CARBON: A Counterfactual Reasoning based Framework for Neural Code Comprehension Debiasing

Previous studies have demonstrated that code intelligence models are sensitive to program transformation among which identifier renaming is particularly easy to apply and effective. By simply renaming one identifier in source code, the models would output completely different results. The prior research generally mitigates the problem by generating more training samples. Such an approach is less than ideal since its effectiveness depends on the quantity and quality of the generated samples. Different from these studies, we are devoted to adjusting models for explicitly distinguishing the influence of identifier names on the results, called naming bias in this paper, and thereby making the models robust to identifier renaming. Specifically, we formulate the naming bias with a structural causal model (SCM), and propose a counterfactual reasoning based framework named CARBON for eliminating the naming bias in neural code comprehension. CARBON explicitly captures the naming bias through multi-task learning in the training stage, and reduces the bias by counterfactual inference in the inference stage. We evaluate CARBON on three neural code comprehension tasks, including function naming, defect detection and code classification. Experiment results show that CARBON achieves relatively better performance (e.g., +0.5% on the function naming task at F1 score) than the baseline models on the original benchmark datasets, and significantly improvement (e.g., +37.9% on the function naming task at F1 score) on the datasets with identifiers renamed. The proposed framework provides a causal view for improving the robustness of code intelligence models

The effects of apoptosis vulnerability markers on the myocardium in depression after myocardial infarction

Background : There is an increased incidence of major depressive disorder (MDD) in individuals after myocardial infarction (MI), but the pathophysiological processes mediating this association are unclear. Our previous study demonstrated an increase in pro-apoptotic pathways in the myocardium and hippocampus in MDD, which was reversed by venlafaxine. This study aimed to attempt to confirm the effects of apoptosis vulnerability markers on the myocardium in a model of depression after myocardial infarction. Methods : Rats were divided into four groups: sham (N = 8), depression (N = 8, chronic mild unpredictable stress and separation were used in the depression group), MI (N = 13) and post-MI depression (N = 7). The rats in all four groups underwent the same open field and sucrose preference behavioral tests. Evan Blue staining was used to determine the area at risk of myocardial infarction in the left ventricle, and 2,3,5-triphenyl tetrazolium chloride (1.5% TTC) dye was used to detect the size of the myocardial infarction. The expression of bax and bcl-2 protein in the myocardium was investigated by immunohistochemistry, and the mRNA expression of bax, bcl-2 and caspase-3 in the myocardium was investigated by real time RT-PCR. Apoptosis was estimated in the myocardium by measuring the Bax:Bcl-2 ratio. Results : In the depression and post-MI depression rats, there were significantly decreased movements and total sucrose consumption, modeling behavioral deficits and an anhedonic-like state. In terms of myocardial infarction size, no difference was seen between the MI and post-MI depression groups. There was an up-regulated Bax:Bcl-2 ratio in the depression, MI and post-MI depression groups. Furthermore, in the latter group, there was a greater up-regulated Bax:Bcl-2 ratio. However, caspase-3 did not differ among the four groups. Conclusions : These results of this animal model suggest that active pro-apoptotic pathways may be involved in the nexus between myocardial infarction and depression. This mechanism may be germane to understanding this relationship in humans

What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectivelyComment: This paper is accepted by ASE 202

Detection-based active defense of biased injection attack based on robust adaptive controller

As the promising technology, the cooperative cyber-physical system can enhance the operating efficiency and reliability of smart grids. Meanwhile, the characteristics that deep integration of cyber-physical system can make smart grid face new security problems caused by false data injection attack. To maintain a safe and stable operation of smart grids, timely detection and defense of the emerging false data injection attacks, such as biased injection attack, is crucial. For this reason, this paper aims at developing a detection-based active defense mechanisms against biased injection attacks via robust adaptive controller. Through the established physical dynamic power model, an improved adaptive observer-based detection algorithm is proposed. Through the design of observer parameters, the proposed adaptive observer can enhance the accuracy of estimation state. In contrast to well-known attack detection methods for smart grids, the performance of attack detection under the developed detection algorithm can be effectively improved, such as the accuracy of state estimation and false positive rate. Through the above results provided by the attack detection, a robust adaptive controller-based active defense method is further developed. The proposed method can offset the impact of biased injection attack to maintain the stable running of power system. Simulation studies demonstrate the reliable response of the developed active defense method against biased injection attacks

