Designing incentives for a multi-agent system to induce a desirable Nash equilibrium is both a crucial and challenging problem appearing in many decision-making domains, especially for a large number of agents $N$. Under the exchangeability assumption, we formalize this incentive design (ID) problem as a parameterized mean-field game (PMFG), aiming to reduce complexity via an infinite-population limit. We first show that when dynamics and rewards are Lipschitz, the finite-$N$ ID objective is approximated by the PMFG at rate $\mathscr{O}(\frac{1}{\sqrt{N}})$. Moreover, beyond the Lipschitz-continuous setting, we prove the same $\mathscr{O}(\frac{1}{\sqrt{N}})$ decay for the important special case of sequential auctions, despite discontinuities in dynamics, through a tailored auction-specific analysis. Built on our novel approximation results, we further introduce our Adjoint Mean-Field Incentive Design (AMID) algorithm, which uses explicit differentiation of iterated equilibrium operators to compute gradients efficiently. By uniting approximation bounds with optimization guarantees, AMID delivers a powerful, scalable algorithmic tool for many-agent (large $N$) ID. Across diverse auction settings, the proposed AMID method substantially increases revenue over first-price formats and outperforms existing benchmark methods.

The integration of Large Language Models (LLMs) into autonomous robotic agents for conducting online transactions poses significant cybersecurity challenges. This study aims to enforce robust cybersecurity constraints to mitigate the risks associated with data breaches, transaction fraud, and system manipulation. The background focuses on the rise of LLM-driven robotic systems in e-commerce, finance, and service industries, alongside the vulnerabilities they introduce. A novel security architecture combining blockchain technology with multi-factor authentication (MFA) and real-time anomaly detection was implemented to safeguard transactions. Key performance metrics such as transaction integrity, response time, and breach detection accuracy were evaluated, showing improved security and system performance. The results highlight that the proposed architecture reduced fraudulent transactions by 90%, improved breach detection accuracy to 98%, and ensured secure transaction validation within a latency of 0.05 seconds. These findings emphasize the importance of cybersecurity in the deployment of LLM-driven robotic systems and suggest a framework adaptable to various online platforms.

There is no theoretical justification of why MFA outperforms FV on transfer learning form object level to holistic scene descriptor. The main argument of the paper about "... inability of the standard GMM ... to provide good approximation ..." in L73-75 needs proof or reference to appropriate literature rather than only experiment results. It needs to clarify why full covariance in MFA is the key to transfer learning problem on CNN features. I reckon it as a week argument although it was considered as second contribution of the paper because; any other dictionary learning method with full covariance should generate the same improvement as MFA according to authors' reasoning. An experienced reader is already aware of these formulations; hence it is expected to see the focus of formulation towards main claims which I could not see them there.

Major comments: This work examines GANs by comparing it to a simple mixture of factor analyzers (MFA) using NDB (a score based on sample histograms). The NDB computes the number of statistically different bins where the bins are obtained via Voronoi tessellation on k-means centroids. The key result is that the GMM/MFA is better able to capture the underlying distribution compared to GANs. When the MFA is combined with a pix2pix model, it generates sharp images comparable to the GAN model. Overall, this is a well-written paper with interesting results that question the overall utility of GANs.

Ukraine has introduced an AI spokesperson to provide information about its ongoing war efforts against Russia's invasion of the country. The AI spokesperson, named Victoria Shi – after'victory' and the Ukrainian abbreviation of'AI' – is based on the likeness of Ukrainian singer and influencer Rosalie Nombre who agreed to participate pro bono. The avatar is dressed in all black with aa Ukranian flag pin, hair pulled back and she's wearing studded earrings - but officials stressed the digital person and Nombre'are two different people.' In a video released by the Ministry of Foreign Affairs (MFA), Shi introduced herself and described her role and job functions, saying she was built to protect'the rights and interests of Ukrainian citizens abroad.' Victoria Shi, an AI spokesperson for Ukraine's Ministry of Foreign Affairs (pictured) will provide information about the governments ongoing war efforts against Russia's invasion The decision to add an AI MFA spokesperson was'not a whim,' but is a requirement of wartime efforts, the Minister of Foreign Affairs of Ukraine, Dmytro Kuleba, said in a Google-translated statement.

