A key-based adaptive transactional memory executor

Software transactional memory systems enable a programmer to easily write concurrent data structures such as lists, trees, hashtables, and graphs, where nonconflicting operations proceed in parallel. Many of these structures take the abstract form of a dictionary, in which each transaction is associated with a search key. By regrouping transactions based on their keys, one may improve locality and reduce conflicts among parallel transactions. In this paper, we present an executor that partitions transactions among available processors. Our keybased adaptive partitioning monitors incoming transactions, estimates the probability distribution of their keys, and adaptively determines the (usually nonuniform) partitions. By comparing the adaptive partitioning with uniform partitioning and round-robin keyless partitioning on a 16-processor SunFire 6800 machine, we demonstrate that key-based adaptive partitioning significantly improves the throughput of finegrained parallel operations on concurrent data structures

The splicing of backscattered scanning electron microscopy method used on evaluation of microscopic pore characteristics in shale sample and compared with results from other methods

The splicing of backscattered scanning electron microscopy (SB-SEM) method was applied to evaluate the microscopic pore characteristics of the Lower Silurian Longmaxi Shale samples from Py1 well in Southeast Chongqing, China. The results from SB-SEM, including frequencies, volumes and specific surface areas of organic and inorganic pores with different sizes, were compared with those of low temperature nitrogen adsorption/desorption (LTNA) and mercury intrusion porosimetry (MIP). The results show that the changes in organic and inorganic surface porosity with increasing image area estimated from the SB-SEM method become almost stable when the SB-SEM image areas are larger than 0.4 mm, which indicates that the heterogeneities of organic and inorganic pore volumes in shale samples can be largely overcome. This method is suitable for evaluating the microscopic pore characteristics of shale samples. Although the SB-SEM underestimates the frequencies, volumes and specific surface areas of pores smaller than its resolution, it can obtain these characteristics of pores larger than 100 nm in width, which are not effectively evaluated by the LTNA method and are underestimated by the MIP method

Call Sequence Prediction through Probabilistic Calling Automata

Predicting a sequence of upcoming function calls is important for optimizing programs written in modern managed languages (e.g., Java, Javascript, C#.) Existing function call predictions are mainly built on statistical patterns, suitable for predicting a single call but not a sequence of calls. This paper presents a new way to enable call sequence prediction, which exploits program structures through Probabilistic Calling Automata (PCA), a new program representation that captures both the inherent ensuing relations among function calls, and the probabilistic nature of execution paths. It shows that PCA-based prediction outperforms existing predictions, yielding substantial speedup when being applied to guide Just-In-Time compilation. By enabling accurate, efficient call sequence prediction for the first time, PCA-based predictors open up many new opportunities for dynamic program optimizations

Scaling Laws for Fact Memorization of Large Language Models

Fact knowledge memorization is crucial for Large Language Models (LLM) to generate factual and reliable responses. However, the behaviors of LLM fact memorization remain under-explored. In this paper, we analyze the scaling laws for LLM's fact knowledge and LLMs' behaviors of memorizing different types of facts. We find that LLMs' fact knowledge capacity has a linear and negative exponential law relationship with model size and training epochs, respectively. Estimated by the built scaling law, memorizing the whole Wikidata's facts requires training an LLM with 1000B non-embed parameters for 100 epochs, suggesting that using LLMs to memorize all public facts is almost implausible for a general pre-training setting. Meanwhile, we find that LLMs can generalize on unseen fact knowledge and its scaling law is similar to general pre-training. Additionally, we analyze the compatibility and preference of LLMs' fact memorization. For compatibility, we find LLMs struggle with memorizing redundant facts in a unified way. Only when correlated facts have the same direction and structure, the LLM can compatibly memorize them. This shows the inefficiency of LLM memorization for redundant facts. For preference, the LLM pays more attention to memorizing more frequent and difficult facts, and the subsequent facts can overwrite prior facts' memorization, which significantly hinders low-frequency facts memorization. Our findings reveal the capacity and characteristics of LLMs' fact knowledge learning, which provide directions for LLMs' fact knowledge augmentation

Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models

Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach fails in scenarios where the correct answers are in the minority. We identify this as a primary factor constraining the reasoning capabilities of LLMs, a limitation that cannot be resolved solely based on the predicted answers. To address this shortcoming, we introduce a hierarchical reasoning aggregation framework AoR (Aggregation of Reasoning), which selects answers based on the evaluation of reasoning chains. Additionally, AoR incorporates dynamic sampling, adjusting the number of reasoning chains in accordance with the complexity of the task. Experimental results on a series of complex reasoning tasks show that AoR outperforms prominent ensemble methods. Further analysis reveals that AoR not only adapts various LLMs but also achieves a superior performance ceiling when compared to current methods.Comment: 17 pages, 14 figures, accepted by LREC-COLING 202

Liquid air energy storage technology:a comprehensive review of research, development and deployment

Abstract Liquid air energy storage (LAES) uses air as both the storage medium and working fluid, and it falls into the broad category of thermo-mechanical energy storage technologies. The LAES technology offers several advantages including high energy density and scalability, cost-competitiveness and non-geographical constraints, and hence has attracted a growing interest in recent years. As a result, several reviews have been published on the topic. However, these reviews covered little in the following aspects of LAES: dynamic simulation and optimisation, key components for LAES, LAES applications through integration, and unified economic and cost models for LAES. This article provides a comprehensive review on the LAES technology and fills the above gaps. Apart from applications in electrical grids such as peak-shaving, load shifting, and dealing with intermittency of renewable generation, the review also shows a diverse range of other LAES applications through integration, including waste heat and cold energy recovery and utilisation, multi-energy vector service provision, and sector coupling for chemical production and carbon capture. The review also leads to the recommendation of several areas for future research and development, including dynamic characteristics of whole LAES system integrated with renewables and end users; thermo-economic and dynamic optimization of stand-alone LAES and integrated systems; and experimental study on commercial systems.</jats:p

AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) are considered a promising foundation to build such agents due to their generalized capabilities. Current approaches either have LLM-based agents imitate expert-provided trajectories step-by-step, requiring human supervision, which is hard to scale and limits environmental exploration; or they let agents explore and learn in isolated environments, resulting in specialist agents with limited generalization. In this paper, we take the first step towards building generally-capable LLM-based agents with self-evolution ability. We identify a trinity of ingredients: 1) diverse environments for agent exploration and learning, 2) a trajectory set to equip agents with basic capabilities and prior knowledge, and 3) an effective and scalable evolution method. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration. AgentGym also includes a database with expanded instructions, a benchmark suite, and high-quality trajectories across environments. Next, we propose a novel method, AgentEvol, to investigate the potential of agent self-evolution beyond previously seen data across tasks and environments. Experimental results show that the evolved agents can achieve results comparable to SOTA models. We release the AgentGym suite, including the platform, dataset, benchmark, checkpoints, and algorithm implementations. The AgentGym suite is available on https://github.com/WooooDyy/AgentGym.Comment: Project site: https://agentgym.github.i