Spatial transcriptomics (ST) enables the joint profiling of gene expression and spatial localization, offering new insights into tissue microenvironments and biological processes. However, accurate spatial domain identification remains challenging due to the difficulty of effectively integrating multimodal data. To address this, we propose stDSGCL, a multi-view graph convolutional framework that models both spatial and transcriptomic information. stDSGCL constructs multiple spatial graphs from different distance metrics and embeds gene expression data into view-specific representations. These are refined via self-supervised contrastive learning and fused adaptively using an attention mechanism to enhance robustness and discriminative power. Experiments on 10x Visium datasets show that stDSGCL outperforms existing methods in clustering accuracy and spatial resolution, and generalizes well across platforms. The source code is available at: https://github.com/yumengg123/stDSGCL

This paper presents an ultra-low power 915 MHz Local Oscillator (LO) with adaptive frequency calibration algorithm for IoT Wake-Up Receivers (WURs). The proposed algorithm can adaptively adjust and minimize the calibration time of each bit oscillator tuning word (OTW) as well as the number of OTWs that require re-calibration after open-loop time. These two features help reducing the average LO power consumption by minimizing the closed-loop frequency calibration time. The duty cycle of the closed-loop time can also be adjusted flexibly to further reduce the average LO power consumption at a given frequency stability requirement. Fabricated in 40-nm CMOS, this proposed algorithm reduces the frequency calibration time from 108 μs to 42 μs, achieving a time reduction of 61%. An average LO power consumption of 93 μW at a 5% closed-loop time duty cycle is measured with a frequency accuracy of 120 ppm.

Large Language Models (LLMs) show constrained performance when confronted with a set of mathematical problems spanning various knowledge concepts. Unlike natural language tasks, the understanding and solution strategies for math problems significantly vary, presenting a great challenge for LLMs to consistently generate precise solutions for different problem types. To address this limitation, we propose UniSpLLM(A Universal Template integrated with Specific methods using Large Language Models), a strategy devised to bolster LLMs' efficiency in solving problems enriched with varied knowledge concepts. UniSpLLM innovates by crafting a Universal Template, versatile enough to accommodate any problem type. Specifically, we design six Specific Methods that can be adapted to different problem types. UniSpLLM distinguishes itself from other prompt-based approaches, which typically cater to a singular problem type. By achieving an improvement of nearly 15% on the latest dataset TAL_SAQ6K_EN from AAA12024 and surpassing the GPT-4 baseline by almost 17% on the MMLU-Math dataset, UniSpLLM significantly elevates the utility of LLMs within educational fields. Our approach and results hold significant educational value, as they aid students in acquiring diverse thinking skills tailored to various problem types.

Symmetric multi-tap transformers are designed, modeled, and characterized. Simple mathematical calculations are utilized to estimate the inductances and coupling coefficients from the physical parameters of a multi-tap transformer, which reduces the time required to develop an initial design. A broadband lumped-element model is then presented and its accuracy is demonstrated by comparing the extracted inductances from the model to Momentum simulation results. A concentric and an interleaved symmetric multi-tap transformer are designed in 0.13μm CMOS RF process and their self-inductances, coupling coefficients, and quality factors are evaluated.

This paper presents a reconfigurable multimode matching network employing a multi-tap transformer designed in 130 nm CMOS. By combining a multi-tap transformer and a bank of switched capacitors, it is shown that frequency matching in 2.4 GHz, 5 GHz, or 5-10 GHz bands, is achievable and thus supports multiple standards such as Bluetooth, 802.11a/b/g/n, WiMAX, and UWB.