Speech emotion recognition aims to identify and analyze emotional states in target speech similar to humans. Perfect emotion recognition can greatly benefit a wide range of human-machine interaction tasks. Inspired by the human process of understanding emotions, we demonstrate that compared to quantized modeling, understanding speech content from a continuous perspective, akin to human-like comprehension, enables the model to capture more comprehensive emotional information. Additionally, considering that humans adjust their perception of emotional words in textual semantic based on certain cues present in speech, we design a novel search space and search for the optimal fusion strategy for the two types of information. Experimental results further validate the significance of this perception adjustment. Building on these observations, we propose a novel framework called Multiple perspectives Fusion Architecture Search (MFAS). Specifically, we utilize continuous-based knowledge to capture speech semantic and quantization-based knowledge to learn textual semantic. Then, we search for the optimal fusion strategy for them. Experimental results demonstrate that MFAS surpasses existing models in comprehensively capturing speech emotion information and can automatically adjust fusion strategy.

Gaussian Mixture Models (GMMs) are a standard tool in data analysis. However, they face problems when applied to high-dimensional data (e.g., images) due to the size of the required full covariance matrices (CMs), whereas the use of diagonal or spherical CMs often imposes restrictions that are too severe. The Mixture of Factor analyzers (MFA) model is an important extension of GMMs, which allows to smoothly interpolate between diagonal and full CMs based on the number of \textit{factor loadings} $l$. MFA has successfully been applied for modeling high-dimensional image data. This article contributes both a theoretical analysis as well as a new method for efficient high-dimensional MFA training by stochastic gradient descent, starting from random centroid initializations. This greatly simplifies the training and initialization process, and avoids problems of batch-type algorithms such Expectation-Maximization (EM) when training with huge amounts of data. In addition, by exploiting the properties of the matrix determinant lemma, we prove that MFA training and inference/sampling can be performed based on precision matrices, which does not require matrix inversions after training is completed. At training time, the methods requires the inversion of $l\times l$ matrices only. Besides the theoretical analysis and proofs, we apply MFA to typical image datasets such as SVHN and MNIST, and demonstrate the ability to perform sample generation and outlier detection.

Even though the brain operates in pure darkness, within the skull, it can infer the most likely causes of its sensory input. An approach to modelling this inference is to assume that the brain has a generative model of the world, which it can invert to infer the hidden causes behind its sensory stimuli, that is, perception. This assumption raises key questions: how to formulate the problem of designing brain-inspired generative models, how to invert them for the tasks of inference and learning, what is the appropriate loss function to be optimised, and, most importantly, what are the different choices of mean field approximation (MFA) and their implications for variational inference (VI).

The study of spoken languages comprises phonology, morphology, and grammar. Analysis of a language can be based on its syntax, semantics, and pragmatics. The languages can be classified as root languages, inflectional languages, and stem languages. All these factors lead to the formation of vocabulary which has commonality/similarity as well as distinct and subtle differences across languages. In this paper, we make use of Paninian system of sounds to construct a phonetic map and then words are represented as state transitions on the phonetic map. Each group of related words that cut across languages is represented by a m-language (morphological language). Morphological Finite Automata (MFA) are defined that accept the words belonging to a given m-language. This exercise can enable us to better understand the inter-relationships between words in spoken languages in both language-agnostic and language-cognizant manner.

The deal will make a range of Windward services available to MFA members. Bunker supplier industry group the Marine Fuels Alliance has signed a new deal with maritime AI firm Windward offering its services to the organisation's members. The partnership will see Windward providing educational sessions to MFA members and offering a bespoke package of AI-powered solutions, the MFA said in an emailed statement on Wednesday. Windward's services use machine learning and behavioural analytics models to help companies optimise business practices and navigate maritime risks in real time. "Upon launching the MFA in Oct 2021, we knew that the issue of sanctions was one of the biggest challenges facing bunker supply companies," Anthony Mollet, executive officer of the MFA, said in the statement.