Strategic Reciprocity in a Contest with Large Stakes

Using the unique properties of a German TV game show, we analyze the extent and implications of strategic reciprocity in sequential performance evaluations in a contest with large stakes. The sequential order of performances implies that the scope for strategic reciprocity differs systematically between participants: Contestants that perform later in the sequence evaluate their rivals before they are evaluated themselves, which creates incentives for strategic reciprocity. We find that earlier contestants benefit from this effect, resulting in a substantial negative sequence order bias. We provide estimates for the change in winning probabilities and for the financial implications of this bias

An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images

Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet despite its prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for assessing their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to DR severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. In this paper, we propose a machine learning system for the detection of referable DR in fundus images that is based on the paradigm of multiple-instance learning. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. Moreover, it can highlight potential image regions where DR manifests through its characteristic lesions. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance, while also producing interpretable visualizations of its predictions.Comment: 11 page

Food Image Classification and Segmentation with Attention-based Multiple Instance Learning

The demand for accurate food quantification has increased in the recent years, driven by the needs of applications in dietary monitoring. At the same time, computer vision approaches have exhibited great potential in automating tasks within the food domain. Traditionally, the development of machine learning models for these problems relies on training data sets with pixel-level class annotations. However, this approach introduces challenges arising from data collection and ground truth generation that quickly become costly and error-prone since they must be performed in multiple settings and for thousands of classes. To overcome these challenges, the paper presents a weakly supervised methodology for training food image classification and semantic segmentation models without relying on pixel-level annotations. The proposed methodology is based on a multiple instance learning approach in combination with an attention-based mechanism. At test time, the models are used for classification and, concurrently, the attention mechanism generates semantic heat maps which are used for food class segmentation. In the paper, we conduct experiments on two meta-classes within the FoodSeg103 data set to verify the feasibility of the proposed approach and we explore the functioning properties of the attention mechanism.Comment: Accepted for presentation at 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023

Autopoietic approach to cultural transmission

Non-representational cognitive science is a promising research field that provides an alternative to the view of the brain as a “computer” filled with symbolic representations of the world and cognition as “calculations” performed on those symbols. Autopoiesis is a biological, bottom-up, non-representational theory of cognition, in which representations and meaning are framed as explanatory concepts that are constituted in an observer’s description of a cognitive system, not operational concepts in the system itself. One of the problems of autopoiesis, and all non-representational theories, is that they struggle with scaling up to high-level cognitive behaviour such as language. The Iterated Learning Model is a theory of language evolution that shows that certain features of language are explained not because of something happening in the linguistic agent’s brain, but as the product of the evolution of the linguistic system itself under the pressures of learnability and expressivity. Our goal in this work is to combine an autopoietic approach with the cultural transmission chains that the ILM uses, in order to provide the first step in an autopoietic explanation of the evolution of language. In order to do that, we introduce a simple, joint action physical task in which agents are rewarded for dancing around each other in either of two directions, left or right. The agents are simulated e-pucks, with continuous-time recurrent neural networks as nervous systems. First, we adapt a biologically plausible reinforcement learning algorithm based on spike-timing dependent plasticity tagging and dopamine reward signals. We show that, using this algorithm, our agents can successfully learn the left/right dancing task and examine how learning time influences the agents’ task success rates. Following that, we link individual learning episodes in cultural transmission chains and show that an expert agent’s initial behaviour is successfully transmitted in long chains. We investigate the conditions under which these transmission chains break down, as well as the emergence of behaviour in the absence of expert agents. By using long transmission chains, we look at the boundary conditions for the re-establishment of transmitted behaviour after chain breakdowns. Bringing all the above experiments together, we discuss their significance for non-representational cognitive science and draw some interesting parallels to existing Iterated Learning research; finally, we close by putting forward a number of ideas for additions and future research directions

Impact of Guard Time Length on IEEE 802.15.4e TSCH Energy Consumption

International audienc

A Legislation-Based Database for COVID-19 Non-Pharmaceutical Interventions

In response to the COVID-19 pandemic, governments around the world imposed a wide variety of Non-Pharmaceutical Interventions (NPIs) in the form of restrictions of various aspects of social life, hoping to curb the spread of the SARS-CoV-2 virus. However, measures such as restrictions on public gatherings, the closure of schools, or the mandatory use of masks, raised several concerns in terms of both their necessity and effectiveness. The Observatory of Government Restrictive Measures for the COVID-19 pandemic (GovRM-COVID19), which began in November 2020 within the Center for Research on Democracy and Law of the University of Macedonia (Greece), has developed a database tracking all legislative measures imposing restrictions across different countries. The use of legislation as the main source of information with a daily frequency, as well as consideration of sub-federal entities in non-unitary (federal, devolved, etc.) states, provide one of the most accurate accounts of such restrictions. The end result provides researchers with accurate data on how various governments around the world have restricted individual rights and freedoms as a result of, and during, the COVID-19 pandemic, offering an opportunity for comparative research across different countries and policy strategies

Guard time optimisation and adaptation for energy efficient multi-hop TSCH networks

International audienceIn the IEEE 802.15.4-2015 standard, Time Slotted Channel Hopping (TSCH) aims to guarantee high-level network reliability by keeping nodes time-synchronised. In order to ensure successful communication between a sender and a receiver, the latter starts listening shortly before the expected time of a MAC layer frame's arrival. The offset between the time a node starts listening and the estimated time of frame arrival is called guard time and it aims to reduce the probability of missed frames due to clock drift. In this paper, we investigate the impact of the guard time on network performance. We identify that, when using the 6tisch minimal schedule, the most significant cause of energy consumption is idle listening during guard time. Therefore, we first perform mathematical modelling on a TSCH link to identify the guard time that maximises the energy-efficiency of the TSCH network in single hop topology. We then continue in multi-hop network, where we empirically adapt the guard time locally at each node depending its distance, in terms of hops, from the sink. Our performance evaluation results, conducted using the Contiki OS, demonstrate that the proposed decentralised guard time adaptation can reduce the energy consumption by up to 40%, without compromising network reliability