Interaction of smooth muscle calponin and desmin

AbstractInteraction of smooth-muscle calponin and desmin was analyzed by means of ultracentrifugation, fluorescent spectroscopy and affinity chromatography. At low and intermediate ionic strength (30–50 mM NaCl) calponin is cosedimented with desmin with an apparent dissociation constant 3–15 μM and stoichiometry of 1 calponin/4–6 desmin. Calmodulin decreases the quantity of calponin bound to desmin. Increase of ionic strength up to 150 mM weakens calponin-desmin interaction, but even at this ionic strength part of calponin remains bound to desmin. Calponin increases the rate and extent of fluorescence quenching induced by polymerization of 5-iodoacetamidofluorescein-labeled desmin. Affinity chromatography data indicate that desmin-binding sites are located in the N-terminal 22 kDa fragment of calponin. Since calponin interacts with desmin with an affinity comparable with that of, e.g., tropomyosin and myosin we suppose that calponin-desmin interaction may be important for cytoskeleton organization

Fast Adjustable NPN Classification Using Generalized Symmetries

NPN classification of Boolean functions is a powerful technique used in many logic synthesis and technology mapping tools in FPGA design flows. Computing the canonical form of a function is the most common approach of Boolean function classification. In this paper, a novel algorithm for computing NPN canonical form is proposed. By exploiting symmetries under different phase assignments and higher-order symmetries of Boolean functions, the search space of NPN canonical form computation is pruned and the runtime is dramatically reduced. The algorithm can be adjusted to be a slow exact algorithm or a fast heuristic algorithm with lower quality. For exact classification, the proposed algorithm achieves a 30× speedup compared to a state-of-the-art algorithm. For heuristic classification, the proposed algorithm has similar performance as the state-of-the-art algorithm with a possibility to trade runtime for quality

Rationale-Enhanced Language Models are Better Continual Relation Learners

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by large language models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.Comment: Accepted at EMNLP 202

Investigating on Through Glass via Based RF Passives for 3-D Integration

Due to low dielectric loss and low cost, glass is developed as a promising material for advanced interposers in 2.5-D and 3-D integration. In this paper, through glass vias (TGVs) are used to implement inductors for minimal footprint and large quality factor. Based on the proposed physical structure, the impact of various process and design parameters on the electrical characteristics of TGV inductors is investigated with 3-D electromagnetic simulator HFSS. It is observed that TGV inductors have identical inductance and larger quality factor in comparison with their through silicon via counterparts. Using TGV inductors and parallel plate capacitors, a compact 3-D band-pass filter (BPF) is designed and analyzed. Compared with some reported BPFs, the proposed TGV-based circuit has an ultra-compact size and excellent filtering performance

Guiding AMR Parsing with Reverse Graph Linearization

Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic graph into a sequence of nodes and edges and generate the linearized graph directly, have achieved good performance. However, we observed that these approaches suffer from structure loss accumulation during the decoding process, leading to a much lower F1-score for nodes and edges decoded later compared to those decoded earlier. To address this issue, we propose a novel Reverse Graph Linearization (RGL) enhanced framework. RGL defines both default and reverse linearization orders of an AMR graph, where most structures at the back part of the default order appear at the front part of the reversed order and vice versa. RGL incorporates the reversed linearization to the original AMR parser through a two-pass self-distillation mechanism, which guides the model when generating the default linearizations. Our analysis shows that our proposed method significantly mitigates the problem of structure loss accumulation, outperforming the previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 dataset, respectively. The code are available at https://github.com/pkunlp-icler/AMR_reverse_graph_linearization.Comment: Findings of EMNLP202

Binary Nonlinearization for AKNS-KN Coupling System

The AKNS-KN coupling system is obtained on the base of zero curvature equation by enlarging the spectral equation. Under the Bargmann symmetry constraint, the AKNS-KN coupling system is decomposed into two integrable Hamiltonian systems with the corresponding variables x, tn and the finite dimensional Hamiltonian systems are Liouville integrable