The notion of trust is a major player, in many epistemic and computational contexts, among others. Such notion appears especially relevant in all those situations where verification or evaluation for knowledge is missing, not reachable or non-existent, and agents must rely on information received by others. This includes cases where expert knowers may not yet ground their claims, and the public has to build an opinion on the subject matter by considering the dynamic of the information exchange. Formal logic approaches to this aim have been increasingly important and diverse in the last decades. Recently, the notion of negative trust has been formalized in terms of a proof theory and semantics: this approach distinguishes between distrust as the epistemic act of rejecting incoming contradictory information considered unreliable; and mistrust as the epistemic act of updating one's own information state by removing previously held data in order to accommodate newly received information, considered more reliable or up to date. A natural extension of such multi-agent contexts is where epistemic acts are performed under uncertainty, at several levels: the claims of the agents may be graded; information may reach agents with a certain degree of probability; and the degree of acceptance or rejection of the information received may not be binary. In this talk we present a logic of negative trust applied to uncertain judgements. We offer a proof theory and a relational semantics for which standard soundness and completeness results hold. This is a joint work with Francesco Antonio Genco and Giuseppe Primiero.
Room D1.01 (Campus-Est),

12 January 2024 - 12 January 2024

SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results.
IDSIA Large meeting room

24 January 2024 - 24 January 2024

The Premurosa project, funded by the EU, has made significant use of machine learning to examine data and offer individualized insights on various aspects of medicine and biology. This includes the use of spiking neural networks to predict toxicity, bioaccumulation, and enzyme activity, as well as data fusion to interpret biomarkers or characterize biomaterials, and anomaly detection in proteomics.
Room C2.09, Sector C, East Campus USI-SUPSI

15 March 2024 - 15 March 2024

The topic of this second seminar will be “Causal Inference (not only) in Clinical Trials”. The topic will be introduced by three short talks (~10 min each) by Radka Švihrová, Michal Bechny, and Alvise De Rossi, PhD students @MeDiTech. After these three short talks there will be an open discussion on the topic.
Room B1.07

15 April 2024 - 18 April 2024

Dr. Federico Fusco, from Sapienza University of Rome, is going to teach a Ph.D. level class on "Submodular Optimization", 10 hours, 15th-18th April. The class is open to all the interested Ph.D. students (but more senior colleagues are very welcome to join). At the end there will be an optional graded homework for the interested Ph.D. students (which might grant 1ECTS if passed).
East Campus USI-SUPSI

17 June 2024 - 17 June 2024

A brain—machine interface (BMI) provides a direct communication and control pathway between the human brain and external devices. It plays a crucial role in stroke rehabilitation and prosthetic control. Traditional data processing workflow consists of transmitting brain signals to a remote processing engine where resource-demanding algorithms are executed to accurately extract useful information. Remote data transmission causes privacy concerns, risks of long latency, and high-power consumption yielding short battery life. To mitigate these issues, recent developments in smart wearable BMIs bring the processing near the sensors directly at the edge device where the data is collected and processed in real time. However, the resources available on wearable BMIs are limited posing big challenges in embedding accurate machine learning models at the edge despite the challenging inter-session and inter-subject variability of BMI signals. In this talk, we elaborate tiny machine learning for BMI paradigms and present optimization techniques and algorithms to successfully embed accurate models on resource-limited, non-stigmatizing BMI systems.
East Campus USI-SUPSI, room B1.07