Positional Non-Cooperative Equilibrium

This paper presents and analyses a game theoretic model for resource allocation, where agents are status-seeking and consuming positional goods. We propose a unified framework to study the competition for resources where agents’ preferences are not necessarily ordered according to the absolute amount of goods they consume, but may depend on the consumption of others as well as on individual valuation of the goods at stake. Our model explicits the relation between absolute good distribution, individual evaluation and the level of consumption adopted by the opponents; such relation has the form of a status function.We show that given a certain set of properties, there exists only one possible status function. The competition mechanism implemented to maximise one own’s status is central in this work. As a result of the mathematical formulation, we show that the standard utility-maximisation paradigm emerges as a special case (non-positional competition). We then define a new class of games where the individual evaluations are negotiable and serve only the purpose of maximising one own’s status

PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition

In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA). It consists in designing a model able to generalize well to any unseen domain, regardless of the possibility to access target data at training time. Then, in a second phase, we extended the approach to work on unlabelled target data, allowing the model to adapt to the target distribution in an unsupervised fashion. For this purpose, we included in our framework existing UDA algorithms, such as Temporal Attentive Adversarial Adaptation Network (TA3N), jointly with new multi-stream consistency losses, namely Temporal Hard Norm Alignment (T-HNA) and Min-Entropy Consistency (MEC). Our submission (entry 'plnet') is visible on the leaderboard and it achieved the 1st position for 'verb', and the 3rd position for both 'noun' and 'action'.Comment: 3rd place in the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognitio

Domain generalization through audio-visual relative norm alignment in first person action recognition

First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that are yet to be addressed in this context. Indeed, the information extracted from learned representations suffers from an intrinsic "environmental bias". This strongly affects the ability to generalize to unseen scenarios, limiting the application of current methods to real settings where labeled data are not available during training. In this work, we introduce the first domain generalization approach for egocentric activity recognition, by proposing a new audiovisual loss, called Relative Norm Alignment loss. It rebalances the contributions from the two modalities during training, over different domains, by aligning their feature norm representations. Our approach leads to strong results in domain generalization on both EPIC-Kitchens-55 and EPIC-Kitchens-100, as demonstrated by extensive experiments, and can be extended to work also on domain adaptation settings with competitive results

IDDA: a large-scale multi-domain dataset for autonomous driving

Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented through the use of synthetic datasets, that have become a popular resource in this field. They have been released with the need to develop semantic segmentation algorithms able to close the visual domain shift between the training and test data. Although exacerbated by the use of artificial data, the problem is extremely relevant in this field even when training on real data. Indeed, weather conditions, viewpoint changes and variations in the city appearances can vary considerably from car to car, and even at test time for a single, specific vehicle. How to deal with domain adaptation in semantic segmentation, and how to leverage effectively several different data distributions (source domains) are important research questions in this field. To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types. Extensive benchmark experiments assess the dataset, showcasing open challenges for the current state of the art. The dataset will be available at: https://idda-dataset.github.io/home/ .Comment: Accepted at IROS 2020 and RA-L. Download at: https://idda-dataset.github.io/home

Deterioration trends of asphalt pavement friction and roughness from medium-term surveys on major Italian roads

Deterioration models are the key factor for effective Pavement Management Systems, helping out road agencies to assess the actual pavement condition and forecast future performance of the asset. Among pavement condition characteristics, friction should be taken into account due to its important effect on user safety, while roughness could be used to express user comfort. The purpose of this study was to provide a reasonable case study for future improvements of Italian road management, even if the length of the analyzed highways was not intended to be representative of the overall Italian network.This research studied the friction trend (Side Force Coefficient) depending on traffic levels (ESALs) and pavement aging for Italian highways, combining the data with roughness and macrotexture. Surface characteristics were monitored during a seven-year time span. A selection of different road sections with homogeneous traffic levels, similar environmental conditions and surface material was performed and high-speed/high-quality road surveys were used for distress data collection. Pavement deterioration models for Italian road sectors were developed at project level, as starting point to advance pavement management practices in Italy. Degradation curves showed the same trends for similar pavement structures, materials and traffic levels; on the other hand, differences in pavement characteristics, increased ESALs and various maintenance treatments significantly altered those trends. Keywords: Pavement Management System, Deterioration models, Friction, Roughness, MPD, High-speed monitorin

Two-step MAPbI3 deposition by low-vacuum proximity-space-effusion for high-efficiency inverted semitransparent perovskite solar cells

The innovative two-step Low Vacuum-Proximity Space Effusion (LV-PSE) method exploits the conversion of a textured PbI2 layer into MAPbI3 by adsorption–incorporation–migration of energetic MAI molecules, thus enabling a best efficiency of 17.5% in 150 nm thick layers

Contrasting information disorder by leveraging people’s biases and pains: innovating in the post-truth era

Disinformation and misinformation have been around since the advent of the media. Many solutions have been developed to contrast this phenomenon such as automated fact-checking tools, media literacy programs, or content moderation strategies. However, these endeavours are limited in scope and easily succumb to the ever changing online information landscape. In addition to that, the human brain is extremely susceptible to fake contents due to frequent biases and illusory effects. On this basis, the present paper describes the application of slightly readapted design thinking methodologies in tackling information disorder as an unconventional approach to global challenges

Competitiveness policies for medical tourism clusters: government initiative in Thailand

9 December 201