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In this paper, we introduce SLQS, a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures.

Artificial nucleic acid circuits with precisely controllable dynamic and function have shown great promise in biosensing, but their utility in molecular diagnostics is still restrained by the inability to process genomic DNA directly and moderate sensitivity. To address this limitation, we present a CRISPR-Cas–powered catalytic nucleic acid circuit, namely, CRISPR-Cas–only amplification network (CONAN), for isothermally amplified detection of genomic DNA. By integrating the stringent target recognition, helicase activity, and trans-cleavage activity of Cas12a, a Cas12a autocatalysis-driven artificial reaction network is programmed to construct a positive feedback circuit with exponential dynamic in CONAN. Consequently, CONAN achieves one-enzyme, one-step, real-time detection of genomic DNA with attomolar sensitivity. Moreover, CONAN increases the intrinsic single-base specificity of Cas12a, and enables the effective detection of hepatitis B virus infection and human bladder cancer–associated single-nucleotide mutation in clinical samples, highlighting its potential as a powerful tool for disease diagnostics.

The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamically change from time to time. We study a generalized form of this problem, called the time-dependent shortest-path problem. A time-dependent graph GT is a graph that has an edge-delay function, wi,j(t), associated with each edge (vi, v j ), to be stored in a database. The edge-delay function w i,j (t) specifies how much time it takes to travel from node v i to node v j , if it departs from v i at time t. A user-specified query is to ask the minimum-travel-time path, from a source node, vs, to a destination node, ve, over the time-dependent graph, GT , with the best departure time to be selected from a time interval T . We denote this user query as LTT(v s , v e , T ) over G T . The challenge of this problem is the added complexity due to the time dependency in the time-dependent graph. That is, edge delays are not constants, and can vary from time to time. In this paper, we propose a novel algorithm to find the minimum-travel-time path with the best departure time for a LTT(v s , v e , T ) query over a large graph G T . Our approach outperforms existing algorithms in terms of both time complexity in theory and efficiency in practice. We will discuss the design of our algorithm, together with its correctness and complexity. We conducted extensive experimental studies over large graphs and will report our findings.

We demonstrate a temperature sensor based on surface plasmon resonances supported by photonic crystal fibers (PCFs). Within the PCF, to enhance the sensitivity of the sensor, the air holes of the second layer are filled with a large thermo-optic coefficient liquid and some of those air holes are selectively coated with metal. Temperature variations will induce changes of coupling efficiencies between the fundamental core mode and the plasmonic mode, thus leading to different loss spectra that will be recorded. In this paper, variations of the dielectric constants of all components, including the metal, the filled liquid, and the fused silica, are considered. We conduct numerical calculations to analyze the mode profile and evaluate the power loss, demonstrating a temperature sensitivity as high as 720 pm/°C.