us to perform a detailed analysis of coordinated miRNA activities. We also build specialized miRNA networks for different solid tumors and leukemias. Combining differential expression, genetic networks, DNA copy number alterations and other systems biology approaches we confirm or discovered miRNAs with comprehensive roles in cancer. We find that normal tissues are represented by single complete miRNA networks. Cancers instead show separate and unlinked miRNA sub-networks. miRNAs independent from the general transcriptional program were often known as cancer-related. We validate our results by in silico, in vitro and in vivo analysis. We demonstrate that the target genes of these uncoordinated miRNA involve in specific cancer-related pathways

Digital documentation of fortified urban routes in Pavia (Italy): territorial databases and structural models for the preservation of military ruins

[EN] The analysis of the fortified routes in the city of Pavia (Italy) clarifies the adaptation of the medieval capital in the historical politics of the Mediterranean, where the evolution of the defensive system till the Spanish bastioned walls (sixteenth century) identifies the updating of the Lombard tradition to the practices of modern military architecture. Their defensive structures survive in the urban design of the contemporary city, in the configuration of infrastructures and urban aggregates, reflecting the consequences of the great processes of their dismantling (from 1905). The comparison between historical investigations and the current ruins, fragmented into disconnected portions between the historical bastions and the monumental gates, shows a picture of abandonment of the military structures that generates repeated collapses and emerging risk factors towards the surrounding densified urban context. The experimentation of military architectural approaches of documentation at the urban scale, developed by the research laboratory DAda Lab. of University of Pavia, defines an analysis process through the digital representation of the urban remains that is suitable for the preservation of the survived city walls and the enhancement of their fortified identity. The application of different 3D LiDAR systems for morphological acquisition promotes an integrated digitation process of scansets on the fortified system controlled at the urban metric scale: the experimentation applies the use of a mobile real time scanner for the digital tracking of historical routes, on which to implement the georeferencing of detailed static scanworlds, integrated in correspondence of Bastions and Monumental Gates. The optimization of architectural data density and the integration between data contribute to finalize a 3D territorial database predisposed to the architectural modelling of volumes and scenarios of structural instability of the military ruins, defining a virtual framework of widespread knowledge for the historical conservation and urban prevention of the fortified systemDe Marco, R.; Galasso, F.; Malusardi, C. (2020). Digital documentation of fortified urban routes in Pavia (Italy): territorial databases and structural models for the preservation of military ruins. Editorial Universitat Politècnica de València. 349-356. https://doi.org/10.4995/FORTMED2020.2020.11518OCS34935

Non-coding RNAs: a key to future personalized molecular therapy?

Continual discoveries on non-coding RNA (ncRNA) have changed the landscape of human genetics and molecular biology. Over the past ten years it has become clear that ncRNAs are involved in many physiological cellular processes and contribute to molecular alterations in pathological conditions. Several classes of ncRNAs, such as small interfering RNAs, microRNAs, PIWI-associated RNAs, small nucleolar RNAs and transcribed ultra-conserved regions, are implicated in cancer, heart diseases, immune disorders, and neurodegenerative and metabolic diseases. ncRNAs have a fundamental role in gene regulation and, given their molecular nature, they are thus both emerging therapeutic targets and innovative intervention tools. Next-generation sequencing technologies (for example SOLiD or Genome Analyzer) are having a substantial role in the high-throughput detection of ncRNAs. Tools for non-invasive diagnostics now include monitoring body fluid concentrations of ncRNAs, and new clinical opportunities include silencing and inhibition of ncRNAs or their replacement and re-activation. Here we review recent progress on our understanding of the biological functions of human ncRNAs and their clinical potential

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065