Data-driven approaches have emerged as a powerful alternative to traditional numerical methods for forecasting physical systems, offering fast inference and reduced computational costs. However, for complex systems and those without prior knowledge, the accuracy of long-term predictions frequently deteriorates due to error accumulation. Existing solutions often adopt an autoregressive approach that unrolls multiple time steps during each training iteration; although effective for long-term forecasting, this method requires storing entire unrolling sequences in GPU memory, leading to high resource demands. Moreover, optimizing for long-term accuracy in autoregressive frameworks can compromise short-term performance. To address these challenges, we introduce the Stochastic PushForward (SPF) training framework in this paper. SPF preserves the one-step-ahead training paradigm while still enabling multi-step-ahead learning. It dynamically constructs a supplementary dataset from the model's predictions and uses this dataset in combination with the original training data. By drawing inputs from both the ground truth and model-generated predictions through a stochastic acquisition strategy, SPF naturally balances short-and long-term predictive performance and further reduces overfitting and improves generalization. Furthermore, the training process is executed in a one-step-ahead manner, with multi-step-ahead predictions precomputed between epochs--thus eliminating the need to retain entire unrolling sequences in memory, thus keeping memory usage stable. We demonstrate the effectiveness of SPF on the Burgers' equation and the Shallow Water benchmark. Experimental results demonstrated that SPF delivers superior long-term accuracy compared to autoregressive approaches while reducing memory consumption. Supplementary dataset update interval Test cases V Flow speed for Burgers' equation h Total water depth including the undisturbed water depth u, v Velocity components in the x (horizontal) and y (vertical) directions g Gravitational acceleration r Spatial euclidean distance ϵ Balgovind type of correlation function L Typical correlation length scale 2 1. Introduction Over many years, scientific research has produced highly detailed mathematical models of physical phenomena[1]. These models are frequently and naturally expressed in the form of differential equations [2], most commonly as time-dependent Partial differential equation (PDE)s.

Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with predefined conceptual domains. This paper introduces Seeded Poisson Factorization (SPF), a novel approach that extends the Poisson Factorization framework by incorporating domain knowledge through seed words. SPF enables a more interpretable and structured topic discovery by modifying the prior distribution of topic-specific term intensities, assigning higher initial rates to predefined seed words. The model is estimated using variational inference with stochastic gradient optimization, ensuring scalability to large datasets. We apply SPF to an Amazon customer feedback dataset, leveraging predefined product categories as guiding structures. Our evaluation demonstrates that SPF achieves superior classification performance compared to alternative guided topic models, particularly in terms of computational efficiency and predictive performance. Furthermore, robustness checks highlight SPF's ability to adaptively balance domain knowledge and data-driven topic discovery, even in cases of imperfect seed word selection. These results establish SPF as a powerful and scalable alternative for integrating expert knowledge into topic modeling, enhancing both interpretability and efficiency in real-world applications.

Multi-agent systems (MAS) are increasingly applied to complex task allocation in two-sided markets, where agents such as companies and customers interact dynamically. Traditional company-led Stackelberg game models, where companies set service prices, and customers respond, struggle to accommodate diverse and personalised customer demands in emerging markets like crowdsourcing. This paper proposes a customer-led Stackelberg game model for cost-efficient task allocation, where customers initiate tasks as leaders, and companies create their strategies as followers to meet these demands. We prove the existence of Nash Equilibrium for the follower game and Stackelberg Equilibrium for the leader game while discussing their uniqueness under specific conditions, ensuring cost-efficient task allocation and improved market performance. Using the satellite constellation services market as a real-world case, experimental results show a 23% reduction in customer payments and a 6.7-fold increase in company revenues, demonstrating the model's effectiveness in emerging markets.

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly Behavior Analysis (ABA)-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behavior. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset - a real-world dataset collected by the team using an AV testbed. The dataset has been publicly released for the global research community. To the best of our knowledge, this dataset is the first of its kind and will serve as a useful resource to address such security challenges.

The tandem approach is characteristic of Standard metrics can be applied to evaluate the performance of the majority of related work, including studies involving other isolated spoofing detection solutions and others have been proposed biometric traits [10, 11]. to support their evaluation when they are combined with Standard metrics developed for the evaluation of speaker speaker detection. These either have well-known deficiencies or detectors can also be applied to the evaluation of spoof detectors, restrict the architectural approach to combine speaker and spoof also known as countermeasures (CMs); they are both binary detectors. In this paper, we propose an architecture-agnostic classifiers. Alternative metrics proposed in recent years also detection cost function (a-DCF). A generalisation of the original support the evaluation of speaker and spoof detectors when DCF used widely for the assessment of automatic speaker combined [12, 13]. While the combination of speaker and spoof verification (ASV), the a-DCF is designed for the evaluation detectors still constitutes a single, binary classifier with the very of spoofing-robust ASV. Like the DCF, the a-DCF reflects the same original task of accepting bonafide target trials and rejecting cost of decisions in a Bayes risk sense, with explicitly defined anything else, the consideration of spoofing complicates class priors and detection cost model.

Researchers from France have devised a method for identifying newly-registered domains that are likely to be used in a'hit and run' fashion by high-volume email spammers – sometimes, even before the spammers have sent out one unwanted email. The technique is based on analysis of the way that that the Sender Policy Framework (SPF), a method of verifying email provenance, has been set up on newly-registered domains. Thanks to the use of passive DNS (Domain Name System) sensors, the researchers were able to obtain near real-time DNS data from Seattle-based company Farsight, yielding SPF activity for TXT records for a range of domains. Using a class weight algorithm originally designed for processing imbalanced medical data, and implemented in the scikit-learn machine learning Python library, the researchers were able to detect three quarters of the pending spam domains within moments, or even in advance of their operation. 'With a single request to the TXT record, we detect 75% of the spam domains, possibly before the start of the spam campaign.

Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an $\epsilon$-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our $\epsilon$-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or -- with probability $\epsilon$ -- from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the $\epsilon$-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance.

The study of a machine learning problem is in many ways is difficult to separate from the study of the loss function being used. One avenue of inquiry has been to look at these loss functions in terms of their properties as scoring rules via the proper-composite representation, in which predictions are mapped to probability distributions which are then scored via a scoring rule. However, recent research so far has primarily been concerned with analysing the (typically) finite-dimensional conditional risk problem on the output space, leaving aside the larger total risk minimisation. We generalise a number of these results to an infinite dimensional setting and in doing so we are able to exploit the familial resemblance of density and conditional density estimation to provide a simple characterisation of the canonical link.

Runoff voting rules such as single transferable vote (STV) and Baldwin's rule are of particular interest in computational social choice due to their recursive nature and hardness of manipulation, as well as in (human) practice because they are relatively easy to understand. However, they are not known for their compliance with desirable axiomatic properties, which we attempt to rectify here. We characterize runoff rules that are based on scoring rules using two axioms: a weakening of local independence of irrelevant alternatives and a variant of population-consistency. We then show, as our main technical result, that STV is the only runoff scoring rule satisfying an independence-of-clones property. Furthermore, we provide axiomatizations of Baldwin's rule and Coombs' rule.