Web automation uses intelligent agents to perform high-level tasks by mimicking human interactions with webpages. Despite recent advances in LLM-based web agents, efficiently navigating complex, real-world webpages remains challenging due to massive DOM structures (10,000 ~ 100,000 tokens). Current approaches either truncate DOMs—losing vital information—or use inefficient heuristics and separate ranking models, failing to balance precision and scalability. We introduce Prune4Web, a novel paradigm that transforms DOM processing from LLM-based filtering to programmatic pruning. Our key innovation is DOM Tree Pruning Programming, where an LLM generates executable Python scoring programs to dynamically filter DOM elements based on semantic clues from decomposed sub-tasks. This approach eliminates the need for LLMs to process full DOMs, instead delegating traversal and scoring to lightweight, interpretable programs. The result is a 25 ~ 50 times reduction in candidate elements for grounding, enabling precise action localization without attention dilution. Additionally, we propose a data annotation method and a two-turn dialogue training strategy that jointly optimizes Planner, Programmatic Filter, and Grounder in a unified framework. Experiments demonstrate state-of-the-art performance. On our low-level task grounding task, our approach dramatically increases grounding accuracy from 46.80% to 88.28%, highlighting its effectiveness.

Two-step linear-exponential architectures can be applied to incremental ADCs (IADC) to achieve high resolution. In the first step, the ADC works as a normal first-order IADC while, in the second step, the exponential integrator is used to implement extended counting. There exist two architectures for the implementation of the exponential step, where the only difference depends on whether the input signal is connected or disconnected. By conducting a comparative analysis on such two slightly different linear-exponential architectures, we propose to combine the exponential and slope techniques to further boost the resolution without degrading its original thermal-noise suppression ability and DWA effectiveness. Mathematical analysis and simulation results are presented to confirm the principle of the proposed IADC.

This paper presents a two-phase linear-exponential incremental analog-to-digital converter (IADC) with using second-order noise coupling (NC). In the first phase, it works as a first-order IADC. Then the second-order NC path is activated in the second phase to significantly expedite the accumulation speed. Moreover, during the second phase, the integrator is disabled to achieve a large maximum stable amplitude (MSA). Simulations demonstrated that the proposed architecture could achieve a higher signal-to-quantization-noise ratio (SQNR) while avoiding the noise penalty and keeping the high effectiveness of data weighting averaging (DWA) compared with the prior art with using first-order NC. Mathematical analysis and further simulation results are presented to confirm the theory of the proposed structure.

With Moore's law reaching its limits, the use of new materials or new devices' structure has emerged as the next generation of CMOS devices. Among all, tunneling field-effect transistors (TFETs) have achieved a steep sub-threshold slope of less than 60mV/decade yet there is a lack of a complete process design kit (PDK) for large-scale circuit design. Hence, we present an open-source 15nm TFET PDK (15nmTFETPDK), which is based on FreePDK15 with additional support for open-source TFET models and its associated Cadence Virtuoso pcell in OA format and Mentor Calibre physical verification files. Our users can design their circuit in the Cadence Virtuoso platform and verify the consistency of the drawn layout and the circuit through the Calibre Platform. We hope that this open-source PDK allows them to further advance their research with new emerging devices.

This paper presents an adaptive anchor-point selection method for single image super-resolution (SR), which is based upon internal example-based SR model via locality constrained anchored neighborhood regression. The anchor points are fixed in anchored SR methods and are not flexible and customized for different input low-resolution (LR) images. To overcome this defect, we adaptively select anchor points via constructing customized training set for different input LR images, which can be realized by an internal example-based SR method. We introduce a locality-constrained anchored neighborhood regression to learn the relationship between LR space and high-resolution (HR) space. Extensive experimental results demonstrate that the performance of proposed method is competitive with several state-of-the-art SR methods.