ABSTRACT A substantial part of fighter pilot training is simulation-based and involves computer-generated forces controlled by predefined behavior models. The behavior models are typically manually created by eliciting knowledge from experienced pilots, which is a time-consuming process. Despite the work put in, the behavior models are often unsatisfactory due to their predictable nature and lack of adaptivity, forcing instructors to spend time manually monitoring and controlling them. Reinforcement and imitation learning pose as alternatives to handcrafted models. This paper presents the Learning Environment for the Air Domain (LEAD), a system for creating and integrating intelligent air combat behavior in military simulations. By incorporating the popular programming library and interface Gymnasium, LEAD allows users to apply readily available machine learning algorithms. Additionally, LEAD can communicate with third-party simulation software through distributed simulation protocols, which allows behavior models to be learned and employed using simulation systems of different fidelities. 1 INTRODUCTION A large part of the training fighter pilots undergo occurs in simulators under instructor supervision. In these simulators, the pilots practice tactics and operations by engaging in scenarios including friendly and hostile forces, often represented by computer-generated forces (CGFs), which are autonomous or semi-autonomous actors used in military simulation (Løvlid et al. 2017). These CGFs must behave in a way that accelerates training and builds the necessary competence of the pilots. Still, a current limitation to using CGFs for training is that their behaviors often come across as predictable, inviting pilots to exploit their vulnerabilities rather than focus on achieving the training objectives (Toubman 2020, ch. 1). Such constraints in the behavior models force instructors to micromanage the CGFs, restricting the complexity of scenarios that can be managed and trained (Källström et al. 2022). Besides, qualified instructors are both in short supply and on tight schedules, meaning they should devote their full attention to giving instructions and feedback to pilots. Modeling adaptive and intelligent air combat behavior for CGFs is thus a key challenge.

Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose search strategies, which often might not identify the best configurations or their time to convergence is a significant barrier. There is, thus, a need for a general purpose and efficient tuning approach that can be easily scaled and adapted to various tuning tasks. We propose a technique for tuning parallel code regions that is general enough to be adapted to multiple tasks. In this paper, we analyze IR-based programming models to make task-specific performance optimizations. To this end, we propose the Multimodal Graph Neural Network and Autoencoder (MGA) tuner, a multimodal deep learning based approach that adapts Heterogeneous Graph Neural Networks and Denoizing Autoencoders for modeling IR-based code representations that serve as separate modalities. This approach is used as part of our pipeline to model a syntax, semantics, and structure-aware IR-based code representation for tuning parallel code regions/kernels. We extensively experiment on OpenMP and OpenCL code regions/kernels obtained from PolyBench, Rodinia, STREAM, DataRaceBench, AMD SDK, NPB, NVIDIA SDK, Parboil, SHOC, and LULESH benchmarks. We apply our multimodal learning techniques to the tasks of i) optimizing the number of threads, scheduling policy and chunk size in OpenMP loops and, ii) identifying the best device for heterogeneous device mapping of OpenCL kernels. Our experiments show that this multimodal learning based approach outperforms the state-of-the-art in all experiments.

While there are abundant researches about evaluating ChatGPT on natural language understanding and generation tasks, few studies have investigated how ChatGPT's behavior changes over time. In this paper, we collect a coarse-to-fine temporal dataset called ChatLog, consisting of two parts that update monthly and daily: ChatLog-Monthly is a dataset of 38,730 question-answer pairs collected every month including questions from both the reasoning and classification tasks. ChatLog-Daily, on the other hand, consists of ChatGPT's responses to 1000 identical questions for long-form generation every day. We conduct comprehensive automatic and human evaluation to provide the evidence for the existence of ChatGPT evolving patterns. We further analyze the unchanged characteristics of ChatGPT over time by extracting its knowledge and linguistic features. We find some stable features to improve the robustness of a RoBERTa-based detector on new versions of ChatGPT. We will continuously maintain our project at https://github.com/THU-KEG/ChatLog.

Time is an important aspect of documents and is used in a range of Temporal signals constitute significant features in various types NLP and IR tasks. In this work, we investigate methods for incorporating of text documents such as news articles or biographies. They can temporal information during pre-training to further improve be leveraged to understand chronology, causalities, developments, the performance on time-related tasks. Compared with common and ramifications of events, being helpful in a range of different pre-trained language models like BERT which utilize synchronic NLP tasks. Utilizing temporal signals in information retrieval has received document collections (e.g., BookCorpus and Wikipedia) as the training considerable attention recently, too. For example, researchers corpora, we use long-span temporal news article collection for have addressed time-sensitive queries in search leading to the formation building word representations. We introduce BiTimeBERT, a novel of a subset of Information Retrieval called Temporal Information language representation model trained on a temporal collection Retrieval [8, 26] in which both query and document of news articles via two new pre-training tasks, which harnesses temporal aspects are of key concern. Event detection and ordering two distinct temporal signals to construct time-aware language [14, 47], timeline summarization [2, 10, 36, 46, 50], event occurrence representations. The experimental results show that BiTimeBERT time prediction [54], temporal clustering [9], question answering consistently outperforms BERT and other existing pre-trained models [39, 52] and semantic change detection [41, 42] are other example with substantial gains on different downstream NLP tasks and tasks where utilizing temporal information has proven beneficial.

