Learning to Prevent Profitless Neural Code Completion

Currently, large pre-trained models are widely applied in neural code completion systems, such as Github Copilot, aiXcoder, and TabNine. Though large models significantly outperform their smaller counterparts, a survey with 2,631 participants reveals that around 70\% displayed code completions from Copilot are not accepted by developers. Being reviewed but not accepted, these completions bring a threat to productivity. Besides, considering the high cost of the large models, it is a huge waste of computing resources and energy, which severely goes against the sustainable development principle of AI technologies. Additionally, in code completion systems, the completion requests are automatically and actively issued to the models as developers type out, which significantly aggravates the workload. However, to the best of our knowledge, such waste has never been realized, not to mention effectively addressed, in the context of neural code completion. Hence, preventing such profitless code completions from happening in a cost-friendly way is of urgent need. To fill this gap, we first investigate the prompts of these completions and find four observable prompt patterns, which demonstrate the feasibility of identifying such prompts based on prompts themselves. Motivated by this finding, we propose an early-rejection mechanism to turn down low-return prompts by foretelling the completion qualities without sending them to the LCM. Further, we propose a lightweight Transformer-based estimator to demonstrate the feasibility of the mechanism. The experimental results show that the estimator rejects low-return prompts with a promising accuracy of 83.2%.

Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This paper proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. Firstly, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV's trajectory, hovering time and the RIS's reflection coefficients. To solve this complex non-convex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV's trajectory and hovering time, in which a minorization-maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV's trajectory, flight time and the RIS's reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.

Multi-Pair D2D Communications Aided by An Active RIS over Spatially Correlated Channels with Phase Noise

This paper investigates a multi-pair device-to-device (D2D) communication system aided by an active reconfigurable intelligent surface (RIS) with phase noise and direct link. The approximate closed-form expression of the ergodic sum rate is derived over spatially correlated Rician fading channels with statistical channel state information (CSI). When the Rician factors go to infinity, the asymptotic expressions of the ergodic sum rates are presented to give insights in poor scattering environment. The power scaling law for the special case of a single D2D pair is presented without phase noise under uncorrelated Rician fading condition. Then, to solve the ergodic sum rate maximization problem, a method based on genetic algorithm (GA) is proposed for joint power control and discrete phase shifts optimization. Simulation results verify the accuracy of our derivations, and also show that the active RIS outperforms the passive RIS.

Deep Reinforcement Learning for RIS-aided Multiuser Full-Duplex Secure Communications with Hardware Impairments

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and an RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time-varying and the system is high-dimensional, this non-convex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of neural network can be accelerated with appropriate neural network parameters.

Towards Hybrid-Optimization Video Coding

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. It searches for the best one among multiple starting points (i.e. modes). However, the search is not efficient enough. On the other hand, end-to-end learned coding represents the continuous optimization solution because the gradient descent is based on a continuous function. It optimizes a group of model parameters efficiently by the numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose to regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments. Compared to the continuous optimization framework, our method outperforms pure learned video coding methods. Meanwhile, compared to the discrete optimization framework, our method achieves comparable performance to HEVC reference software HM16.10 in PSNR.

Piecewise Linear Neural Networks and Deep Learning

As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the representation and the learning have long been studied. In 1977, the canonical representation pioneered the works of shallow PWLNNs learned by incremental designs, but the applications to large-scale data were prohibited. In 2010, the Rectified Linear Unit (ReLU) advocated the prevalence of PWLNNs in deep learning. Ever since, PWLNNs have been successfully applied to extensive tasks and achieved advantageous performances. In this Primer, we systematically introduce the methodology of PWLNNs by grouping the works into shallow and deep networks. Firstly, different PWLNN representation models are constructed with elaborated examples. With PWLNNs, the evolution of learning algorithms for data is presented and fundamental theoretical analysis follows up for in-depth understandings. Then, representative applications are introduced together with discussions and outlooks.

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pre-training. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively.

Multiple EffNet/ResNet Architectures for Melanoma Classification

Melanoma is the most malignant skin tumor and usually cancerates from normal moles, which is difficult to distinguish benign from malignant in the early stage. Therefore, many machine learning methods are trying to make auxiliary prediction. However, these methods attach more attention to the image data of suspected tumor, and focus on improving the accuracy of image classification, but ignore the significance of patient-level contextual information for disease diagnosis in actual clinical diagnosis. To make more use of patient information and improve the accuracy of diagnosis, we propose a new melanoma classification model based on EffNet and Resnet. Our model not only uses images within the same patient but also consider patient-level contextual information for better cancer prediction. The experimental results demonstrated that the proposed model achieved 0.981 ACC. Furthermore, we note that the overall ROC value of the model is 0.976 which is better than the previous state-of-the-art approaches.

Image Captioning In the Transformer Age

Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not share the basic network component, such a heterogeneous pipeline is hard to be trained end-to-end where the visual encoder will not learn anything from the caption supervision. This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline. Meantime, self-supervised learning releases the power of the Transformer architecture that a pre-trained large-scale one can be generalized to various tasks including IC. The success of these large-scale models seems to weaken the importance of the single IC task. However, we demonstrate that IC still has its specific significance in this age by analyzing the connections between IC with some popular self-supervised learning paradigms. Due to the page limitation, we only refer to highly important papers in this short survey and more related works can be found at https://github.com/SjokerLily/awesome-image-captioning.

Evolving symbolic density functionals

Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than tens of thousands parameters, which makes a huge gap in the formulation with the conventional human-designed symbolic functionals. We propose a new framework, Symbolic Functional Evolutionary Search (SyFES), that automatically constructs accurate functionals in the symbolic form, which is more explainable to humans, cheaper to evaluate, and easier to integrate to existing density functional theory codes than other ML functionals. We first show that without prior knowledge, SyFES reconstructed a known functional from scratch. We then demonstrate that evolving from an existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google Accelerated Science 22), that performs better on main-group chemistry. Our framework opens a new direction in leveraging computing power for the systematic development of symbolic density functionals.