Open source software supply-chain attacks, once successful, can exact heavy costs in mission-critical applications. As open source ecosystems for deep learning flourish and become increasingly universal, they present attackers previously unexplored avenues to code-inject malicious backdoors in deep neural network models. This article proposes Flareon , a small, stealthy, seemingly harmless code modification that specifically targets the data augmentation pipeline with motion-based triggers. Flareon neither alters ground-truth labels, nor modifies the training loss objective, nor does it assume prior knowledge of the victim model architecture, training data, and training hyperparameters. Yet, it has a surprisingly large ramification on training—models trained under Flareon learn powerful target-conditioned (or “ all2all ”) backdoors. We also proposed a learnable variant of Flareon that is even stealthier in terms of added perturbations. The resulting models can exhibit high attack success rates for any target choices and better clean accuracies than backdoor attacks that not only seize greater control but also assume more restrictive attack capabilities. We also demonstrate the resilience of Flareon against a wide range of defenses. Flareon is fully open source and available online to the deep learning community.

With the rapid development of large language models (LLMs), there has been a strong interest in integrating other modalities such as image comprehension capabilities. While they have shown impressive performance in various multimodal tasks, the robustness of Visual Language Models (VLMs) has not been thoroughly investigated. We mainly focus on the robustness of VLMs on visual adversarial examples. In this work, we explore the capability of adversarial examples targeting VLMs. We highlight that the multimodal nature of VLMs presents a unique attack surface to manipulate the outputs of the LLMs, and the continuous nature of visual inputs further enhances the effectiveness of adversarial attacks against language generative models. Furthermore, we demonstrate three application scenarios for adversarial examples targeting VLMs: image description, jailbreaking, and information hiding. We conduct experiments on several leading open-source VLMs and demonstrate the successful application of adversarial examples in all the proposed scenarios. We hope that our findings would enable the development of multimodal models more robust to adversarial attacks. Our code is available at https://github.com/lafeat/m3-break.

Publishing commuting trajectory data, including information of home and workplace locations, commuting distances and working hours, provides valuable insights for urban transportation planning. However, the data also contains sensitive personal information, raising privacy concerns even after the removal of unique identifiers. While traditional privacy-preserving methods, such as k-anonymity and differential privacy, have been widely applied, they mainly focus on single-trip travel patterns (e.g., point sequences or paths) and fail to capture the unique characteristics of commuting behavior. In this paper, we propose RCDP, a novel differential privacy-based model for synthesizing realistic commuting data using a prefix tree structure. RCDP introduces two key innovations: (1) it models round-trip commuting patterns through adaptive spatio-temporal generalization and a prefix tree, ensuring that the synthesized data retains the key commuting characteristics; (2) it employs a hierarchical privacy budget allocation mechanism that dynamically adjusts the budget across tree levels, along with a distribution-based node insertion method to maintain tree consistency, effectively balancing privacy and utility. Validation using public transport smart card data in Shenzhen, China demonstrates that RCDP outperforms existing k-anonymity and differential privacy approaches in preserving essential commuting features while ensuring strong privacy protection.

Deep learning is one of the most widely used modeling approach for spatio-temporal prediction. Traditional deep learning models get discrete features or hidden states from discrete data. However such discretization method cannot work well if the data is irregularly sampled or incompletely observed. It is difficult to obtain continuous features and hidden states for deep learning models. In this paper, we demonstrate how to use neural differential equations to solve this problem. We first validated the ability of Neural Controlled Differential Equations (Neural CDE) to process real-world irregular spatiotemporal data. Neural CDE is a type of neural differential equation that can obtain continuous hidden states. To make Neural CDE can obtain the spatial correlation of spatio-temporal data, we combined Graph Attention Network (GAT) with Neural CDE to propose a new model for spatio-temporal prediction. Furthermore, we used neural differential equations to designed a module to reduce the error of the prediction results. Experimental results show our proposed model outperforms the standard baseline by up to 37.40%.

Urban traffic network is a typical complex network. Traffic states data (e.g., traffic flow, traffic occupancy, traffic speed, etc.) has strong temporal and spatial correlation. To accurately predict urban traffic state, it is very important to extract the road features in the traffic network. The existing methods use separated temporal and spatial components or Spatio-temporal fusion components to predict traffic. Graph Convolution Network (GCN) is usually used to obtain the correlation between spatial nodes or Spatio-temporal nodes. However, the message aggregation method of GCN cannot assign different weights to neighbor nodes. While Graph Attention Network (GAT) can pay attention to different neighbor nodes. To better explain the existing traffic prediction models, we carried out experiments on the model framework based on GCN. We use a new proposed GAT instead of GCN, and find that the new GAT has better performance in multi-features traffic prediction tasks. We also made a theoretical analysis on the improvement of the performance and carried out experiments on four real datasets, which can provide strong support for the theoretical analysis. Our method improves the interpretability of the Graph Neural Network (GNN) model in extracting spatial features of the traffic networks.

To improve resource efficiency and design intelligent scheduler for clouds, it is necessary to understand the workload characteristics and machine utilization in large-scale cloud data centers. In this paper, we perform a deep analysis on a newly released trace dataset by Alibaba in September 2017, consists of detail statistics of 11089 online service jobs and 12951 batch jobs co-locating on 1300 machines over 12 hours. To the best of our knowledge, this is one of the first work to analyze the Alibaba public trace. Our analysis reveals several important insights about different types of imbalance in the Alibaba cloud. Such imbalances exacerbate the complexity and challenge of cloud resource management, which might incur severe wastes of resources and low cluster utilization. 1) Spatial Imbalance: heterogeneous resource utilization across machines and workloads. 2) Temporal Imbalance: greatly time-varying resource usages per workload and machine. 3) Imbalanced proportion of multi-dimensional resources (CPU and memory) utilization per workload. 4) Imbalanced resource demands and runtime statistics (duration and task number) between online service and offline batch jobs. We argue accommodating such imbalances during resource allocation is critical to improve cluster efficiency, and will motivate the emergence of new resource managers and schedulers.