Non-Autoregressive Math Word Problem Solver with Unified Tree Structure

Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description. However, such structures fail to handle the variants that can be derived via mathematical manipulation, e.g., $(a_1+a_2) * a_3$ and $a_1 * a_3+a_2 * a_3$ can both be possible valid solutions for a same problem but formulated as different expression sequences or trees. The multiple solution variants depicting different possible solving procedures for the same input problem would raise two issues: 1) making it hard for the model to learn the mapping function between the input and output spaces effectively, and 2) wrongly indicating \textit{wrong} when evaluating a valid expression variant. To address these issues, we introduce a unified tree structure to present a solution expression, where the elements are permutable and identical for all the expression variants. We propose a novel non-autoregressive solver, named \textit{MWP-NAS}, to parse the problem and deduce the solution expression based on the unified tree. For evaluating the possible expression variants, we design a path-based metric to evaluate the partial accuracy of expressions of a unified tree. The results from extensive experiments conducted on Math23K and MAWPS demonstrate the effectiveness of our proposed MWP-NAS. The codes and checkpoints are available at: \url{https://github.com/mengqunhan/MWP-NAS}.Comment: Accepted at EMNLP202

Dual Recognition Strategy for Specific and Sensitive Detection of Bacteria Using Aptamer-Coated Magnetic Beads and Antibiotic-Capped Gold Nanoclusters

Food poisoning and infectious diseases caused by pathogenic bacteria such as Staphylococcus aureus (SA) are serious public health concerns. A method of specific, sensitive, and rapid detection of such bacteria is essential and important. This study presents a strategy that combines aptamer and antibiotic-based dual recognition units with magnetic enrichment and fluorescent detection to achieve specific and sensitive quantification of SA in authentic specimens and in the presence of much higher concentrations of other bacteria. Aptamer-coated magnetic beads (Apt-MB) were employed for specific capture of SA. Vancomycin-stabilized fluorescent gold nanoclusters (AuNCs@Van) were prepared by a simple one-step process and used for sensitive quantification of SA in the range of 32–10⁸ cfu/mL with the detection limit of 16 cfu/mL via a fluorescence intensity measurement. And using this strategy, about 70 cfu/mL of <i>SA</i> in complex samples (containing 3 × 10⁸ cfu/mL of other different contaminated bacteria) could be successfully detected. In comparison to prior studies, the developed strategy here not only simplifies the preparation procedure of the fluorescent probes (AuNCs@Van) to a great extent but also could sensitively quantify SA in the presence of much higher concentrations of other bacteria directly with good accuracy. Moreover, the aptamer and antibiotic used in this strategy are much less expensive and widely available compared to common-used antibodies, making it cost-effective. This general aptamer- and antibiotic-based dual recognition strategy, combined with magnetic enrichment and fluorescent detection of trace bacteria, shows great potential application in monitoring bacterial food contamination and infectious diseases