School for Advanced Studies Lucca

Gianstefani I., Longo L., Riccaboni M.

Orsi C.

Amerini I., Barni M., Battiato S., Bestagini P., Boato G., Bruni V., Caldelli R., De Natale F., De Nicola R., Guarnera L., Mandelli S., Majid T., Marcialis G.L., Micheletto M., Montibeller A., et. al.

The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like “Impostor Bias”, a growing skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions. This paper is mainly based on the description of a research project called FF4ALL (FF4ALL-Detection of Deep Fake Media and Life-Long Media Authentication) for the detection and authentication of deepfakes, focusing on areas such as forensic attribution, passive and active authentication, and detection in real-world scenarios. By exploring both the strengths and limitations of current methodologies, we highlight critical research gaps and propose directions for future advancements to ensure media integrity and trustworthiness in an era increasingly dominated by synthetic media.

Gallo A., Saracco F., Lambiotte R., Garlaschelli D., Squartini T.

Most of the analyses concerning signed networks have focused on balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts. To fill this gap, we focus on signed, directed connections, with the aim of exploring the notion of frustration in such a context. When dealing with signed, directed edges, frustration is a multifaceted concept, admitting different definitions at different scales: if we limit ourselves to consider cycles of length 2, frustration is related to reciprocity, i.e., the tendency of edges to admit the presence of partners pointing in the opposite direction. As the reciprocity of signed networks is still poorly understood, we adopt a principled approach for its study, defining quantities and introducing models to consistently capture empirical patterns of the kind. In order to quantify the tendency of empirical networks to form either mutualistic or antagonistic cycles of length 2, we extend the exponential random graph framework to binary, directed, signed networks with global and local constraints and then compare the empirical abundance of the aforementioned patterns with the one expected under each model. We find that the (directed extension of the) balance theory is not capable of providing a consistent explanation of the patterns characterizing the directed, signed networks considered in this work. Although part of the ambiguities can be solved by adopting a coarser definition of balance, our results call for a different theory, accounting for the directionality of edges in a coherent manner. In any case, the evidence that the empirical, signed networks can be highly reciprocated leads us to recommend to explicitly account for the role played by bidirectional dyads in determining frustration at higher levels (e.g., the triadic one). Published by the American Physical Society 2025

Kitzler S., Balietti S., Saggese P., Haslhofer B., Strohmaier M.

We present a study analyzing the voting behavior of contributors, or vested users, in Decentralized Autonomous Organizations (DAOs). We evaluate their involvement in decision-making processes, discovering that in at least 7.54% of all DAOs, contributors, on average, held the necessary majority to control governance decisions. Furthermore, contributors have singularly decided at least one proposal in 20.41% of DAOs. Notably, contributors tend to be centrally positioned within the DAO governance ecosystem, suggesting the presence of inner power circles. Additionally, we observed a tendency for shifts in governance token ownership shortly before governance polls take place in 1202 (14.81%) of 8116 evaluated proposals. Our findings highlight the central role of contributors across a spectrum of DAOs, including Decentralized Finance protocols. Our research also offers important empirical insights pertinent to ongoing regulatory activities aimed at increasing transparency to DAO governance frameworks.

Leogrande E., Piccoli S., Dell’Olio F., Smania N., Mazzoleni S., Gandolfi M.

This case report explores the innovative integration of robotic and biomechatronic technologies, including the Motore and Ultra+ devices and neuro-suits, in a 10-session rehabilitation program for a young adult with dystonic spastic tetraparesis. Notable improvements were observed in upper limb motor function, coordination, and quality of life as measured by an increase of 18 pints on the Fugl-Meyer scale and a 25% improvement in the Bartle Index. Range of motion measurements showed consistent improvements, with task execution times improving by 10 s. These findings suggest the potential of combining wearable, robotic, and biomechatronic systems to enhance neurorehabilitation. Further refinement of these technologies might support clinicians in maximizing their integration in therapeutics, despite technical issues like synchronization issues that must be overcome.

Burgholzer L., Jimenez-Pastor A., Larsen K., Tribastone M., Tschaikowski M., Wille R.

