This paper presents a multi-granularity method for source code summarization, which generates a concise functional description for the given code snippet. We notice that skilled programmers write and read source codes hierarchically and pay close attention to conceptual entities like statements, tokens, sub-tokens, and the mapping relations between them. The entities have specific emphasis according to their granularities, e.g., statements in coarse-granularity reveal the global logical semantics of code, and the sub-tokens in fine-granularity are more related to the textual semantics. Driven by this observation, we demonstrate that a multi-granularity formulation incorporating these conceptual entities benefit the code summarization task. Concretely, the source code is transformed into a pyramidal representation, and then a pyramid attention mechanism is applied for efficient feature aggregation among different hierarchies in it. We instantiate our multi-granularity method using the proposed pyramid attention and name it P A-former (Pyramid Attention transformer). We evaluated it on two source code summarization benchmarks where it surpasses the prior works and achieves new state-of-the-art results.

A.2 Proof of Theorem 1 To prove Theorem 1, we assume that G Proof of Lemma 1. Let's first rewrite Equation (4) as null null By Lemma 1, linearity of expectation and knowing that each RWT is independent from the other tours by the Strong Markov Property, Theorem 1 holds. MHM-GNN can recover edge-based models where representations don't use graph-wide However, on Rent the Runway we see the raw features achieving the highest performance. That is, structural information does not seem to be relevant to this specific task. All hyperparameters were chosen to minimize training loss. For k = 5, we used a minibatch of size 5 in all datasets.

Algorithm 1 shows the pseudocode of LIAM.Algorithm 1 Pseudocode of LIAM's algorithmfor m = 1,...,M episodes do Reset the hidden state of the encoder LSTM Sample E fixed policies from Π Create E parallel environments and gather initial observations a The fixed policies in the predator-prey consist of a combination of heuristic and pretrained policies. First we created four heuristic policies, which are: (i) going after the prey, (ii) going after one of the predators, (iii) going after the agent (predator or prey) that is closest, (iv) going after the predator that is closest. CARL has access to the trajectories of all the other agents in the environment during training, but during execution only to the local trajectory. To extract such representations, we use self-supervised learning based on recent advances on contrastive learning [Oord et al., 2018, He et al., 2020, Chen et al., 2020a,b]. During training and given a batch of episode trajectories we construct the positive and negative pairs following Equation (4) and minimise the InfoNCE loss [Oord et al., 2018] Following the work of Chung et al. [2015] we can write the lower bound in the log-evidence of the We train LIAM-V AE similarly to LIAM.

Indeed, we can accuse modern machine learning models of emulating the same behaviour.

We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model. Our approach's key novelty lies in formulating dense correspondence computation as initializing and synchronizing local transformations between the scan and the template model.

Integrated circuits (ICs) are extensively used in modern electronic products like computers, smart-phones, and cars. Electronic Design Automation (EDA) includes a set of tools for circuit design in different development stages especially logic synthesis stage and placement stage (Fig.1).

While early adopters continue to employ CNN backbones, the latest networks are end-to-end CNN-free Transformer solutions.