OpenRaiser/Intern-Atlas huggingface dataset:
StreamOptix: A Cross-layer Adaptive Video Delivery Scheme
Venue: arXiv cs.MM 2024
Year: 2024

This paper presents a cross-layer video delivery scheme, StreamOptix, and proposes a joint optimization algorithm for video delivery that leverages the characteristics of the physical (PHY), medium access control (MAC), and application (APP) layers. Most existing methods for optimizing video transmission over different layers were developed individually. Realizing a cross-layer design has always been a significant challenge, mainly due to the complex interactions and mismatches in timescales between layers, as well as the presence of distinct objectives in different layers. To address these complications, we take a divide-and-conquer approach and break down the formulated cross-layer optimization problem for video delivery into three sub-problems. We then propose a three-stage closedloop optimization framework, which consists of 1) an adaptive bitrate (ABR) strategy based on the link capacity information from PHY, 2) a video-aware resource allocation scheme accounting for the APP bitrate constraint, and 3) a link adaptation technique utilizing the soft acknowledgment feedback (soft-ACK). The proposed framework also supports the collections of the distorted bitstreams transmitted across the link. This allows a more reasonable assessment of video quality compared to many existing ABR methods that simply neglect the distortions occurring in the PHY layer. Experiments conducted under various network settings demonstrate the effectiveness and superiority of the new cross-layer optimization strategy. A byproduct of this study is the development of more comprehensive performance metrics on video delivery, which lays down the foundation for extending our system to multimodal communications in the future. Code for reproducing the experimental results is available at https://github.com/Evan-sudo/StreamOptix.
Scaling Automatic Extraction of Pseudocode
Venue: arXiv cs.IR 2024
Year: 2024

Pseudocode in a scholarly paper provides a concise way to express the algorithms implemented therein. Pseudocode can also be thought of as an intermediary representation that helps bridge the gap between programming languages and natural languages. Having access to a large collection of pseudocode can provide various benefits ranging from enhancing algorithmic understanding, facilitating further algorithmic design, to empowering NLP or computer vision based models for tasks such as automated code generation and optical character recognition (OCR). We have created a large pseudocode collection by extracting nearly 320,000 pseudocode examples from arXiv papers. This process involved scanning over $2.2$ million scholarly papers, with 1,000 of them being manually inspected and labeled. Our approach encompasses an extraction mechanism tailored to optimize the coverage and a validation mechanism based on random sampling to check its accuracy and reliability, given the inherent heterogeneity of the collection. In addition, we offer insights into common pseudocode structures, supported by clustering and statistical analyses. Notably, these analyses indicate an exponential-like growth in the usage of pseudocodes, highlighting their increasing significance.
Large Language Model-guided Document Selection
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Model (LLM) pre-training exhausts an ever growing compute budget, yet recent research has demonstrated that careful document selection enables comparable model quality with only a fraction of the FLOPs. Inspired by efforts suggesting that domain-specific training document selection is in fact an interpretable process [Gunasekar et al., 2023], as well as research showing that instruction-finetuned LLMs are adept zero-shot data labelers [Gilardi et al.,2023], we explore a promising direction for scalable general-domain document selection; employing a prompted LLM as a document grader, we distill quality labels into a classifier model, which is applied at scale to a large, and already heavily-filtered, web-crawl-derived corpus autonomously. Following the guidance of this classifier, we drop 75% of the corpus and train LLMs on the remaining data. Results across multiple benchmarks show that: 1. Filtering allows us to quality-match a model trained on the full corpus across diverse benchmarks with at most 70% of the FLOPs, 2. More capable LLM labelers and classifier models lead to better results that are less sensitive to the labeler's prompt, 3. In-context learning helps to boost the performance of less-capable labeling models. In all cases we use open-source datasets, models, recipes, and evaluation frameworks, so that results can be reproduced by the community.
LLM-Vectorizer: LLM-based Verified Loop Vectorizer
Venue: arXiv cs.SE 2024
Year: 2024

Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.
Morescient GAI for Software Engineering (Extended Version)
Venue: arXiv cs.SE 2024
Year: 2024

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
From Worst to Average Case to Incremental Search Bounds of the Strong Lucas Test
Venue: arXiv cs.CR 2024
Year: 2024

The strong Lucas test is a widely used probabilistic primality test in cryptographic libraries. When combined with the Miller-Rabin primality test, it forms the Baillie-PSW primality test, known for its absence of false positives, undermining the relevance of a complete understanding of the strong Lucas test. In primality testing, the worst-case error probability serves as an upper bound on the likelihood of incorrectly identifying a composite as prime. For the strong Lucas test, this bound is $4/15$ for odd composites, not products of twin primes. On the other hand, the average-case error probability indicates the probability that a randomly chosen integer is inaccurately classified as prime by the test. This bound is especially important for practical applications, where we test primes that are randomly generated and not generated by an adversary. The error probability of $4/15$ does not directly carry over due to the scarcity of primes, and whether this estimate holds has not yet been established in the literature. This paper addresses this gap by demonstrating that an integer passing $t$ consecutive test rounds, alongside additional standard tests of low computational cost, is indeed prime with a probability greater than $1-(4/15)^t$ for all $t\geq 1$. Furthermore, we introduce error bounds for the incremental search algorithm based on the strong Lucas test, as there are no established bounds up to date as well. Rather than independent selection, in this approach, the candidate is chosen uniformly at random, with subsequent candidates determined by incrementally adding 2. This modification reduces the need for random bits and enhances the efficiency of trial division computation further.
Experiences from Integrating Large Language Model Chatbots into the Classroom
Venue: arXiv cs.CY 2024
Year: 2024

