Cyanine-based molecules have gained significant attention in photothermal therapy due to their unique fluorescence brightness and tunable spectral properties. However, the development of new photothermal agents is often constrained by the complexity of the chemical landscape and the need for biocompatibility. To address these challenges, we present an innovative transfer learning approach for rapidly identifying promising photothermal agent candidates with excellent photothermal properties, high synthetic feasibility, and superior biocompatibility. Using natural language processing, our pretrained model generated a molecular library based on cyanine scaffolds. The most promising candidates were screened rigorously through a weighted analysis of chemical indicators, such as photothermal performance and synthetic accessibility and biological indicators, including bio-toxicity. From these, three molecules were selected for retrosynthetic analysis. This artificial intelligence-driven approach provides a robust solution to the traditional challenges in photothermal agent design, significantly enhancing their potential applications in cancer bioimaging, mitochondrial phototherapy, and image-guided surgery.

In a multicore system, effective management of shared last level cache (LLC), such as hardware/software cache partitioning, has attracted significant research attention. Some eminent progress is that Intel introduced Cache Allocation Technology (CAT) to its commodity processors recently. CAT implements way partitioning and provides software interface to control cache allocation. Unfortunately, CAT can only allocate at way level, which does not scale well for a large thread or program count to serve their various performance goals effectively. This paper proposes Dynamic Cache Allocation with Partial Sharing (DCAPS), a framework that dynamically monitors and predicts a multi-programmed workload's cache demand, and reallocates LLC given a performance target. Further, DCAPS explores partial sharing of a cache partition among programs and thus practically achieves cache allocation at a finer granularity. DCAPS consists of three parts: (1) Online Practical Miss Rate Curve (OPMRC), a low-overhead software technique to predict online miss rate curves (MRCs) of individual programs of a workload; (2) a prediction model that estimates the LLC occupancy of each individual program under any CAT allocation scheme; (3) a simulated annealing algorithm that searches for a near-optimal CAT scheme given a specific performance goal. Our experimental results show that DCAPS is able to optimize for a wide range of performance targets and can scale to a large core count.

Existing code designs for display-camera based visual communication all have an all-or-nothing behavior, i.e., they assume the entire code must be decoded. However, diverse operational conditions due to device hardware diversity (in camera resolution and frame rate) and distance range motivate more scalable designs. In this paper, we borrow the notion of hierarchical modulation from traditional RF communication, and design Strata, a layered coding scheme for visual communication. Strata can support a range of frame capture resolutions and rates, and deliver information rates correspondingly. Strata embeds information at multiple granularity into the same code area spatially or the same frame interval temporally. It ensures all layers are decodable independently, by controlling the amount of interference between adjacent layers. Further, our design is recursive and extends readily to generate more layers. Compared with existing codes, it significantly extends the operational range, though at the expense of less capacity than a single-layer code.