Given a large graph G with a set of its k vertices called terminals, a quality-q flow sparsifier is a small graph G′ that contains the terminals and preserves all multicommodity flows between them up to some multiplicative factor q≥ 1, called the quality. Constructing flow sparsifiers with good quality and small size (|V(G′)|) has been a central problem in graph compression.

For complex dynamic systems, their dynamical behaviours often exhibit unique phenomena such as chaotic oscillations and multistability switching, one of the main reasons for which is the nonlinear characteristics. In addition, the time-varying parameters, strong coupling effects, and multiscale interactions of nonlinear systems further exacerbate the complexity of modelling and analysis. Therefore, in order to study nonlinear complex dynamic systems and to obtain their physical properties, this paper proposes a data-driven method, the nonlinear probing excitation method. Nonlinear complex dynamical systems are often regarded as black-box systems and cannot be explained directly. The method proposed in this paper constructs a data-driven model through inputs and outputs, which contains the essential properties of the system, and is able to extract the physical properties of the system from it, and then explains part of the black-box system, and by repeating this process, it is possible to construct multiple models, and obtain the physical properties of the system one by one, and ultimately complete the implementation of the interpretability of the whole black-box model. In this way, the correlation between the input and output of the system and the nonlinear nature of the system is established from the data-driven point of view, and the physical characteristics of the nonlinear system are obtained, in order to predict and control the behaviour of the nonlinear system.

Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder practical deployment. Although both quantization and sparsity can independently accelerate inference while maintaining generation quality, naively combining these techniques in existing training-free approaches leads to significant performance degradation, as they fail to achieve proper joint optimization. We introduce FPSAttention, a novel training-aware co-design of FP8 quantization and Sparsity for video generation, with a focus on the 3D bi-directional attention mechanism. Our approach features three key innovations: 1) A unified 3D tile-wise granularity that simultaneously supports both quantization and sparsity. 2) A denoising step-aware strategy that adapts to the noise schedule, addressing the strong correlation between quantization/sparsity errors and denoising steps. 3) A native, hardware-friendly kernel that leverages FlashAttention and is implemented with optimized Hopper architecture features, enabling highly efficient execution. Trained on Wan2.1's 1.3B and 14B models and evaluated on the vBench benchmark, FPSAttention achieves a 7.09$\times$ kernel speedup for attention operations and a 4.96$\times$ end-to-end speedup for video generation compared to the BF16 baseline at 720p resolution—without sacrificing generation quality.

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at helping to accelerate or even autonomously perform work-related tasks? The answer to this question has important implications for both industry looking to adopt AI into their workflows, and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents' performance on performing real-world professional tasks, in this paper, we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that with the most competitive agent, 30% of the tasks can be completed autonomously. This paints a nuanced picture on task automation with LM agents -- in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems. For more information and demos, refer to https://the-agent-company.com.

Bonne and Censor-Hillel (ICALP 2019) initiated the study of distributed subgraph finding in dynamic networks of limited bandwidth. For the case where the target subgraph is a clique, they determined the tight bandwidth complexity bounds in nearly all settings. However, several open questions remain, and very little is known about finding subgraphs beyond cliques. In this work, we consider these questions and explore subgraphs beyond cliques.

Bonne and Censor-Hillel (ICALP 2019) initiated the study of distributed subgraph finding in dynamic networks of limited bandwidth. For the case where the target subgraph is a clique, they determined the tight bandwidth complexity bounds in nearly all settings. However, several open questions remain, and very little is known about finding subgraphs beyond cliques. In this work, we consider these questions and explore subgraphs beyond cliques in the deterministic setting. For finding cliques, we establish an Ω(log log n) bandwidth lower bound for one-round membership-detection under edge insertions only and an Ω(log log log n) bandwidth lower bound for one-round detection under both edge insertions and node insertions. Moreover, we demonstrate new algorithms to show that our lower bounds are tight in bounded-degree networks when the target subgraph is a triangle. Prior to our work, no lower bounds were known for these problems. For finding subgraphs beyond cliques, we present a complete characterization of the bandwidth complexity of the membership-listing problem for every target subgraph, every number of rounds, and every type of topological change: node insertions, node deletions, edge insertions, and edge deletions. We also show partial characterizations for one-round membership-detection and listing.