Efficient methods for the simulation of quantum circuits on classical computers are crucial for their analysis due to the exponential growth of the problem size with the number of qubits. Here we study lumping methods based on bisimulation, an established class of techniques that has been proven successful for (classic) stochastic and deterministic systems such as Markov chains and ordinary differential equations. Forward constrained bisimulation yields a lower-dimensional model which exactly preserves quantum measurements projected on a linear subspace of interest. Backward constrained bisimulation gives a reduction that is valid on a subspace containing the circuit input, from which the circuit result can be fully recovered. We provide an algorithm to compute the constraint bisimulations yielding coarsest reductions in both cases, using a duality result relating the two notions. As applications, we provide theoretical bounds on the size of the reduced state space for well-known quantum algorithms for search, optimization, and factorization. Using a prototype implementation, we report significant reductions on a set of benchmarks. In particular, we show that constrained bisimulation can boost decision-diagram-based quantum circuit simulation by several orders of magnitude, allowing thus for substantial synergy effects.

Bemporad A.

Groh A.P., Guenther C., Schweizer D., Vismara S.

Recent global crises, such as the COVID-19 pandemic, severe supply chain disruptions, and ongoing geopolitical tensions, have profoundly reshaped the entrepreneurial and financial landscapes. This Special Issue of the Small Business Economics Journal explores these transformations. Key insights include the impact of unconventional monetary policy on SME financing, the success factors of bailout programs, the critical role of active investors in fostering firm resilience, the implementation of digitizing technologies, and the adoption of survival strategies on a microeconomic level. Together, these findings underscore the flexibility and resilience of entrepreneurs and offer actionable lessons for policy and practice. Extended periods of crises reshape entrepreneurial finance markets—spurring digital innovation, resilient business models, and new designs of bailout programs. Recent global crises, including the COVID-19 pandemic, the Ukraine war, and inflation, have disrupted economies, forcing entrepreneurs to adapt quickly. Challenges such as tighter financing conditions, supply chain disruptions, and rising costs drove businesses to adopt innovative strategies, e.g., raising (bailout) capital, revamping inventory management, and employing digital financial tools. While many ventures struggled with higher costs and uncertain markets, the adaptability of entrepreneurs demonstrated their vital role in economic resilience. This special issue collects timely papers that underline this resilience and provides managerial and policy recommendations for future crises. This paper summarizes the findings about the importance of fostering entrepreneurial ecosystems through targeted financial support policies, digital transformation initiatives, and managerial adaptions to enhance economic resilience during periods of extended crises.

Clerici L., Bottari D., Bottari B.

Abstract Purpose of Review This review explores the intricate relationships among the gut microbiota, dietary patterns, and mental health, focusing specifically on depression. It synthesizes insights from microbiological, nutritional, and neuroscientific perspectives to understand how the gut-brain axis influences mood and cognitive function. Recent Findings Recent studies underscore the central role of gut microbiota in modulating neurological and psychological health via the gut-brain axis. Key findings highlight the importance of dietary components, including probiotics, prebiotics, and psychobiotics, in restoring microbial balance and enhancing mood regulation. Different dietary patterns exhibit a profound impact on gut microbiota composition, suggesting their potential as complementary strategies for mental health support. Furthermore, mechanisms like tryptophan metabolism, the HPA axis, and microbial metabolites such as SCFAs are implicated in linking diet and microbiota to depression. Clinical trials show promising effects of probiotics in alleviating depressive symptoms. Summary This review illuminates the potential of diet-based interventions targeting the gut microbiota to mitigate depression and improve mental health. While the interplay between microbial diversity, diet, and brain function offers promising therapeutic avenues, further clinical research is needed to validate these findings and establish robust, individualized treatment strategies.

Loconte R., Battaglini C., Maldera S., Pietrini P., Sartori G., Navarin N., Monaro M.

Detecting deception in interpersonal communication is a pivotal issue in social psychology, with significant implications for court and criminal proceedings. In this study, four experiments were designed to compare the performance of natural language processing (NLP) techniques and human judges in detecting deception from linguistic cues in a dataset of 62 transcriptions of video-taped interviews (32 genuine and 30 deceptive). The results showed that machine-learning algorithms significantly outperform naïve (accuracy = 54.7%) and expert judges (accuracy = 59.4%) when trained on features from the reality monitoring (RM) and cognitive load frameworks (accuracy = 69.4%) or on features automatically extracted through NLP techniques (accuracy = 77.3%) but not when trained on the RM criteria alone. This evidence suggests that NLP algorithms, due to their ability to handle complex patterns of linguistic data, might be useful for better disentangling truthful from deceptive narratives, outperforming traditional theoretical models.

Soman S., Zanon M., Bemporad A.

Placini F., Bargnesi F., Di Cicco D., Rinaldi D., Balestra S., Berloffa S., Viglione V., Fantozzi P., Tolomei G., Schirone G., Milone A., Masi G., Sesso G.

Mulagaleti S.K., Mejari M., Bemporad A.

Adeoye A.D., Bemporad A.