Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and restricted structural changes persist. This leads to the question: Is it possible to develop more property-conserving and structure-sensitive augmentation methods? Through a spectral lens, we investigate the interplay between graph properties, their augmentation, and their spectral behavior, and found that keeping the low-frequency eigenvalues unchanged can preserve the critical properties at a large scale when generating augmented graphs. These observations inform our introduction of the Dual-Prism (DP) augmentation method, comprising DP-Noise and DP-Mask, which adeptly retains essential graph properties while diversifying augmented graphs. Graph structures, modeling complex systems through nodes and edges, are ubiquitous across various domains, including social networks (Newman et al., 2002), bioinformatics (Yi et al., 2022), and transportation systems (Jin et al., 2023a). Graph Neural Networks (GNNs) (Kipf & Welling, 2016a) elegantly handle this relational information, paving the way for tasks such as accurate predictions. Their capabilities are further enhanced by graph data augmentation techniques. These methods artificially diversify the dataset through strategic manipulations, thereby bolstering the performance and generalization of GNNs (Rong et al., 2019; Feng et al., 2020; You et al., 2020). Graph data augmentation has progressed from early random topological modifications, exemplified by DropEdge (Rong et al., 2019) and DropNode (Feng et al., 2020), to sophisticated learning-centric approaches like InfoMin (Suresh et al., 2021). Furthermore, techniques inspired by image augmentation's mixup principle (Zhang et al., 2017) have emerged as prominent contenders in this domain (Verma et al., 2019; Wang et al., 2021; Guo & Mao, 2021). Though promising, these augmentation methods are challenged by three key issues as follows. Before the era of deep learning, graph properties, e.g., graph connectivity and diameter, served as vital features for classification for decades (Childs et al., 2009). While now they seem to be ignored, many aforementioned contemporary augmentation methods appear to sidestep this tradition and overlook the graph properties.

The emergence of Large language models (LLMs) is expected to have a major impact on education. This paper explores the potential of using ChatGPT, an LLM, as a virtual Teaching Assistant (TA) in an Introductory Programming Course. We evaluate ChatGPT's capabilities by comparing its performance with that of human TAs in some of the important TA functions. The TA functions which we focus on include (1) grading student code submissions, and (2) providing feedback to undergraduate students in an introductory programming course. Firstly, we assess ChatGPT's proficiency in grading student code submissions using a given grading rubric and compare its performance with the grades assigned by human TAs. Secondly, we analyze the quality and relevance of the feedback provided by ChatGPT. This evaluation considers how well ChatGPT addresses mistakes and offers suggestions for improvement in student solutions from both code correctness and code quality perspectives. We conclude with a discussion on the implications of integrating ChatGPT into computing education for automated grading, personalized learning experiences, and instructional support.

In this work we systematically review the recent advancements in code processing with language models, covering 50+ models, 30+ evaluation tasks, 170+ datasets, and 700+ related works. We break down code processing models into general language models represented by the GPT family and specialized models that are specifically pretrained on code, often with tailored objectives. We discuss the relations and differences between these models, and highlight the historical transition of code modeling from statistical models and RNNs to pretrained Transformers and LLMs, which is exactly the same course that had been taken by NLP. We also discuss code-specific features such as AST, CFG, and unit tests, along with their application in training code language models, and identify key challenges and potential future directions in this domain.

Radio maps quantify magnitudes such as the received signal strength at every location of a geographical region. Although the estimation of radio maps has attracted widespread interest, the vast majority of works rely on simulated data and, therefore, cannot establish the effectiveness and relative performance of existing algorithms in practice. To fill this gap, this paper presents the first comprehensive and rigorous study of radio map estimation (RME) in the real world. The main features of the RME problem are analyzed and the capabilities of existing estimators are compared using large measurement datasets collected in this work. By studying four performance metrics, recent theoretical findings are empirically corroborated and a large number of conclusions are drawn. Remarkably, the estimation error is seen to be reasonably small even with few measurements, which establishes the viability of RME in practice. Besides, from extensive comparisons, it is concluded that estimators based on deep neural networks necessitate large volumes of training data to exhibit a significant advantage over more traditional methods. Combining both types of schemes is seen to result in a novel estimator that features the best performance in most situations. The acquired datasets are made publicly available to enable further studies.

We investigate the CLIP image encoder by analyzing how individual model components affect the final representation. We decompose the image representation as a sum across individual image patches, model layers, and attention heads, and use CLIP's text representation to interpret the summands. Interpreting the attention heads, we characterize each head's role by automatically finding text representations that span its output space, which reveals property-specific roles for many heads (e.g. location or shape). Next, interpreting the image patches, we uncover an emergent spatial localization within CLIP. Finally, we use this understanding to remove spurious features from CLIP and to create a strong zero-shot image segmenter. Our results indicate that a scalable understanding of transformer models is attainable and can be used to repair and improve models.

Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves multi-step reasoning ability in language models, it is limited by being unimodal and applied mainly to question-answering tasks. We claim that incorporating visual augmentation into reasoning is essential, especially for complex, imaginative tasks. Consequently, we introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to recursively bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks that can benefit from temporal reasoning, as well as provide interpretability into models' multi-step reasoning. We apply VCoT to the Visual Storytelling and WikiHow summarization datasets and demonstrate through human evaluation that VCoT offers novel and consistent synthetic data augmentation beating chain-of-thought baselines, which can be used to enhance downstream performance.

Optimal control (OC) algorithms such as Differential Dynamic Programming (DDP) take advantage of the derivatives of the dynamics to efficiently control physical systems. Yet, in the presence of nonsmooth dynamical systems, such class of algorithms are likely to fail due, for instance, to the presence of discontinuities in the dynamics derivatives or because of non-informative gradient. On the contrary, reinforcement learning (RL) algorithms have shown better empirical results in scenarios exhibiting non-smooth effects (contacts, frictions, etc). Our approach leverages recent works on randomized smoothing (RS) to tackle non-smoothness issues commonly encountered in optimal control, and provides key insights on the interplay between RL and OC through the prism of RS methods. This naturally leads us to introduce the randomized Differential Dynamic Programming (R-DDP) algorithm accounting for deterministic but non-smooth dynamics in a very sample-efficient way. The experiments demonstrate that our method is able to solve classic robotic problems with dry friction and frictional contacts, where classical OC algorithms are likely to fail and RL algorithms require in practice a prohibitive number of samples to find an optimal solution.

Robot dog uses sensors, cameras and artificial intelligence to perceive and navigate surroundings. Agnieszka Pilat is not your typical artist. She doesn't use brushes, pencils or even her own hands to create her artwork. Pilat, who was born in Poland and now lives in the U.S., spent months teaching three of these four-legged machines named Basia, Vanya and Bunny to hold a paintbrush in their "mouths" and move them across a large canvas, turning the paint into abstract art. They use sensors, cameras and artificial intelligence to perceive and navigate their surroundings.

The manipulation of deformable objects by robotic systems presents a significant challenge due to their complex and infinite-dimensional configuration spaces. This paper introduces a novel approach to Deformable Object Manipulation (DOM) by emphasizing the identification and manipulation of Structures of Interest (SOIs) in deformable fabric bags. We propose a bimanual manipulation framework that leverages a Graph Neural Network (GNN)-based latent dynamics model to succinctly represent and predict the behavior of these SOIs. Our approach involves constructing a graph representation from partial point cloud data of the object and learning the latent dynamics model that effectively captures the essential deformations of the fabric bag within a reduced computational space. By integrating this latent dynamics model with Model Predictive Control (MPC), we empower robotic manipulators to perform precise and stable manipulation tasks focused on the SOIs. We have validated our framework through various empirical experiments demonstrating its efficacy in bimanual manipulation of fabric bags. Our contributions not only address the complexities inherent in DOM but also provide new perspectives and methodologies for enhancing robotic interactions with deformable objects by concentrating on their critical structural elements. Experimental videos can be obtained from https://sites.google.com/view/bagbot.

Visible (VIS) imagery of satellites has various important applications in meteorology, including monitoring Tropical Cyclones (TCs). However, it is unavailable at night because of the lack of sunlight. This study presents a Conditional Generative Adversarial Networks (CGAN) model that generates highly accurate nighttime visible reflectance using infrared (IR) bands and sunlight direction parameters as input. The model was trained and validated using target area observations of the Advanced Himawari Imager (AHI) in the daytime. This study also presents the first nighttime model validation using the Day/Night Band (DNB) of the Visible/Infrared Imager Radiometer Suite (VIIRS). The daytime statistical results of the Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Bias are 0.885, 28.3, 0.0428, 0.984, and -0.0016 respectively, completely surpassing the model performance of previous studies. The nighttime statistical results of SSIM, PSNR, RMSE, and CC are 0.821, 24.4, 0.0643, and 0.969 respectively, which are slightly negatively impacted by the parallax between satellites. We performed full-disk model validation which proves our model could also be readily applied in the tropical ocean without TCs in the northern hemisphere. This model contributes to the nighttime monitoring of meteorological phenomena by providing accurate AI-generated visible imagery with adjustable virtual sunlight directions.