OpenRaiser/Intern-Atlas huggingface dataset:
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks
Venue: arXiv cs.CV 2024
Year: 2024

We present a novel unconditional video generative model designed to address long-term spatial and temporal dependencies, with attention to computational and dataset efficiency. To capture long spatio-temporal dependencies, our approach incorporates a hybrid explicit-implicit tri-plane representation inspired by 3D-aware generative frameworks developed for three-dimensional object representation and employs a single latent code to model an entire video clip. Individual video frames are then synthesized from an intermediate tri-plane representation, which itself is derived from the primary latent code. This novel strategy more than halves the computational complexity measured in FLOPs compared to the most efficient state-of-the-art methods. Consequently, our approach facilitates the efficient and temporally coherent generation of videos. Moreover, our joint frame modeling approach, in contrast to autoregressive methods, mitigates the generation of visual artifacts. We further enhance the model's capabilities by integrating an optical flow-based module within our Generative Adversarial Network (GAN) based generator architecture, thereby compensating for the constraints imposed by a smaller generator size. As a result, our model synthesizes high-fidelity video clips at a resolution of $256\times256$ pixels, with durations extending to more than $5$ seconds at a frame rate of 30 fps. The efficacy and versatility of our approach are empirically validated through qualitative and quantitative assessments across three different datasets comprising both synthetic and real video clips. We will make our training and inference code public.
Fast High Dynamic Range Radiance Fields for Dynamic Scenes
Venue: arXiv cs.CV 2024
Year: 2024

Neural Radiances Fields (NeRF) and their extensions have shown great success in representing 3D scenes and synthesizing novel-view images. However, most NeRF methods take in low-dynamic-range (LDR) images, which may lose details, especially with nonuniform illumination. Some previous NeRF methods attempt to introduce high-dynamic-range (HDR) techniques but mainly target static scenes. To extend HDR NeRF methods to wider applications, we propose a dynamic HDR NeRF framework, named HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures. A learnable exposure mapping function is constructed to obtain adaptive exposure values for each image. Based on the monotonically increasing prior, a camera response function is designed for stable learning. With the proposed model, high-quality novel-view images at any time point can be rendered with any desired exposure. We further construct a dataset containing multiple dynamic scenes captured with diverse exposures for evaluation. All the datasets and code are available at \url{https://guanjunwu.github.io/HDR-HexPlane/}.
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Venue: arXiv cs.AI 2024
Year: 2024

Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural knowledge. This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models (LLMs) for reasoning in temporal knowledge graphs, presenting an easily transferable pipeline. In terms of graph modality, we underscore the LLMs' prowess in discerning the structural information of pivotal nodes within the historical chain. As for the generation mode of the LLMs utilized for inference, we conduct an exhaustive exploration into the variances induced by a range of inherent factors in LLMs, with particular attention to the challenges in comprehending reverse logic. We adopt a parameter-efficient fine-tuning strategy to harmonize the LLMs with the task requirements, facilitating the learning of the key knowledge highlighted earlier. Comprehensive experiments are undertaken on several widely recognized datasets, revealing that our framework exceeds or parallels existing methods across numerous popular metrics. Additionally, we execute a substantial range of ablation experiments and draw comparisons with several advanced commercial LLMs, to investigate the crucial factors influencing LLMs' performance in structured temporal knowledge inference tasks.
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Venue: arXiv cs.AI 2024
Year: 2024

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.
Multimodal Optimization with k-Cluster Big Bang-Big Crunch Algorithm and Postprocessing Methods for Identification and Quantification of Optima
Venue: arXiv cs.NE 2024
Year: 2024

Multimodal optimization is often encountered in engineering problems, especially when different and alternative solutions are sought. Evolutionary algorithms can efficiently tackle multimodal optimization thanks to their features such as the concept of population, exploration/exploitation, and being suitable for parallel computation. This paper investigates whether a less-known optimizer, the Big Bang-Big Crunch (BBBC) algorithm, is suitable for multimodal optimization. We extended BBBC and propose k-BBBC, a clustering-based multi-modal optimizer. Additionally, we introduce two post-processing methods to (i) identify the local optima in a set of retrieved solutions (i.e., a population), and (ii) quantify the number of correctly retrieved optima against the expected ones (i.e., success rate). Our results show that k-BBBC performs well even with problems having a large number of optima (tested on $379$ optima) and high dimensionality (tested on $32$ decision variables), but it becomes computationally too expensive for problems with many local optima (i.e., in the CEC'2013 benchmark set). Compared to other multimodal optimization methods, it outperforms them in terms of accuracy (in both search and objective space) and success rate (number of correctly retrieved optima) when tested on basic multimodal functions, especially when elitism is applied; however, it requires knowing the number of optima of a problem, which makes its performance decrease when tested on niching competition test CEC'2013. Lastly, we validated our proposed post-processing methods by comparing their success rate to the actual one: results suggest that these methods can be used to evaluate the performance of a multimodal optimization algorithm by correctly identifying optima and providing an indication of success -- without the need to know where the optima are located in the search space.
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
Venue: arXiv cs.SE 2024
Year: 2024