Bio-Inspired Aquaporinz Containing Double-Skinned Forward Osmosis Membrane Synthesized through Layer-by-Layer Assembly

We demonstrated a novel AquaporinZ (AqpZ)-incorporated double-skinned forward osmosis (FO) membrane by layer-by-layer (LbL) assembly strategy. Positively charged poly(ethyleneimine) (PEI) and negatively charged poly(sodium 4-styrenesulfonate) (PSS) were alternately deposited on both the top and bottom surfaces of a hydrolyzed polyacrylonitrile (H-PAN) substrate. Subsequently, an AqpZ-embedded 1,2-dioleloyl-sn-glycero-3-phosphocholine (DOPC)/1,2-dioleoyl-3-trimethylammonium- propane (chloride salt) (DOTAP) supported lipid bilayer (SLB) was formed on PSS-terminated (T-PSS) membrane via vesicle rupture method. The morphology and structure of the biomimetic membranes were characterized by in situ atomic force microscopy (AFM), scanning electron microscope (SEM), Fourier transform infrared spectrometer using the attenuated total reflection technique (ATR-FTIR), and contact angle. Moreover, the FO performance of the resultant membrane was measured by using 2 M MgCl2 solution as draw solution and deionized (DI) water as feed solution, respectively. The membrane with a protein-to-lipid weight ratio (P/L) of 1/50 exhibits 13.2 L/m2h water flux and 3.2 g/m2h reversed flux by using FO mode, as well as 15.6 L/m2h water flux and 3.4 L/m2h reversed flux for PRO mode (the draw solution is placed against the active layer). It was also shown that the SLB layer of the double-skinned FO membrane can increase the surface hydrophilicity and reduce the surface roughness, which leads to an improved anti-fouling performance against humic acid foulant. The current work introduced a new method of fabricating high performance biomimetic FO membrane by combining AqpZ and a double-skinned structure based on LbL assembly

Structural Health Monitoring of Repairs in Carbon-Fiber-Reinforced Polymer Composites by MWCNT-Based Multiscale Sensors

The precision maintenance of delaminated carbon-fiber-reinforced polymer composites calls for the high demand of continuous, in situ monitoring of the damage-repair process along with the in-service status of the repaired region. Moreover, the repaired region faces a high risk of re-damage; therefore, in-service monitoring is highly desired. However, the current repair process lacks the in situ monitoring function, leading to the mechanism and evaluation of the repair approach being unclear. Here, we implanted multi-wall carbon nanotubes (MWCNTs) at the interface between the carbon fiber and resin matrix of the damaged region to achieve in situ monitoring of the repair, compression, and seawater-immersion processes. By depositing both the coupling agent and MWCNTs at the interfaces, a high recovery efficiency of 85% was achieved, which was independent of the delamination pattern shapes. The electric resistance changes of MWCNT-modified panels could effectively identify the resin permeation and solidification processes and could be used to in situ monitor the structural health of the repair region when it is subjected to the compression and seawater immersion tests. This strategy, combining high-efficient repair and precision maintenance, demonstrates potential in the structural applications of carbon-fiber-reinforced polymer composites

Soil respiration along an altitudinal gradient in a subalpine secondary forest in China

The subalpine forest ecosystems in the Miyaluo Forest District in western Sichuan (China) could be very sensitive to global climate change, with important consequences for the regional carbon (C) balance. In a birch secondary forest in this area, we measured plots with (Control) and without (No Litter) leaf litter to explore variation in soil respiration and its relationship with environmental factors along an altitudinal gradient, and to quantify the litter contribution to soil respiration. Soil respiration rate decreased with elevation. The average of soil respiration rates along the elevation gradient during the measurement period was 2.83 +/- 0.14 mu mol CO2 m(-2) s(-1) in the Control treatment and 2.35 +/- 0.16 mu mol CO2 m(-2) s(-1) in the No Litter treatment, with an average proportion of litter layer contribution to soil respiration of 17%. A significant linear relationship between soil respiration and soil temperature along the altitudinal gradient was found, while soil respiration was not significantly correlated with soil water content in both treatments. Soil temperature accounted for 94.9% and 95.6% of the total variation in soil respiration in Control and No Litter treatments, respectively. At altitudes of 2910 m, 3135 m, 3300 m and 3492 m a.s.l., soil respiration had a significant exponential relationship with soil temperature (p0.05). Soil temperature accounted for more than 92% and 81% of the total variation in soil respiration in Control and No Litter treatments, respectively, at all altitudes except at 3135 m a.s.l. Our results suggest that the expected temperature increases by global warming might enhance soil respiration in the birch secondary forest

