Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default trajectories without centralizing sensitive data. The framework provides Bayesian (data-dependent) differential privacy (DP) guarantees while enabling institutions to jointly learn risk dynamics. Experiments on three real-world credit datasets (LendingClub, SBA, Bondora) show that federation fundamentally alters the relative effectiveness of privacy mechanisms. While classical DP performs better than Bayesian DP in centralized settings, the latter benefits substantially more from federation (+7.0\% vs +1.4\%), achieving near parity of non-private performance and outperforming classical DP in the majority of participating clients. This ranking reversal yields a key decision-support insight: privacy mechanism selection should be evaluated in the target deployment architecture, rather than centralized benchmarks. These findings provide actionable guidance for practitioners designing privacy-preserving decision support systems in regulated, multi-institutional environments.

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. These regimes emerge in small intervals of noise intensity values, which, in agreement with the extended criticality concept, never shrink to a single critical point. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.

In this paper, we propose a distributed framework for reducing the dimensionality of high-dimensional, large-scale, heterogeneous matrix-variate time series data using a factor model. The data are first partitioned column-wise (or row-wise) and allocated to node servers, where each node estimates the row (or column) loading matrix via two-dimensional tensor PCA. These local estimates are then transmitted to a central server and aggregated, followed by a final PCA step to obtain the global row (or column) loading matrix estimator. Given the estimated loading matrices, the corresponding factor matrices are subsequently computed. Unlike existing distributed approaches, our framework preserves the latent matrix structure, thereby improving computational efficiency and enhancing information utilization. We also discuss row- and column-wise clustering procedures for settings in which the group memberships are unknown. Furthermore, we extend the analysis to unit-root nonstationary matrix-variate time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size $T$. Simulation results assess the computational efficiency and estimation accuracy of the proposed framework, and real data applications further validate its predictive performance.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian attacks killed three people, including a 20-year-old woman, and injured 11 others in Ukraine's Kharkiv region, Governor Oleh Syniehubov wrote on Telegram on Sunday. In Ukraine's Kherson region, two people were killed, and one person was injured, as Russian forces launched attacks using drones, air strikes and shelling, Governor Oleksandr Prokudin said on Telegram on Sunday.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Hydrogen produces only water emissions, plus the fuel-cell trucks are quick. Breakthroughs, discoveries, and DIY tips sent six days a week. Filling up a hydrogen tank is much like filling up a gas-powered car in both the basic experience and in the time it takes. That's been a major barrier for EVs thus far; adding 20 minutes or more for each recharge on a road trip is not nearly as appealing as pulling up to a Chevron station and getting out of there in a few minutes. However, hydrogen hasn't yet caught on as a large-scale solution largely due to funding, even though even the US Department of Energy says it has "several benefits over conventional combustion-based technologies currently used in many power plants and vehicles."

Brightspeed investigates claims that hackers allegedly stole data from over 1 million customers after Crimson Collective threatened to release sample records.

Birthing a litter with several fathers may help when food is scarce. Breakthroughs, discoveries, and DIY tips sent six days a week. If a female house mouse mates with multiple male house mice, her litter could have multiple fathers. Polyandry, as this mating practice is called, is common for various species. Yet scientists are still investigating its purpose and the potential benefits of birthing half siblings within the same litter.

AI is asbestos in the walls of our tech society, stuffed there by monopolists run amok. What I do not do is predict the future. No one can predict the future, which is a good thing, since if the future were predictable, that would mean we couldn't change it. Now, not everyone understands the distinction. They think science-fiction writers are oracles. Even some of my colleagues labor under the delusion that we can "see the future". Then there are science-fiction fans who believe that they are the future. A depressing number of those people appear to have become AI bros. These guys can't shut up about the day that their spicy autocomplete machine will wake up and turn us all into paperclips has led many confused journalists and conference organizers to try to get me to comment on the future of AI. That's something I used to strenuously resist doing, because I wasted two years of my life explaining patiently and repeatedly why I thought crypto was stupid, and getting relentlessly bollocked by cryptocurrency cultists who at first insisted that I just didn't understand crypto.

Rictor X4 personal electric aircraft unveiled at CES 2026 promises affordable flight at $39,900 with 50 mph top speed, 20-minute flight time, and delivery scheduled for Q2 2026.