A New Method to Solve Same-different Problems with Few-shot Learning

Visual learning of highly abstract concepts is often simple for humans but very challenging for machines. Same-different (SD) problems are a visual reasoning task with highly abstract concepts. Previous work has shown that SD problems are difficult to solve with standard deep learning algorithms, especially in the few-shot case, despite the ability of such algorithms to learn abstract features. In this thesis, we propose a new method to solve SD problems with few training samples, in which same-different visual concepts can be recognized by examining similarities between Regions of Interest by using a same-different twins network. Our method achieves state-of-the-art results on the Synthetic Visual Reasoning Test SD tasks and outperforms several strong baselines, achieving accuracy above 95% on several tasks and above 85% on average with only 10 training samples. On a few of these challenging SD tasks, our approach even outperforms reported human performance. We further evaluate the performance of our method outside of the synthetic tasks and achieve good performance on the MNIST, FashionMNIST and Face Recognition datasets

Oscillation theorems for certain third order nonlinear delay dynamic equations on time scales

In this paper, we establish some new oscillation criteria for the third order nonlinear delay dynamic equations $$\left(b(t)\left([a(t)(x^\Delta(t))^{\alpha_1}]^\Delta\right)^{\alpha_2}\right)^\Delta+q(t)x^{\alpha_3}(\tau(t))=0$$ on a time scale $\mathbb{T}$, where $\alpha_i$ are ratios of positive odd integers, $i=1,\ 2,\ 3,$ $b,\ a$ and $q$ are positive real-valued rd-continuous functions defined on $\mathbb{T}$, and the so-called delay function $\tau:\mathbb{T}\rightarrow \mathbb{T}$ is a strictly increasing function such that $\tau(t)\leq t$ for $t\in \mathbb{T}$ and $\tau(t)\rightarrow\infty$ as $t\rightarrow\infty.$ By using the Riccati transformation technique and integral averaging technique, some new sufficient conditions which insure that every solution oscillates or tends to zero are established. Our results are new for third order nonlinear delay dynamic equations and complement the results established by Yu and Wang in J. Comput. Appl. Math., 2009, and Erbe, Peterson and Saker in J. Comput. Appl. Math., 2005. Some examples are given here to illustrate our main results

Deciphering of interactions between platinated DNA and HMGB1 by hydrogen/deuterium exchange mass spectrometry

A high mobility group box 1 (HMGB1) protein has been reported to recognize both 1,2-intrastrand crosslinked DNA by cisplatin (1,2-cis-Pt-DNA) and monofunctional platinated DNA using trans-[PtCl2(NH3)(thiazole)] (1-trans-PtTz-DNA). However, the molecular basis of recognition between the trans-PtTz-DNA and HMGB1 remains unclear. In the present work, we described a hydrogen/deuterium exchange mass spectrometry (HDX-MS) method in combination with docking simulation to decipher the interactions of platinated DNA with domain A of HMGB1. The global deuterium uptake results indicated that 1-trans-PtTz-DNA bound to HMGB1a slightly tighter than the 1,2-cis-Pt-DNA. The local deuterium uptake at the peptide level revealed that the helices I and II, and loop 1 of HMGB1a were involved in the interactions with both platinated DNA adducts. However, docking simulation disclosed different H-bonding networks and distinct DNA-backbone orientations in the two Pt-DNA-HMGB1a complexes. Moreover, the Phe37 residue of HMGB1a was shown to play a key role in the recognition between HMGB1a and the platinated DNAs. In the cis-Pt-DNA-HMGB1a complex, the phenyl ring of Phe37 intercalates into a hydrophobic notch created by the two platinated guanines, while in the trans-PtTz-DNA-HMGB1a complex the phenyl ring appears to intercalate into a hydrophobic crevice formed by the platinated guanine and the opposite adenine in the complementary strand, forming a penta-layer π–π stacking associated with the adjacent thymine and the thiazole ligand. This work demonstrates that HDX-MS associated with docking simulation is a powerful tool to elucidate the interactions between platinated DNAs and proteins

Assessment of Long-Term Watershed Management on Reservoir Phosphorus Concentrations and Export Fluxes.

Source water nutrient management to prevent eutrophication requires critical strategies to reduce watershed phosphorus (P) loadings. Shanxi Drinking-Water Source Area (SDWSA) in eastern China experienced severe water quality deterioration before 2010, but showed considerable improvement following application of several watershed management actions to reduce P. This paper assessed the changes in total phosphorus (TP) concentrations and fluxes at the SDWSA outlet relative to watershed anthropogenic P sources during 2005⁻2016. Overall anthropogenic P inputs decreased by 21.5% over the study period. Domestic sewage, livestock, and fertilizer accounted for (mean ± SD) 18.4 ± 0.6%, 30.1 ± 1.9%, and 51.5 ± 1.5% of total anthropogenic P inputs during 2005⁻2010, compared to 24.3 ± 2.7%, 8.8 ± 10.7%, and 66.9 ± 8.0% for the 2011⁻2016 period, respectively. Annual average TP concentrations in SDWSA decreased from 0.041 ± 0.019 mg/L in 2009 to 0.025 ± 0.013 mg/L in 2016, a total decrease of 38.2%. Annual P flux exported from SDWSA decreased from 0.46 ± 0.04 kg P/(ha·a) in 2010 to 0.25 ± 0.02 kg P/(ha·a) in 2016, a decrease of 44.9%. The success in reducing TP concentrations was mainly due to the development of domestic sewage/refuse collection/treatment and improved livestock management. These P management practices have prevented harmful algal blooms, providing for safe drinking water

HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HYDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HYDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https://github.com/cyyever/aaai_hydra_8686

Landing dynamic simulation of aircraft landing gear with multi-struts

The landing dynamic modeling technology for aircraft landing gear is based on accurate evaluation of the landing gear landing performance. Aiming to study the post landing gear, a model for dynamic analysis of the gear is established based on the analysis of the structure mechanical features and the characteristics of landing dynamic performance. The landing dynamic analysis of strut landing gear is conducted by using LMS Motion software. According to the comparative analysis between simulation and drop test, the dynamic modeling method is accurate and reasonable. To obtain the load distribution of each landing gear, a full aircraft model of multi-strut landing gear is built, and then the dynamic simulation analysis is carried out in different landing process. The study shows that the rear main landing gear bears the highest proportion of load. The initial pitch angle influences load distribution of each landing gear. A lateral force is exerted on the main landing gear tire, when the plane is landing asymmetrically. With landing condition becoming stable, the lateral force is eliminated