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We introduce a two-player game played on undirected graphs called Trail Trap, which is a variant of a game known as Partizan Edge Geography. One player starts by choosing any edge and moving a token from one endpoint to the other; the other player then chooses a different edge and does the same. Alternating turns, each player moves their token along an unused edge from its current vertex to an adjacent vertex, until one player cannot move and loses. We present an algorithm to determine which player has a winning strategy when the graph is a tree, and partially characterize the trees on which a given player wins. Additionally, we show that Trail Trap is NP-hard, even for connected bipartite planar graphs with maximum degree $4$ as well as for disconnected graphs. We determine which player has a winning strategy for certain subclasses of complete bipartite graphs and grid graphs, and we propose several open problems for further study.

6G Open Radio Access Networks (ORAN) promises to open data interfaces to enable plug-and-play service Apps, many of which are consumer and business-facing. Opening up 6G access lowers the barrier to innovation but raises the challenge that the required communication specifications are not fully known to all service designers. As such, business innovators must either be familiar with 6G standards or consult with experts. Enabling consistent, unbiased, rapid, and low-cost requirement assessment and specification generation is crucial to the ORAN innovation ecosystem. Here, we discuss our initiative to bridge service specification generation gaps between network service providers and business innovators. We first review the state-of-the-art and motivation in 6G plug-and-play services and capabilities, potential use cases, and relevant advances in Large Language Models (LLMs). We identify an ample innovation space for hybrid use cases that may require diverse and variational wireless functionalities across its operating time. We show that the network specification can be automated and present the first automatic retrieval-augmented specification generation (RAG) framework for 6G use cases. To enable public acceptance and feedback, a website interface is also published for the research and industrial community to experiment with the RAG framework. We hope this review highlights the need and the emerging foundation models that advance this area and motivate researchers to engage with the framework.

Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation framework using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios.

This paper investigates the application of Quantum Support Vector Machines (QSVMs) with an emphasis on the computational advancements enabled by NVIDIA's cuQuantum SDK, especially leveraging the cuTensorNet library. We present a simulation workflow that substantially diminishes computational overhead, as evidenced by our experiments, from exponential to quadratic cost. While state vector simulations become infeasible for qubit counts over 50, our evaluation demonstrates that cuTensorNet speeds up simulations to be completed within seconds on the NVIDIA A100 GPU, even for qubit counts approaching 784. By employing multi-GPU processing with Message Passing Interface (MPI), we document a marked decrease in computation times, effectively demonstrating the strong linear speedup of our approach for increasing data sizes. This enables QSVMs to operate efficiently on High-Performance Computing (HPC) systems, thereby opening a new window for researchers to explore complex quantum algorithms that have not yet been investigated. In accuracy assessments, our QSVM achieves up to 95\% on challenging classifications within the MNIST dataset for training sets larger than 100 instances, surpassing the capabilities of classical SVMs. These advancements position cuTensorNet within the cuQuantum SDK as a pivotal tool for scaling quantum machine learning simulations and potentially signpost the seamless integration of such computational strategies as pivotal within the Quantum-HPC ecosystem.

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at //gapszju.github.io/X-SLAM.

We consider modal logic extended with the well-known temporal operator `eventually' and provide a cut-elimination procedure for a cyclic sequent calculus that captures this fragment. The work showcases an adaptation of the reductive cut-elimination method to cyclic calculi. Notably, the proposed algorithm applies to a cyclic proof and directly outputs a cyclic cut-free proof without appealing to intermediate machinery for regularising the end proof.

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets.

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is trained to minimize perplexity, an automatic metric that we compare against human judgement of multi-turn conversation quality. To capture this judgement, we propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of good conversation. Interestingly, our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher than the next highest scoring chatbot that we evaluated.

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.