Traditional extensions of the Nyquist-Johnson formula for thermal fluctuations in nonlinear dissipative elements have often led to thermodynamically inconsistent models and sparked long-standing debates about the proper interpretation of stochastic differential equations. In this work, we show that it is possible to derive, for each of the main stochastic interpretations, a Gaussian white-noise model for nonlinear dissipative elements at thermal equilibrium that fully complies with the fundamental principles of thermodynamics. The resulting models reproduce the Gibbs (Maxwell-Boltzmann) distribution and ensure zero-mean voltages and currents, thereby resolving the Brillouin paradox and maintaining consistency with the second law of thermodynamics. Furthermore, we demonstrate that these models satisfy additional thermodynamic requirements, including positive entropy production during transients and zero net heat exchange between dissipative elements at equilibrium.

Stability of ultra-low-voltage SRAM bitcells in retention mode is threatened by two types of uncertainty: process variability and intrinsic noise. While variability dominates the failure probability, noise-induced bit flips in weakened bitcells lead to dynamic instability. We study both effects jointly in a unified SPICE simulation framework. Starting from a synthetic representation of process variations introduced in a previous work, we identify the cases of poor noise immunity that require thorough noise analyses. Relying on a rigorous and systematic methodology, we simulate them in the time domain so as to emulate a true data retention operation. Short times to failure, unacceptable for a practical ultra-low-power memory system application, are recorded. The transient bit-flip mechanism is analyzed and a dynamic failure criterion involving the unstable steady state is established. We conclude that, beyond static variability, the dynamic noise inflates defectiveness among SRAM bitcells. Then, a stochastic nonlinear model, fully characterizable from conventional deterministic SPICE simulations, is presented. We then leverage it to efficiently and accurately predict the mean time to failure with an analytical Eyring-Kramers formula, recently extended to account for the varying-noise behavior of nonlinear systems.

Meta-learning is a line of research that develops the ability to leverage past experiences to efficiently solve new learning problems.In the context of Reinforcement Learning (RL), meta-RL methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information on a set of related tasks.The Alchemy benchmark has been proposed in [1] to test such methods.Alchemy features a rich structured latent space that is challenging for state-of-the-art model-free RL methods.These methods fail to learn to properly explore then exploit.We develop a model-based algorithm.We train a model whose principal block is a Transformer Decoder to fit the symbolic Alchemy environment dynamics.Then we define an online planner with the learned model using a tree search method.This algorithm significantly outperforms previously applied methods on the symbolic Alchemy problem.Our results reveal the relevance of model-based approaches with online planning to perform exploration and exploitation successfully in meta-RL.

Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain challenging due to the multi-scale composition of real networks and the heterogeneity of node metadata. Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. We characterize anomalous nodes in terms of the concentration retained for each node after smoothing specific signals localized on the vertices of the graph. Besides, we introduce a graph signal processing formulation of the Markov stability framework used in community detection, in order to find the context of anomalies. The performance of our method is assessed on synthetic and real-world attributed networks and shows superior results concerning state of the art algorithms. Finally, we show the scalability of our approach in large networks employing Chebychev polynomial approximations.

Identification of influential users in online social networks allows to facilitate efficient information diffusion to a large part of the network and thus benefiting diverse applications including viral marketing, disease control, and news dissemination. Existing methods have mainly relied on the network structure only for the detection of influential users. In this paper, we enrich this approach by proposing a fast, efficient, and unsupervised algorithm SmartInf to detect a set of influential users by identifying anchor nodes from a temporal sequence of retweets in Twitter cascades. Such anchor nodes provide important signatures of tweet diffusion across multiple diffusion localities and, hence, act as precursors for detection of influential nodes.1The set of influential nodes identified by SmartInf has the capacity to expose the tweet to a large and diverse population, when targeted as seeds thereby maximizing the influence spread. Experimental evaluation on empirical datasets from Twitter shows the superiority of SmartInf over state-of-the-art baselines in terms of infecting larger population; further, our evaluation shows that SmartInf is scalable to large-scale networks and is robust to missing data. Finally, we investigate the key factors behind the improved performance of SmartInf by testing our algorithm on a synthetic network using synthetic cascades simulated on this network. Our results reveal the effectiveness of SmartInf in identifying a diverse set of influential users that facilitate faster diffusion of tweets to a larger population.1We use the terms “influential nodes” and “influential users” interchangeably.

Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.