Key Performance Indicators (KPIs) are essential time-series metrics for ensuring the reliability and stability of many software systems. They faithfully record runtime states to facilitate the understanding of anomalous system behaviors and provide informative clues for engineers to pinpoint the root causes. The unprecedented scale and complexity of modern software systems, however, make the volume of KPIs explode. Consequently, many traditional methods of KPI anomaly detection become impractical, which serves as a catalyst for the fast development of machine learning-based solutions in both academia and industry. However, there is currently a lack of rigorous comparison among these KPI anomaly detection methods, and re-implementation demands a non-trivial effort. Moreover, we observe that different works adopt independent evaluation processes with different metrics. Some of them may not fully reveal the capability of a model and some are creating an illusion of progress. To better understand the characteristics of different KPI anomaly detectors and address the evaluation issue, in this paper, we provide a comprehensive review and evaluation of twelve state-of-the-art methods, and propose a novel metric called salience. Particularly, the selected methods include five traditional machine learning-based methods and seven deep learning-based methods. These methods are evaluated with five multivariate KPI datasets that are publicly available. A unified toolkit with easy-to-use interfaces is also released. We report the benchmark results in terms of accuracy, salience, efficiency, and delay, which are of practical importance for industrial deployment. We believe our work can contribute as a basis for future academic research and industrial application.
AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks
Venue: arXiv cs.CY 2024
Year: 2024

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft.
Cyclic and Negacyclic Codes with Optimal and Best Known Minimum Distances
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we construct a new family of distance-optimal binary cyclic codes with the minimum distance $6$ and a new family of distance-optimal quaternary cyclic codes with the minimum distance $4$. We also construct several families of cyclic and negacyclic codes over ${\bf F}_2$, ${\bf F}_3$, ${\bf F}_4$, ${\bf F}_5$, ${\bf F}_7$ and ${\bf F}_9$ with good parameters $n,\,k,\,d$, such that the maximal possible minimum distance $d_{max}$ of a linear $[n, k]_q$ code is at most $d_{max} \leq d+8$. The first codes in these families have optimal or best known minimum distances. $145$ optimal or best known codes are constructed as cyclic codes, negacyclic codes, their shortening codes and punctured codes. All optimal or best known codes constructed in this paper are not equivalent to the presently best known codes. Several infinite families of negacyclic $[n,\frac{n+1}{2}, d]_q$ codes or $[n, \frac{n}{2}, d]_q$ codes, such that their minimum distances satisfy $d\approx O(\frac{n}{\log_q n})$, are also constructed. These are first several families of such negacyclic codes.
Sprout: Designing Expressivity for Robots Using Fiber-Embedded Actuator
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we explore how techniques from soft robotics can help create a new form of robot expression. We present Sprout, a soft expressive robot that conveys its internal states by changing its body shape. Sprout can extend, bend, twist, and expand using fiber-embedded actuators integrated into its construction. These deformations enable Sprout to express its internal states, for example, by expanding to express anger and bending its body sideways to express curiosity. Through two user studies, we investigated how users interpreted Sprout's expressions, their perceptions of Sprout, and their expectations from future iterations of Sprout's design. We argue that the use of soft actuators opens a novel design space for robot expressions to convey internal states, emotions, and intent.
SCARIF: Towards Carbon Modeling of Cloud Servers with Accelerators
Venue: arXiv cs.DC 2024
Year: 2024

Embodied carbon has been widely reported as a significant component in the full system lifecycle of various computing systems' green house gas emissions. Many efforts have been undertaken to quantify the elements that comprise this embodied carbon, from tools that evaluate semiconductor manufacturing to those that can quantify different elements of the computing system from commercial and academic sources. However, these tools cannot easily reproduce results reported by server vendors' product carbon reports and the accuracy can vary substantially due to various assumptions. Furthermore, attempts to determine green house gas contributions using bottom-up methodologies often do not agree with system-level studies and are hard to rectify. Nonetheless, given there is a need to consider all contributions to green house gas emissions in datacenters, we propose SCARIF, the Server Carbon including Accelerator Reporter with Intelligence-based Formulation tool. SCARIF has three main contributions: (1) We first collect reported carbon cost data from server vendors and design statistic models to predict the embodied carbon cost so that users can get the embodied carbon cost for their server configurations. (2) We provide embodied carbon cost if users configure servers with accelerators including GPUs, and FPGAs. (3) By using case studies, we show that certain design choices of data center management might flip by the insight and observation from using SCARIF. Thus, SCARIF provides an opportunity for large-scale datacenter and hyperscaler design. We release SCARIF as an open-source tool at https://github.com/arc-research-lab/SCARIF.
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
Venue: arXiv cs.AI 2024
Year: 2024

In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequential Greedy Algorithm (SGA) is efficient enough (linear time complexity) for large-scale production recommendation engines. It achieved a lift of $+6\%$ to $ +10\%$ offline Area Under the receiver operating characteristic Curve (AUC) which is mainly due to explicitly modeling mutual influences among items of a list, and leveraging the second pass ranking scores of multiple objectives. In addition, we have generalized the offline replay theory to multi-slot re-ranking scenarios, with trade-offs among multiple objectives. The offline replay results can be further improved by Pareto Optimality. Moreover, we've built a multi-slot re-ranking simulator based on OpenAI Gym integrated with the Ray framework. It can be easily configured for different assumptions to quickly benchmark both reinforcement learning and supervised learning algorithms.
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications
Venue: arXiv cs.NI 2024
Year: 2024

