Region-based memory management for Mercury programs

Region-based memory management (RBMM) is a form of compile time memory management, well-known from the functional programming world. In this paper we describe our work on implementing RBMM for the logic programming language Mercury. One interesting point about Mercury is that it is designed with strong type, mode, and determinism systems. These systems not only provide Mercury programmers with several direct software engineering benefits, such as self-documenting code and clear program logic, but also give language implementors a large amount of information that is useful for program analyses. In this work, we make use of this information to develop program analyses that determine the distribution of data into regions and transform Mercury programs by inserting into them the necessary region operations. We prove the correctness of our program analyses and transformation. To execute the annotated programs, we have implemented runtime support that tackles the two main challenges posed by backtracking. First, backtracking can require regions removed during forward execution to be "resurrected"; and second, any memory allocated during a computation that has been backtracked over must be recovered promptly and without waiting for the regions involved to come to the end of their life. We describe in detail our solution of both these problems. We study in detail how our RBMM system performs on a selection of benchmark programs, including some well-known difficult cases for RBMM. Even with these difficult cases, our RBMM-enabled Mercury system obtains clearly faster runtimes for 15 out of 18 benchmarks compared to the base Mercury system with its Boehm runtime garbage collector, with an average runtime speedup of 24%, and an average reduction in memory requirements of 95%. In fact, our system achieves optimal memory consumption in some programs.Comment: 74 pages, 23 figures, 11 tables. A shorter version of this paper, without proofs, is to appear in the journal Theory and Practice of Logic Programming (TPLP

Thermoresistance of p-Type 4H–SiC Integrated MEMS Devices for High-Temperature Sensing

There is an increasing demand for the development and integration of multifunctional sensing modules into power electronic devices that can operate in high temperature environments. Here, the authors demonstrate the tunable thermoresistance of p‐type 4H–SiC for a wide temperature range from the room temperature to above 800 K with integrated flow sensing functionality into a single power electronic chip. The electrical resistance of p‐type 4H–SiC is found to exponentially decrease with increasing temperature to a threshold temperature of 536 K. The temperature coefficient of resistance (TCR) shows a large and negative value from −2100 to −7600 ppm K−1, corresponding to a thermal index of 625 K. From the threshold temperature of 536–846 K, the electrical resistance shows excellent linearity with a positive TCR value of 900 ppm K−1. The authors successfully demonstrate the integration of p–4H–SiC flow sensing functionality with a high sensitivity of 1.035 μA(m s−1)−0.5 mW−1. These insights in the electrical transport of p–4H–SiC aid to improve the performance of p–4H–SiC integrated temperature and flow sensing systems, as well as the design consideration and integration of thermal sensors into 4H–SiC power electronic systems operating at high temperatures of up to 846 K

Invited review. Bond dissociation enthalpies in benzene derivatives and effect of substituents: an overview of density functional theory (B3LYP) based computational approach

In this review, we have mainly focused on the recent computational studies on the bond dissociation enthalpies (BDE) of the X‒H bonds of the para and meta substituted benzene derivatives (3Y-C6H4X‒H and 4Y-C6H4X‒H with X = O, S, Se, NH, PH, CH2, SiH2 and Y = H, F, Cl, CH3, OCH3, NH2, CF3, CN, NO2). In addition, the remote substituent effects on the BDE(X‒H), the radical stability and parent one have also been discussed in terms of the calculated ground state effect, radical effect and total effect. Model chemistry of ROB3LYP/6-311++G(d,p)//B3LYP/6-311G(d,p) can reproduce the BDE values with the accuracy of 1.0‒2.0 kcal/mol. The good linear correlations between Hammett constants and BDE values were discovered for both para and meta substitutions in phenols, thiophenols, benzeneselenols, anilines and phenylposphines with the R-squared lager than 0.94. In contrast, it does not occur in case of toluenes and phenylsilanes.Keywords. Benzene derivatives, density functional theory, bond dissociation enthalpies, substituent effects, radical effect, ground state effect, total effect, Hammett constants

Electrochemical probing of selective haemoglobin binding in hydrogel-based molecularly imprinted polymers

An electrochemical method has been developed for the probing of hydrogel-based molecularly imprinted polymers (HydroMIPs) on the surface of a glassy carbon electrode. HydroMIPs designed for bovine haemoglobin selectivity were electrochemically characterised and their rebinding properties were monitored using cyclic voltammetry. The electrochemical reduction of bovine oxyhaemoglobin (BHb) in solution was observed to occur at ?0.460 V vs (Ag/AgCl) in 150 mM phosphate buffer solution (PBS). When the protein was selectively bound to the MIP, the electrochemical reduction of oxyhaemoglobin could be observed at a similar peak potential of ?0.480 V vs (Ag/AgCl). When analysing the non-imprinted control polymer (NIP) interfaced at the electrode, which contained no protein, the peak reduction potential corresponded to that observed for dissolved oxygen in solution (?0.65 V vs (Ag/AgCl)). MIP and NIP (in the absence of protein) were interfaced at the electrode and protein allowed to diffuse through the polymers from the bulk solution end to the electrode. It was observed that whereas NIP exhibited a protein response within 10 min of protein exposure, up to 45 min of exposure time was required in the case of the MIP before a protein response could be obtained. Our results suggest that due to the selective nature of the MIP, BHb arrival at the electrode via diffusion is delayed by the MIP due to attractive selective interactions with exposed cavities, but not the NIP which is devoid of selective cavities

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

Metric Learning for Automatic Sleep Stage Classification

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step

Metric Learning for Automatic Sleep Stage Classification

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step