Machine-Learning-Based Multi-Corner Timing Prediction for Faster Timing Closure

For the purpose of fixing timing violations, static timing analysis (STA) of full-corners is repeatedly executed, which is time-consuming. Given a timing path, timing results at some corners (“dominant corners”) are utilized to predict timing at other corners (“non-dominant corners”), which can greatly shorten the runtime of STA. However, the huge number of combinations of the dominant corners and the wide difference in prediction accuracy make it difficult to apply multi-corner timing prediction to chip industrial design. In this paper, we propose a dominant corner selection strategy to quickly determine the dominant corner combination with high prediction accuracy, along with which a new multi-corner timing prediction process is established to speed up STA. Experimental results show that our method can not only effectively accelerate STA, but also ensure the high prediction accuracy of the prediction timing. On the public ITC’99 benchmark, the prediction accuracy of the dominant corner combination selected by the proposed method is up to 98.2%, which is an improvement of 15% compared to the state-of-the-art method. For industrial application, we apply our method by using timing results on only 2 dominant corners to predict the other 12 non-dominant corners, which accelerates the runtime of the timing closure process by more than 2×

Effects of Heating on the Binding of Rare Earth Elements to Humic Acids

In deep underground environments, temperature is one of the key factors affecting the geochemistry behaviors of rare earth elements (REE) in organic-rich fluid. However, the influence of temperature on the interaction between humic acids (HA) and REE is not well known. In the present study, the influence of temperature on the HA–REE-binding behavior was evaluated based on heating experiments of REE-doped HA solution. Lignite-extracted HA and REE-binding experiments were conducted over a temperature range of 20 to 200 °C to quantify HA–REE complexation and the influence of temperature on HA binding sites. Results showed that increasing temperature and decreasing [REE]/[HA] ratio cause an increase of Kd value (the partition coefficient of REE between HA and aqueous solution). During heating KdREE KdREE patterns gradually change from middle REE-enriched-type (M-type) at 20 °C to light and middle REE-enriched-type (L-M-type) at 50 and 100 °C, and to light REE-enriched-type (L-type) at 150 °C and 200 °C. The increase of REE bonded with HA and modifications of KdREE patterns during the thermal treatment may be attributed to the increase of REE-binding sites, especially carboxylic sites, as a consequent of HA decomposition. This study provides a glimpse into the HA–REE-binding behaviors in the deep underground environment, which may shed light on the geochemical characteristics of REE in some organic-bearing rocks, and their changes during the coalification process

Effects of Heating on the Binding of Rare Earth Elements to Humic Acids

In deep underground environments, temperature is one of the key factors affecting the geochemistry behaviors of rare earth elements (REE) in organic-rich fluid. However, the influence of temperature on the interaction between humic acids (HA) and REE is not well known. In the present study, the influence of temperature on the HA–REE-binding behavior was evaluated based on heating experiments of REE-doped HA solution. Lignite-extracted HA and REE-binding experiments were conducted over a temperature range of 20 to 200 °C to quantify HA–REE complexation and the influence of temperature on HA binding sites. Results showed that increasing temperature and decreasing [REE]/[HA] ratio cause an increase of Kd value (the partition coefficient of REE between HA and aqueous solution). During heating KdREE KdREE patterns gradually change from middle REE-enriched-type (M-type) at 20 °C to light and middle REE-enriched-type (L-M-type) at 50 and 100 °C, and to light REE-enriched-type (L-type) at 150 °C and 200 °C. The increase of REE bonded with HA and modifications of KdREE patterns during the thermal treatment may be attributed to the increase of REE-binding sites, especially carboxylic sites, as a consequent of HA decomposition. This study provides a glimpse into the HA–REE-binding behaviors in the deep underground environment, which may shed light on the geochemical characteristics of REE in some organic-bearing rocks, and their changes during the coalification process