The emergence of the semantic-aware paradigm presents opportunities for innovative services, especially in the context of 6G-based applications. Although significant progress has been made in semantic extraction techniques, the incorporation of semantic information into resource allocation decision-making is still in its early stages, lacking consideration of the requirements and characteristics of future systems. In response, this paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the $α$-fairness metric, while accounting for user data correlation by introducing the concepts of self- and assisted throughputs. Initially, the problem is analyzed to identify its optimal solution. Subsequently, a Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed. This method is grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), enabling the user equipment to autonomously make decisions regarding wireless spectrum access based solely on their local individual observations. The efficiency of the proposed technique is evaluated through two scenarios: single-channel and multi-channel. The findings illustrate that, across a spectrum of $α$ values, association matrices, and channels, SAMA-D3QL consistently outperforms alternative approaches. This establishes it as a promising candidate for facilitating the realization of future federated, dynamically evolving applications.
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static population. In this paper, we study fairness in dynamic systems where sequential decisions are made. Each decision may shift the underlying distribution of features or user behavior. We model the dynamic system through a Markov Decision Process (MDP). By acknowledging that traditional fairness notions and long-term fairness are distinct requirements that may not necessarily align with one another, we propose an algorithmic framework to integrate various fairness considerations with reinforcement learning using both pre-processing and in-processing approaches. Three case studies show that our method can strike a balance between traditional fairness notions, long-term fairness, and utility.
TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights
Venue: arXiv cs.IR 2024
Year: 2024

A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics. This paper presents TRACE, a Time-Relational Approximate Cubing Engine that enables interactive analysis on such slices with a low upfront cost - both in space and computation. It does this by materializing the most important parts of the cube over time enabling interactive querying for a large class of analytical queries e.g. what part of my business has the highest revenue growth ([SubCategory=Sports Equipment, Gender=Female]), what slices are lagging in revenue per user ([State=CA, Age=20-30]). Many user defined metrics are supported including common aggregations such as SUM, COUNT, DISTINCT COUNT and more complex ones such as AVERAGE. We implemented and deployed TRACE for a variety of business use cases.
Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching
Venue: arXiv cs.NE 2024
Year: 2024

Attention-based Neural Networks (NN) have demonstrated their effectiveness in accurate memory access prediction, an essential step in data prefetching. However, the substantial computational overheads associated with these models result in high inference latency, limiting their feasibility as practical prefetchers. To close the gap, we propose a new approach based on tabularization that significantly reduces model complexity and inference latency without sacrificing prediction accuracy. Our novel tabularization methodology takes as input a distilled, yet highly accurate attention-based model for memory access prediction and efficiently converts its expensive matrix multiplications into a hierarchy of fast table lookups. As an exemplar of the above approach, we develop DART, a prefetcher comprised of a simple hierarchy of tables. With a modest 0.09 drop in F1-score, DART reduces 99.99% of arithmetic operations from the large attention-based model and 91.83% from the distilled model. DART accelerates the large model inference by 170x and the distilled model by 9.4x. DART has comparable latency and storage costs as state-of-the-art rule-based prefetcher BO but surpasses it by 6.1% in IPC improvement. DART outperforms state-of-the-art NN-based prefetchers TransFetch by 33.1% and Voyager by 37.2% in terms of IPC improvement, primarily due to its low prefetching latency.
Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud Gaming
Venue: arXiv cs.NI 2024
Year: 2024

Cloud gaming, wherein game graphics is rendered in the cloud and streamed back to the user as real-time video, expands the gaming market to billions of users who do not have gaming consoles or high-power graphics PCs. Companies like Nvidia, Amazon, Sony and Microsoft are investing in building cloud gaming platforms to tap this large unserved market. However, cloud gaming requires the user to have high bandwidth and stable network connectivity - whereas a typical console game needs about 100-200 kbps, a cloud game demands minimum 10-20 Mbps. This makes the Internet Service Provider (ISP) a key player in ensuring the end-user's good gaming experience. In this paper we develop a method to detect Nvidia's GeForce NOW cloud gaming sessions over their network infrastructure, and measure associated user experience. In particular, we envision ISPs taking advantage of our method to provision network capacity at the right time and in the right place to support growth in cloud gaming at the right experience level; as well as identify the role of contextual factors such as user setup (browser vs app) and connectivity type (wired vs wireless) in performance degradation. We first present a detailed anatomy of flow establishment and volumetric profiles of cloud gaming sessions over multiple platforms, followed by a method to detect gameplay and measure key experience aspects such as latency, frame rate and resolution via real-time analysis of network traffic. The insights and methods are also validated in the lab for XBox Cloud Gaming platform. We then implement and deploy our method in a campus network to capture gameplay behaviors and experience measures across various user setups and connectivity types which we believe are valuable for network operators.