In the present study, we provided students an unfiltered access to a state-of-the-art large language model (LLM) chatbot. The chatbot was intentionally designed to mimic proprietary commercial chatbots such as ChatGPT where the chatbot has not been tailored for the educational context; the underlying engine was OpenAI GPT-4. The chatbot was integrated into online learning materials of three courses. One of the courses focused on software engineering with LLMs, while the two other courses were not directly related to LLMs. Our results suggest that only a minority of students engage with the chatbot in the courses that do not relate to LLMs. At the same time, unsurprisingly, nearly all students in the LLM-focused course leveraged the chatbot. In all courses, the majority of the LLM usage came from a few superusers, whereas the majority of the students did not heavily use the chatbot even though it was readily available and effectively provided a free access to the OpenAI GPT-4 model. We also observe that in addition to students using the chatbot for course-specific purposes, many use the chatbot for their own purposes. These results suggest that the worst fears of educators -- all students overrelying on LLMs -- did not materialize even when the chatbot access was unfiltered. We finally discuss potential reasons for the low usage, suggesting the need for more tailored and scaffolded LLM experiences targeted for specific types of student use cases.
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one item. This makes user behaviors more complex, and presents two challenges for Q&A recommendation, including: the collaborative information entanglement, which means user feedback is influenced by either the question or the answer; and the semantic information entanglement, where questions are correlated with their corresponding answers, and correlations also exist among different question-answer pairs. Traditional recommendation methods treat the question-answer pair as a whole or only consider the answer as a single item, which overlooks the two challenges and cannot effectively model user interests. To address these challenges, we introduce Question & Answer Graph Collaborative Filtering (QAGCF), a graph neural network model that creates separate graphs for collaborative and semantic views to disentangle the information in question-answer pairs. The collaborative view disentangles questions and answers to individually model collaborative information, while the semantic view captures the semantic information both within and between question-answer pairs. These views are further merged into a global graph to integrate the collaborative and semantic information. Polynomial-based graph filters are used to address the high heterophily issues of the global graph. Additionally, contrastive learning is utilized to obtain robust embeddings during training. Extensive experiments on industrial and public datasets demonstrate that QAGCF consistently outperforms baselines and achieves state-of-the-art results.
Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior
Venue: arXiv cs.CV 2024
Year: 2024

Video-to-video synthesis poses significant challenges in maintaining character consistency, smooth temporal transitions, and preserving visual quality during fast motion. While recent fully cross-frame self-attention mechanisms have improved character consistency across multiple frames, they come with high computational costs and often include redundant operations, especially for videos with higher frame rates. To address these inefficiencies, we propose an adaptive motion-guided cross-frame attention mechanism that selectively reduces redundant computations. This enables a greater number of cross-frame attentions over more frames within the same computational budget, thereby enhancing both video quality and temporal coherence. Our method leverages optical flow to focus on moving regions while sparsely attending to stationary areas, allowing for the joint editing of more frames without increasing computational demands. Traditional frame interpolation techniques struggle with motion blur and flickering in intermediate frames, which compromises visual fidelity. To mitigate this, we introduce KV-caching for jointly edited frames, reusing keys and values across intermediate frames to preserve visual quality and maintain temporal consistency throughout the video. With our adaptive cross-frame self-attention approach, we achieve a threefold increase in the number of keyframes processed compared to existing methods, all within the same computational budget as fully cross-frame attention baselines. This results in significant improvements in prediction accuracy and temporal consistency, outperforming state-of-the-art approaches. Code will be made publicly available at https://github.com/tanvir-utexas/AdaVE/tree/main
Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments
Venue: arXiv cs.LG 2024
Year: 2024

Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios and transitory regimes such as major temperature increases or decreases are de-facto excluded. Acquisition of such data requires significant energy consumption and a dedicated facility, hindering the quantity and diversity of available data. Cost related constraints however do not allow for continuous year-around acquisition. To address this, we investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models. This approach alleviates the need for continuously acquiring extensive time series data, especially in contexts involving repetitive heating and cooling cycles in buildings. In our evaluation 1) we assess the performance of synthetic data generators independently, particularly focusing on SoTA AI-based methods; 2) we measure the utility of incorporating synthetically augmented data in a subsequent forecasting tasks where we employ a simple model in two distinct scenarios: 1) we first examine an augmentation technique that combines real and synthetically generated data to expand the training dataset, 2) we delve into utilizing synthetic data to tackle dataset imbalances. Our results highlight the potential of synthetic data augmentation in enhancing forecasting accuracy while mitigating training variance. Through empirical experiments, we show significant improvements achievable by integrating synthetic data, thereby paving the way for more robust forecasting models in low-data regime.
Stabilizing Extreme Q-learning by Maclaurin Expansion
Venue: arXiv cs.LG 2024
Year: 2024

