ACE: A Cliché-based Program Structure Editor

ACE extends the syntax-directed paradigm of program editing by adding support for programming clichés. A programming cliché is a standard algorithmic fragment. ACE supports the rapid construction of programs through the combination of clichés selected from a cliché library. ACE is also innovative in the way it support the basic structure editor operations. Instead of being based directly on the grammar for a programming language, ACE is based on a modified grammar which is designed to facilitate editing. Uniformity of the user interface is achieved by encoding the modified grammar as a set of clichés.MIT Artificial Intelligence Laborator

Formal specification techniques for promoting software modularity, enhancing documentation, and testing specifications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 173-175).by Yang Meng Tan.Ph.D

A Program Design Assistant

The DA will be a design assistant which can assist the programmer in low-level design. The input language of the DA is a cliché-based program description language that allows the specification and high-level design of commonly-written programs to be described concisely. The DA language is high-level in the sense that programmers need not bother with detailed design. The DA will provide automatic low-level design assistance to the programmer in selecting appropriate algorithms and data structures. It will also detect inconsistencies and incompleteness in program descriptions. A key related issue in this research is the representation of programming knowledge in a design assistant. The knowledge needed to automate low-level design and the knowledge in specific programming clichés have to be represented explicitly to facilitate reuse.MIT Artificial Intelligence Laborator

Low CPNE3 expression is associated with risk of acute myocardial infarction: A feasible genetic marker of acute myocardial infarction in patients with stable coronary artery disease

Background: Gene COPINE III may be related to a phosphoprotein with intrinsic kinase activity and  belongs to an unconventional kinase family. The CPNE3 gene may be used as a biomarker for assess- ment of occurrence and prognosis of various tumors. Methods: Peripheral blood was collected from 87 stable coronary artery disease (CAD) patients and 91 acute myocardial infarction (AMI) patients. Real-time quantitative polymerase chain reaction test and the western blot method were adopted to measure expression quantity of CPNE3 gene at the mRNA level and the protein level.  Results: The expression of the CPNE3 gene in peripheral blood of AMI patients was significantly lower than those in peripheral blood of stable CAD patients. Low expression of CPNE3 gene was found to be unrelated to level of fasting blood glucose and serum blood lipid of patients, quantity of cardiac troponin and time of onset but was found to be correlated to the Gensini score for coronary artery. When the ex- pression of CPNE3 gene at the mRNA level in peripheral blood was used as the criterion for diagnosing AMI, its sensitivity, specificity, positive predictive value and negative predictive value were 69%, 64.8%, 68.6% and 65.2%, respectively.  Conclusions: Compared to stable CAD patients, AMI patients have a lower expression of CPNE3 gene in their peripheral blood. Patients who have low CPNE3 expression in peripheral blood are more likely to suffer from AMI than those with stable CAD. Low expression of CPNE3 gene serves as an potential independent risk factor of AMI.

Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

This paper presents a novel allowable deformation prediction model of surrounding rock based on support vector machine (SVM). The engineering rock mass classification is subdivided based on the national standards Standard for Engineering Classification of Rock Masses in order to get more accurate physicalmechanical parameters. Using the developed parameters, 100 sets of multi-factors and multi-levels orthogonal experiments are designed, which are simulated with two-dimensional numerical models established based on ABAQUS. 100 groups of learning samples and 9 samples of random inspection are obtained. The prediction model has been established from the study of learning samples based on LibSVM. Using this model, 9 samples of random inspection and 9 engineering examples are predicted and the prediction accuracy is good compared with their actual values. It is indicated that this model can meet the initial support design requirements of underground caverns well. The novel model has the advantages of convenience, rapidity, and reliability

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.Comment: Accepted by IEEE Robotics and Automation Letters (RAL

InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition

In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by step and then adding a fixed penalty margin to reduce the intra-class distance to increase the inter-class distance. Although a great deal of previous work has been done to optimize the boundary penalty to improve the discriminative power of the model, adding a fixed margin penalty to the depth feature and the corresponding weight is not consistent with the pattern of data in the real scenario. To address this issue, in this paper, we propose a novel loss function, InterFace, releasing the constraint of adding a margin penalty only between the depth feature and the corresponding weight to push the separability of classes by adding corresponding margin penalties between the depth features and all weights. To illustrate the advantages of InterFace over a fixed penalty margin, we explained geometrically and comparisons on a set of mainstream benchmarks. From a wider perspective, our InterFace has advanced the state-of-the-art face recognition performance on five out of thirteen mainstream benchmarks. All training codes, pre-trained models, and training logs, are publicly released \footnote{$https://github.com/iamsangmeng/InterFace$}.Comment: arXiv admin note: text overlap with arXiv:2109.09416 by other author

Continuous Versatile Jumping Using Learned Action Residuals

Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement learning to learn continuous jumping motions for quadrupedal robots. The core of our framework is a stance controller, which combines a manually designed acceleration controller with a learned residual policy. As the acceleration controller warm starts policy for efficient training, the trained policy overcomes the limitation of the acceleration controller and improves the jumping stability. In addition, a low-level whole-body controller converts the body pose command from the stance controller to motor commands. After training in simulation, our framework can be deployed directly to the real robot, and perform versatile, continuous jumping motions, including omni-directional jumps at up to 50cm high, 60cm forward, and jump-turning at up to 90 degrees. Please visit our website for more results: https://sites.google.com/view/learning-to-jump.Comment: To be presented at L4DC 202

Cross-linked CoMoO4/rGO nanosheets as oxygen reduction catalyst

Development of inexpensive and robust electrocatalysts towards oxygen reduction reaction (ORR) is crucial for the cost-affordable manufacturing of metal-air batteries and fuel cells. Here we show that cross-linked CoMoO4 nanosheets and reduced graphene oxide (CoMoO4/rGO) can be integrated in a hybrid material under one-pot hydrothermal conditions, yielding a composite material with promising catalytic activity for oxygen reduction reaction (ORR). Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to investigate the efficiency of the fabricated CoMoO4/rGO catalyst towards ORR in alkaline conditions. The CoMoO4/rGO composite revealed the main reduction peak and onset potential centered at 0.78 and 0.89 V (vs. RHE), respectively. This study shows that the CoMoO4/rGO composite is a highly promising catalyst for the ORR under alkaline conditions, and potential noble metal replacement cathode in fuel cells and metal-air batteries