Scratch-pad memory (SPM) has been widely used in embedded systems because it allows software-controlled data placement. By designing data placement strategies, optimal solutions with minimal memory access latency for loops on SPM-DRAM architecture can be explored. Although existing works effectively reduce the latency by using fine-grained data placement methods, they fail in solving the case of inconsecutive array access. Meanwhile, fine-grained strategy can lead to excessive memory activation overhead, making it less efficient. Therefore, in this paper, we first propose a fine-grained dynamic programming algorithm, called FiDP, to tackle unsolved case and minimize latency. In order to mitigate the frequent activation before data access, we then add a medium-grained scheme to our strategy. It can achieve a better solution than FiDP by strictly formulating an integer linear programming (ILP) problem and considering multiple granularities, which is called MuILP. Furthermore, to compensate for the high time complexity of ILP, we develop a heuristic multi-granularity data placement algorithm, called HMuDP, which achieves a near-optimal solution with lower complexity. Experimental results show that our FiDP reduces the total latency by 75.90%, 47.70% and 12.34% compared with LRU-cache, a greedy-based comparison method (called Uday) and a dynamic programming-based comparison method (called DLAA). Besides, our MuILP and HMuDP yield less latency than FiDP with 45.10% and 43.14% average improvement, respectively.
Container-based virtualization is increasingly popular in cloud computing due to its efficiency and flexibility. Isolation is a fundamental property of containers and weak isolation could cause significant performance degradation and security vulnerability. However, existing works have almost not discussed the isolation problems of system log which is critical for monitoring and maintenance of containerized applications. In this paper, we present a detailed isolation analysis of system log in current container environment. First, we find several system log isolation problems which can cause significant impacts on system usability, security, and efficiency. For example, system log accidentally exposes information of host and co-resident containers to one container, causing information leakage. Second, we reveal that the root cause of these isolation problems is that containers share the global log configuration, the same log storage, and the global log view. To address these problems, we design and implement a system named private logs (POGs). POGs provides each container with its own log configuration and stores logs individually for each container, avoiding log configuration and storage sharing, respectively. In addition, POGs enables private log view to help distinguish which container the logs belong to. The experimental results show that POGs can effectively enhance system log isolation for containers with negligible performance overhead.
Tree models have made an impressive progress during the past years, while an important problem is to understand how these models predict, in particular for critical applications such as finance and medicine. For this issue, most previous works measured the importance of individual features. In this work, we consider the interpretation of feature groups, which is more effective to capture intrinsic structures and correlations of multiple features. We propose the Baseline Group Shapley value (short for BGShapvalue) to calculate the importance of a feature group for tree models. We further develop a polynomial algorithm, BGShapTree, to deal with the sum of exponential terms in the BGShapvalue. The basic idea is to decompose the BGShapvalue into leaves’ weights and exploit the relationships between features and leaves. Based on this idea, we could greedily search salient feature groups with large BGShapvalues. Extensive experiments have validated the effectiveness of our approach, in comparison with state-of-the-art methods on the interpretation of tree models.
Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as
Synthetic binding proteins (SBPs) with small size, marked solubility and stability, and high affinity are important for protein-based research, treatment, and diagnostics. Over the last several decades, site-directed mutagenesis and directed evolution of privileged protein scaffold make up the great majority of SBPs. The groundbreaking advancement of deep learning (DL) in recent years has revolutionized the problem of protein structure prediction and design. Here, for the first time, the cutting-edge DL framework ProteinMPNN was applied to fulfill the de novo design of 7,245 new synthetic proteins covering 55 different scaffolds based on the original SBPs collected in our SYNBIP database. Comprehensive bioinformatics analysis indicated that, in addition to the excellent performance of sequence recovery, the designed synthetic proteins have a significant improvement in solubility and thermal stability compared to the currently known SBPs. Meanwhile, 8 incredibly suitable protein scaffolds for ProteinMPNN have been identified, from which the designed synthetic proteins calculate displayed good performance on binding ability to their corresponding protein targets. Therefore, the DL-based framework shown great potential in target-directed de novo generation of synthetic protein library with high quality, which could assist experimental biologists to rational protein engineering to discover novel functional protein binders.
Drug side effects have become paramount concerns in drug safety research, ranking as the fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious diseases. Simultaneously, the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions (DDIs). Traditionally, assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments. However, recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data. Based on this foundation, researchers have developed diverse machine learning methods for discovering and detecting drug side effects. This paper provides a comprehensive overview of recent advancements in predicting drug side effects, encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models. The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction. Subsequently, The study delves into machine learning methods customized for binary, multi-class, and multi-label classification tasks associated with drug side effects. These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs. Finally, the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.