Vehicle teleoperation has potential applications in fallback solutions for autonomous vehicles, remote delivery services, and hazardous operations. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these issues, we previously introduced the novel successive reference pose tracking (SRPT) approach, which transmits successive reference poses to the vehicle instead of steering commands. This paper compares the stability and performance of SRPT with Smith predictor-based approaches for direct vehicle teleoperation in challenging scenarios. The Smith predictor approach is further categorized, one with Lookahead driver and second with Stanley driver. Simulations are conducted in a Simulink environment, considering variable network delays and different vehicle speeds, and include maneuvers such as tight corners, slalom, low-adhesion roads, and strong crosswinds. The results show that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking. Our findings demonstrate the effectiveness of SRPT in eliminating the detrimental effect of network delays in vehicle teleoperation.

Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis. A prime example of such a dataset is a collection of cell cycle measurements, where the inherently cyclical nature of the process can be represented as a circle or sphere. Motivated by the need to analyze these types of datasets, we propose a nonlinear dimension reduction method, Spherical Rotation Component Analysis (SRCA), that incorporates geometric information to better approximate low-dimensional manifolds. SRCA is a versatile method designed to work in both high-dimensional and small sample size settings. By employing spheres or ellipsoids, SRCA provides a low-rank spherical representation of the data with general theoretic guarantees, effectively retaining the geometric structure of the dataset during dimensionality reduction. A comprehensive simulation study, along with a successful application to human cell cycle data, further highlights the advantages of SRCA compared to state-of-the-art alternatives, demonstrating its superior performance in approximating the manifold while preserving inherent geometric structures.

Zero-resource cross-lingual transfer approaches aim to apply supervised models from a source language to unlabelled target languages. In this paper we perform an in-depth study of the two main techniques employed so far for cross-lingual zero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection (data-based cross-lingual transfer) as an effective technique for cross-lingual sequence labelling, in this paper we experimentally demonstrate that high capacity multilingual language models applied in a zero-shot (model-based cross-lingual transfer) setting consistently outperform data-based cross-lingual transfer approaches. A detailed analysis of our results suggests that this might be due to important differences in language use. More specifically, machine translation often generates a textual signal which is different to what the models are exposed to when using gold standard data, which affects both the fine-tuning and evaluation processes. Our results also indicate that data-based cross-lingual transfer approaches remain a competitive option when high-capacity multilingual language models are not available.

The summarization of conversation, a case of discourse conversational summarization and conversational over a discourse, clearly illustrates a series AI more broadly. We illustrate the remaining challenges of pragmatic limitations in contemporary conversational in this area with ill-conceived examples inspired AI applications. While there has been some by conversational AI systems (Gratch et al., previous work examining pragmatic issues in conversational 2014), conversation summarization models, (Gaur AI (i.e., (Bao et al., 2022; Kim et al., et al., 2021) and author interactions with chatbots 2020, 2021a; Nath, 2020; Wu and Ong, 2021)), and voice assistants. Like Chomsky's star sentences, additional progress depends on understanding the these examples have clear pragmatic deficiencies source of limitations in current applications. We that trigger the Turing Test criterion. No aim to contribute to both theory and applications by competent speaker would construct such discourse.

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in timeseries forecasting problems: subgroup fairness and instantaneous fairness. These notion extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.

Palantir already sells its domestic surveillance services to US Immigration and Customs Enforcement, so it should come as no surprise that the company founded by billionaire Peter Thiel is working to make inroads into the Pentagon as well. On Tuesday, the company released a video demo of its latest offering, the Palantir Artificial Intelligence Platform (AIP). While the system itself is simply designed to integrate large language models (LLMs) like OpenAI's GPT-4 or Google's BERT into privately-operated networks, the very first thing they did was apply it to the modern battlefield. In the video demo above, a military operator tasked with monitoring the Eastern European theater discovers enemy forces massing near the border and responds by asking a ChatGPT-style digital assistant for help with deploying reconnaissance drones, ginning up tactical responses to the perceived aggression and even organize the jamming of the enemy's communications. The AIP is shown helping estimate the enemy's composition and capabilities by launching a Reaper drone on a reconnaissance mission in response the to operator's request for better pictures, and suggesting appropriate responses given the discovery of an armored element.