Fourier ptychography (FP) is a new computational imaging technique with the advantage of being able to provide super-resolution imaging. FP has a very complex degradation process. Merging with Fourier transforms and pupil aperture scanning causes difficulty in reconstructing high-resolution images by the commonly used deep neural network methods, e.g., based on convolutional neural networks (CNNs). In this paper, we propose a new optimization algorithm for FP, which is carefully designed so that it only constrains concise operations. Then, we unfold the proposed algorithm to design a new neural network, MDFP-Net, specifically for the FP task. MDFP-Net is consistent with a few stages, which well corresponds to the iterations of the proposed optimization algorithm for FP. This not only makes MDFP-Net more intuitively interpretable, but also makes MDFP-Net much more suitable for FP tasks than commonly used CNNs. Moreover, we have built a long-distance reflection FP measurement system and tested our neural network in real experiments. Simulation and real experimental results show that the proposed network can provide better reconstruction results than either traditional algorithms or other deep learning methods. Code is available at https://github.com/BP113/MDFPNET.

Recently, Akbari et al. (ICALP 2023) studied the locality of graph problems in distributed, sequential, dynamic, and online settings from a unified point of view. They designed a novel O(log n)-locality deterministic algorithm for proper 3-coloring bipartite graphs in the Online-LOCAL model. In this work, we establish the optimality of the algorithm by showing a tight deterministic Ω (log n) locality lower bound, which holds even on grids. To complement this result, we have the following additional results:

With the advancement of geo-systems and the increased availability of satellite data, a plethora of Land-Use and Land-Cover (LULC) products have been developed. The existing LULC products primarily relied on time-series imagery to classify land by pixel-based classifiers, allowing for local analysis and accurate boundary detection. However, the advent of deep learning has shifted towards the use of patch-based CNN models for generating land cover maps. In this paper, (1) we create a training dataset for China using a voting strategy based on three off-the-shelf available LULC products, avoiding the labor-intensive manual annotation. (2) We design a novel CNN-based model for LULC task, called Multi-modal Fine-grained Dual Network (dubbed as Dual-Net), which takes dual-date images to generate final maps, and reduces the need for gap-free temporal sequences or separate cloud detection. To leverage the correlation between location, date, and category, we embed multi-modal information (dates and geo-locations) to the model. Further, by incorporating low-level constraints and using pseudo-label refinement, we continually improve the performance and achieve more refined segmentation. (3) Due to the lack of a suitable validation dataset for China, we create a new validation dataset called China Sentinel2 Validation Dataset (CSVD) by manually annotating 733 finely labeled images of 1024 × 1024 pixels of China-specific Sentinel2 data. (4) Extensive experiments demonstrate that our model outperforms existing LULC products and produces more fine-grained segmentation results comparable to other patch-based products. Finally, we release annual LULC maps for China in 2020-2022 and also make our model accessible online for real-time results export.

The decoupling control of two-link flexible manipulators with uncertain parameters and joint motor dynamics is investigated by combining terminal sliding mode (TSM) and output redefinition in this paper. The linear combination of joint angles and flexible modes is chosen as the redefined output to overcome the inherent non-minimum phase characteristics of flexible manipulators; correspondingly the system is decomposed into an input-output subsystem with motor dynamics and an internal dynamic subsystem with uncertain disturbances. A TSM controller is designed for the robust stability of input-output subsystem as well as to transfer the internal dynamics into zero dynamics. The stability of zero dynamic subsystem is proved to be related to the redefined parameters. Finally the steady-state error of the tip position for each link is deduced by Lyapunov stability theory. Simulation results validate the proposed scheme.

In this paper, based on the traditional Partial Least Squares(PLS) algorithm, a new way to select the most effective data sets for the PLS regression is proposed. The reason why we apply this new approach is that it could maintain or even surpass the original performance of control and diagnosis of a certain process while keep the data sets as less as possible to enhance the conciseness. The most significant advantage of the proposed data set selection method is that it identifies the data sets with the most typical characteristics of the group of data, excluding less informative data. Based on the ordinary PLS algorithm and with the improvement of conciseness, a better performance on prediction and fitting could be achieved. To achieve these goals, the ordinary and the improved PLS algorithm are introduced, after which a new method of data set selection is proposed. Specifically, the enhanced effectiveness of the proposed approach could be revealed by the simulation results of a numerical case.