In offline reinforcement learning, in-sample learning methods have been widely used to prevent performance degradation caused by evaluating out-of-distribution actions from the dataset. Extreme Q-learning (XQL) employs a loss function based on the assumption that Bellman error follows a Gumbel distribution, enabling it to model the soft optimal value function in an in-sample manner. It has demonstrated strong performance in both offline and online reinforcement learning settings. However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. This approach involves adjusting the modeled value function between the value function under the behavior policy and the soft optimal value function, thus achieving a trade-off between stability and optimality depending on the order of expansion. It also enables adjustment of the error distribution assumption from a normal distribution to a Gumbel distribution. Our method significantly stabilizes learning in online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several offline RL tasks from D4RL.
A Modular Framework for Flexible Planning in Human-Robot Collaboration
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system's scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications.
SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have superior capability in learning graph data. Full-graph GNN training generally has high accuracy, however, it suffers from large peak memory usage and encounters the Out-of-Memory problem when handling large graphs. To address this memory problem, a popular solution is mini-batch GNN training. However, mini-batch GNN training increases the training variance and sacrifices the model accuracy. In this paper, we propose a new memory-efficient GNN training method using spanning subgraph, called SpanGNN. SpanGNN trains GNN models over a sequence of spanning subgraphs, which are constructed from empty structure. To overcome the excessive peak memory consumption problem, SpanGNN selects a set of edges from the original graph to incrementally update the spanning subgraph between every epoch. To ensure the model accuracy, we introduce two types of edge sampling strategies (i.e., variance-reduced and noise-reduced), and help SpanGNN select high-quality edges for the GNN learning. We conduct experiments with SpanGNN on widely used datasets, demonstrating SpanGNN's advantages in the model performance and low peak memory usage.
TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

The recently unprecedented advancements in Large Language Models (LLMs) have propelled the medical community by establishing advanced medical-domain models. However, due to the limited collection of medical datasets, there are only a few comprehensive benchmarks available to gauge progress in this area. In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. Specifically, our TCMD collects massive questions across diverse domains with their annotated medical subjects and thus supports us in comprehensively assessing the capability of LLMs in the TCM domain. Extensive evaluation of various general LLMs and medical-domain-specific LLMs is conducted. Moreover, we also analyze the robustness of current LLMs in solving TCM QA tasks by introducing randomness. The inconsistency of the experimental results also reveals the shortcomings of current LLMs in solving QA tasks. We also expect that our dataset can further facilitate the development of LLMs in the TCM area.
Semantic Segmentation on VSPW Dataset through Masked Video Consistency
Venue: arXiv cs.CV 2024
Year: 2024

Pixel-level Video Understanding requires effectively integrating three-dimensional data in both spatial and temporal dimensions to learn accurate and stable semantic information from continuous frames. However, existing advanced models on the VSPW dataset have not fully modeled spatiotemporal relationships. In this paper, we present our solution for the PVUW competition, where we introduce masked video consistency (MVC) based on existing models. MVC enforces the consistency between predictions of masked frames where random patches are withheld. The model needs to learn the segmentation results of the masked parts through the context of images and the relationship between preceding and succeeding frames of the video. Additionally, we employed test-time augmentation, model aggeregation and a multimodal model-based post-processing method. Our approach achieves 67.27% mIoU performance on the VSPW dataset, ranking 2nd place in the PVUW2024 challenge VSS track.
Online Frequency Scheduling by Learning Parallel Actions
Venue: arXiv cs.NI 2024
Year: 2024

Radio Resource Management is a challenging topic in future 6G networks where novel applications create strong competition among the users for the available resources. In this work we consider the frequency scheduling problem in a multi-user MIMO system. Frequency resources need to be assigned to a set of users while allowing for concurrent transmissions in the same sub-band. Traditional methods are insufficient to cope with all the involved constraints and uncertainties, whereas reinforcement learning can directly learn near-optimal solutions for such complex environments. However, the scheduling problem has an enormous action space accounting for all the combinations of users and sub-bands, so out-of-the-box algorithms cannot be used directly. In this work, we propose a scheduler based on action-branching over sub-bands, which is a deep Q-learning architecture with parallel decision capabilities. The sub-bands learn correlated but local decision policies and altogether they optimize a global reward. To improve the scaling of the architecture with the number of sub-bands, we propose variations (Unibranch, Graph Neural Network-based) that reduce the number of parameters to learn. The parallel decision making of the proposed architecture allows to meet short inference time requirements in real systems. Furthermore, the deep Q-learning approach permits online fine-tuning after deployment to bridge the sim-to-real gap. The proposed architectures are evaluated against relevant baselines from the literature showing competitive performance and possibilities of online adaptation to evolving environments.