Let us begin by recalling how SP ADE works, and study where its defects come from. These statistics are calculated via averages over examples and all spatial dimensions. In Figure 4, we can see that SP ADE has these droplet artifacts as well. Despite the rationale behind this idea, we could not find settings where we noticed a decrease in distortion that was not accompanied by a drastic decrease in quality. SSNs trained in FFHQ at 256 x 256 resolution.

Many computer vision problems can be phrased as conditional or unconditional image generation. This includes super-resolution, colorization, and semantic image synthesis among others.

Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moisture time-series analysis either rely on threshold-based rules or data-hungry machine learning or deep learning models that are limited in adaptability and interpretability. In this study, we introduce SP ADE (Soil moisture Pattern and Anomaly DE-tection), an integrated framework that leverages large language models (LLMs) to jointly detect irrigation patterns and anomalies in soil moisture time-series data. By converting time-series data into a textual representation and designing domain-informed prompt templates, SP ADE identifies irrigation events, estimates net irrigation gains, detects, classifies anomalies, and produces structured, interpretable reports. Experiments were conducted on real-world soil moisture sensor data from commercial and experimental farms cultivating multiple crops across the United States. Results demonstrate that SP ADE outperforms the existing method in anomaly detection, achieving higher recall and F1 scores and accurately classifying anomaly types. Furthermore, SP ADE achieved high precision and recall in detecting irrigation events, indicating its strong capability to capture irrigation patterns accurately. SP ADE's reports provide interpretability and usability of soil moisture analytics. This study highlights the potential of LLMs as scalable, adaptable tools for precision agriculture, which is capable of integrating qualitative knowledge and data-driven reasoning to produce actionable insights for accurate soil moisture monitoring and improved irrigation scheduling from soil moisture time-series data. Introduction Global crop production systems are facing mounting challenges due to climate change, population growth, and water scarcity (Farooq et al., 2023). These challenges demand more resource-efficient agricultural strategies.

Figure 1: A sequence of generated images from Cityscapes. Q1: (1) Motivation behind the proposed method? A2: The contribution of our discriminator design is twofold. We will add more explanations in the final version. A3: The parameter size of our proposed generater is 107.4 million, which is similar to that of SP ADE (96 million).

Below we clarify each question and we hope reviewers can raise their scores based on the responses. L205), we have provided the detailed experiment settings. The default ratio value is 50%, i.e., train 1 iteration passport-aware branch after training every 1 iteration In this case, the theoretical computation cost will be 2x. More importantly, this will not introduce any extra cost for deployment . Then it can be viewed as a special case (i.e., only the nonlinear transform We adopt a similar setting as the trigger-set based method [10].

In this work, we introduce SPADE, a path planning framework designed for autonomous navigation in dynamic environments using 3D scene graphs. SPADE combines hierarchical path planning with local geometric awareness to enable collision-free movement in dynamic scenes. The framework bifurcates the planning problem into two: (a) solving the sparse abstract global layer plan and (b) iterative path refinement across denser lower local layers in step with local geometric scene navigation. To ensure efficient extraction of a feasible route in a dense multi-task domain scene graphs, the framework enforces informed sampling of traversable edges prior to path-planning. This removes extraneous information not relevant to path-planning and reduces the overall planning complexity over a graph. Existing approaches address the problem of path planning over scene graphs by decoupling hierarchical and geometric path evaluation processes. Specifically, this results in an inefficient replanning over the entire scene graph when encountering path obstructions blocking the original route. In contrast, SPADE prioritizes local layer planning coupled with local geometric scene navigation, enabling navigation through dynamic scenes while maintaining efficiency in computing a traversable route. We validate SPADE through extensive simulation experiments and real-world deployment on a quadrupedal robot, demonstrating its efficacy in handling complex and dynamic scenarios.

Large language models are computationally expensive due to their deep structures. Prior research has shown that intermediate layers contain sufficient information to generate accurate answers, leading to the development of early-exit algorithms that reduce inference costs by terminating computation at earlier layers. However, these methods often suffer from poor performance due to misalignment between intermediate and output layer representations that lead to decoding inaccuracy. To address these challenges, we propose SPADE (SPace Alignment DEcoding), a novel decoding method that aligns intermediate layer representations with the output layer by propagating a minimally reduced sequence consisting of only the start token and the answer token. We further optimize the early-exit decision-making process by training a linear approximation of SPADE that computes entropy-based confidence metrics. Putting them together, we create a hybrid early-exit algorithm that monitors confidence levels and stops inference at intermediate layers while using SPADE to generate high-quality outputs. This approach significantly reduces inference costs without compromising accuracy, offering a scalable and efficient solution for deploying large language models in real-world applications.

Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are being developed. Optimizing these programs for accelerators is challenging for two reasons: program performance is highly sensitive to variations in sparse inputs, and early-stage accelerators rely on expensive simulators. Therefore, ML-based cost models used for optimizing such programs on general-purpose hardware are often ineffective for early-stage accelerators, as they require large datasets for proper training. To this end, we introduce COGNATE, a novel framework that leverages inexpensive data samples from general-purpose hardware (e.g., CPUs) to train cost models, followed by few-shot fine-tuning on emerging hardware. COGNATE exploits the homogeneity of input features across hardware platforms while effectively mitigating heterogeneity, enabling cost model training with just 5% of the data samples needed by accelerator-specific models to achieve comparable performance. We conduct extensive experiments to demonstrate that COGNATE outperforms existing techniques, achieving average speedups of 1.47x (up to 5.46x) for SpMM and 1.39x (up to 4.22x) for SDDMM.

Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes. W e assume pixel-wise segmentation labels are available during training and use them to learn the scene structure. During inference, our model first synthesizes a realistic segmentation layout from scratch, then synthesizes a realistic scene conditioned on that layout. F or the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts. F or the latter, we use a conditional segmentation-to-image synthesis network that captures the distribution of photo-realistic images conditioned on the semantic layout. When trained end-to-end, the resulting model outperforms state-of-the-art generative models in unsupervised image synthesis on two challenging domains in terms of the Fr echet Inception Distance and user-study evaluations. Moreover, we demonstrate the generated segmentation maps can be used as additional training data to strongly improve recent segmentation-to-image synthesis networks.