Floorplan-guided placement for large-scale mixed-size designs

In the nanometer scale era, placement has become an extremely challenging stage in modern Very-Large-Scale Integration (VLSI) designs. Millions of objects need to be placed legally within a chip region, while both the interconnection and object distribution have to be optimized simultaneously. Due to the extensive use of Intellectual Property (IP) and embedded memory blocks, a design usually contains tens or even hundreds of big macros. A design with big movable macros and numerous standard cells is known as mixed-size design. Due to the big size difference between big macros and standard cells, the placement of mixed-size designs is much more difficult than the standard-cell placement. This work presents an efficient and high-quality placement tool to handle modern large-scale mixed-size designs. This tool is developed based on a new placement algorithm flow. The main idea is to use the fixed-outline floorplanning algorithm to guide the state-of-the-art analytical placer. This new flow consists of four steps: 1) The objects in the original netlist are clustered into blocks; 2) Floorplanning is performed on the blocks; 3) The blocks are shifted within the chip region to further optimize the wirelength; 4) With big macro locations fixed, incremental placement is applied to place the remaining objects. Several key techniques are proposed to be used in the first two steps. These techniques are mainly focused on the following two aspects: 1) Hypergraph clustering algorithm that can cut down the original problem size without loss of placement Quality of Results (QoR); 2) Fixed-outline floorplanning algorithm that can provide a good guidance to the analytical placer at the global level. The effectiveness of each key technique is demonstrated by promising experimental results compared with the state-of-the-art algorithms. Moreover, using the industrial mixed-size designs, the new placement tool shows better performance than other existing approaches

Modified ground calcium carbonate mineral powder using in asphalt concrete: modification mechanism characterization at macro and micro levels

The study investigated the modification mechanism of modified ground calcium carbonate (GCC) mineral powder using in asphalt concrete. Two types of Titanate coupling agents, namely, K38S (TCA-K38S) and 201 (TCA-201), as well as sodium stearate coupling agent, were adopted to prepare modified GCC. The optimized preparation process was obtained through the orthogonal test. Two kinds of modified GCC were preferably selected to prepare asphalt concrete according to modification mechanism characterization, their performance was analyzed and evaluated at macro and micro levels. The study results show that, the optimal scheme of sodium stearate modified GCC is modification temperature of 80°C, modification time of 50 min, modifying agent dosage of 2.0%. The crystal structure of GCC remains unchanged after modification, with the original lattice structure being maintained. TCA-201 and sodium stearate exhibit better coating properties than that of TCA-K38S. The contact angles of TCA-201 and sodium stearate modified GCC are larger than that of TCA-K38S modified GCC. The in-service performance of AC-13C asphalt concrete modified with sodium stearate is found to be superior to that of TCA-201 modified AC-13C asphalt concrete. Compared with the unmodified AC-13C asphalt concrete, the Marshall modulus, residual stability, freeze-thaw splitting strength ratio, and maximum flexural tensile strain of sodium stearate modified AC-13C asphalt concrete are increased by 54.55%, 2.73%, 10.47%, and 26.41% respectively. This paper provides theoretical guidance for the application of GCC mineral powder in asphalt concrete

Long-tail Cross Modal Hashing

Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-modal data. Furthermore, CMH methods mostly mine the commonality of multi-modal data to learn hash codes, which may override tail labels encoded by the individuality of respective modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities. Then it dynamically combines the individuality and commonality with direct features extracted from respective modalities to create meta features that enrich the representation of tail labels, and binaries meta features to generate hash codes. LtCMH significantly outperforms state-of-the-art baselines on long-tail datasets and holds a better (or comparable) performance on datasets with balanced labels.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence(AAAI2023

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.Comment: 23 pages, 16 figures, to appear in the 45th IEEE Symposium on Security and Privacy (S&P'24

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Research on the Evaluation System of Scientific Research Ethics Based on AHP-Fuzzy Comprehensive Evaluation

Scientific research ethics is the value concept and code of conduct to be followed in scientific research, technological development and other scientific and technological activities. However, in recent years, some researchers have ignored ethical constraints in scientific research activities, resulting in more prominent ethical issues in scientific research. This study takes China Southern Power Grid Corporation as an example, based on ISO9000 standards, uses AHP-fuzzy comprehensive evaluation theory to establish a scientific research ethics evaluation system, aiming at improving the timeliness and targeting of scientific research ethics management

Genetic Regulation of N6-Methyladenosine-RNA in Mammalian Gametogenesis and Embryonic Development

Emerging evidence shows that m(6)A is the most abundant modification in eukaryotic RNA molecules. It has only recently been found that this epigenetic modification plays an important role in many physiological and pathological processes, such as cell fate commitment, immune response, obesity, tumorigenesis, and relevant for the present review, gametogenesis. Notably the RNA metabolism process mediated by m(6)A is controlled and regulated by a series of proteins termed writers, readers and erasers that are highly expressed in germ cells and somatic cells of gonads. Here, we review and discuss the expression and the functional emerging roles of m(6)A in gametogenesis and early embryogenesis of mammals. Besides updated references about such new topics, readers might find in the present work inspiration and clues to elucidate epigenetic molecular mechanisms of reproductive dysfunction and perspectives for future research