SETI: Systematicity Evaluation of Textual Inference

We propose SETI (Systematicity Evaluation of Textual Inference), a novel and comprehensive benchmark designed for evaluating pre-trained language models (PLMs) for their systematicity capabilities in the domain of textual inference. Specifically, SETI offers three different NLI tasks and corresponding datasets to evaluate various types of systematicity in reasoning processes. In order to solve these tasks, models are required to perform compositional inference based on known primitive constituents. We conduct experiments of SETI on six widely used PLMs. Results show that various PLMs are able to solve unseen compositional inferences when having encountered the knowledge of how to combine primitives, with good performance. However, they are considerably limited when this knowledge is unknown to the model (40-100% points decrease). Furthermore, we find that PLMs can improve drastically once exposed to crucial compositional knowledge in minimalistic shots. These findings position SETI as the first benchmark for measuring the future progress of PLMs in achieving systematicity generalization in the textual inference.Comment: Accepted to Findings of ACL202

Discounted stochastic games, the 3M property and stationary Markov perfect equilibria

We show that all discounted stochastic games DSGs satisfying the usual assumptions have Nash payoff selection correspondences having fixed points. Our fixed point result is surprising because it is well known that Nash payoff selection correspondences are badly behaved, being in general neither convex valued nor closed valued in the appropriate topologies (in this case the weak star topologies). Here we show that because all DSGs satisfying the usual assumptions have upper Caratheodory (uC) Nash (equilibrium) correspondences containing uC Nash sub-correspondences having the 3M property (defined here), these uC Nash sub-correspondences are continuum valued and therefore induce interval-valued uC player payoff sub-correspondences - and therefore, Caratheodory approximable uC player payoff sub-correspondences. Finally, because these uC player payoff sub-correspondences are Caratheodory approximable, their induced Nash payoff selection sub-correspondences have fixed points - implying that the DSGs to which they belong have stationary Markov perfect equilibria

Parameterized state-contingent games, 3M minimal Nash correspondences, and connectedness

Under mild assumptions on primitives, we show that all parameterized state-contingent games (PSGs) have upper Caratheodory (uC) Nash (equilibrium) correspondences which contain minimal uC Nash correspondences having the 3M property (defined here). This implies that all PSGs have Nash correspondences made up of minimal uC Nash correspondences taking closed, connected, and essential Nash equilibrium values (essential in the sense of Fort, 1950). It then follows from Fu and Page (2022b), that because all PSGs have continuum valued minimal Nash correspondences, all PSGs have Caratheodory approximable Nash payoff correspondences - which in turn implies that all PSGs have approximable Nash payoff selection correspondences, and therefore have Nash payoff selection correspondences with fixed points

Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation

Despite the success of neural models in solving reasoning tasks, their compositional generalization capabilities remain unclear. In this work, we propose a new setting of the structured explanation generation task to facilitate compositional reasoning research. Previous works found that symbolic methods achieve superior compositionality by using pre-defined inference rules for iterative reasoning. But these approaches rely on brittle symbolic transfers and are restricted to well-defined tasks. Hence, we propose a dynamic modularized reasoning model, MORSE, to improve the compositional generalization of neural models. MORSE factorizes the inference process into a combination of modules, where each module represents a functional unit. Specifically, we adopt modularized self-attention to dynamically select and route inputs to dedicated heads, which specializes them to specific functions. We conduct experiments for increasing lengths and shapes of reasoning trees on two benchmarks to test MORSE's compositional generalization abilities, and find it outperforms competitive baselines. Model ablation and deeper analyses show the effectiveness of dynamic reasoning modules and their generalization abilities

Order-Free RNN with Visual Attention for Multi-Label Classification

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.Comment: Accepted at 32nd AAAI Conference on Artificial Intelligence (AAAI-18

The inhibition effect of phage display peptides on E. coli 0157:H7

Abstract only availableE. coli 0157: H7 strain is a strong food borne pathogen that lives in the cattle GI tract. It has been estimated that 76 million or more food borne illness occurs in the United States every year. In the last few years, massive amounts of beef were recalled because of E. coli 0157:H7. The focus of this study was to determine the minimal concentration of phage producing strains of E. coli to inhibit the pathogen E. coli 0157:H7. These strains were selected by Dr. C. J. Fu to bind to E. coli 0157: H7. This will guide us to determine the potential application of peptides carried on the phage in animal science and human health areas as replacements of antibiotics. We used a micro plate test to find out if the phage/peptide could inhibit E. coli 0157:H7 pathogen. The wells contained E. coli 0157:H7 and a phage- producing E. coli BluKan strain in varying ratios. We determine the minimum ratio of phage-producing E. coli that would inhibit E. coli 0157: H7 growth. We assayed the presence of the different strains by selective plating on antibiotics containing media. Both strains were resistant to Tetracycline; however, E. coli Blukan was also resistant to Kanamycine while E. coli 0157: H7 was also resistant to Nalidixic acid. We identified the selected peptides that inhibited E. coli 0157:H7 growth.NSF-REU Biology & Biochemistr