Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. This task requires not only to understand the context clues which play an important role in planning the plot but also to handle implicit knowledge to make a reasonable, coherent story. In this paper, we devise a novel model for story ending generation. The model adopts an incremental encoding scheme to represent context clues which are spanning in the story context. In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. Through building context clues and using implicit knowledge, the model is able to produce reasonable story endings. context clues implied in the post and make the inference based on it. Automatic and manual evaluation shows that our model can generate more reasonable story endings than state-of-the-art baselines.Comment: Accepted in AAAI201

A new genus and species of sabretooth, Oriensmilus liupanensis (Barbourofelinae, Nimravidae, Carnivora), from the middle Miocene of China suggests barbourofelines are nimravids, not felids

Since the early 2000s, a revival of a felid relationship for barbourofeline sabretooths has become popular due to recent discoveries of fragmentary fossils from Africa. According to this view, barbourofelines trace their common ancestor with felids through shared similarities in dental morphology going back to the early Miocene of Africa and Europe. However, whether or not such an idea is represented in the basicranial morphology, a conservative area of high importance in family-level relationships, is yet to be tested. A nearly complete skull of Oriensmilus liupanensis gen. and sp. nov. from the middle Miocene Tongxin Basin of northern China represents the most primitive known barbourofeline with an intact basicranial region, affording an opportunity to re-examine the relationship of felids and nimravines. We also present an update on East Asian records of barbourofelines. The new skull of Oriensmilus possesses a suite of characters shared with nimravines, such as the lack of an ossified (entotympanic) bullar floor, absence of an intrabullar septum, lack of a ventral promontorial process of the petrosal, presence of a small rostral entotympanic on the dorsal side of the caudal entotympanic, and a distinct caudal entry of the internal carotid artery and nerve that pierces the caudal entotympanic at the junction of the ossified and unossified caudal entotympanics. The absence of an ossified bullar floor in O. liupanensis and its presence in those from the middle Miocene of Sansan, France thus help to bracket the transition of this character, which must have happened in the early part of the middle Miocene. Spatial relationships between bullar construction and the middle ear configuration of the carotid artery in Oriensmilus strongly resemble those in nimravines but are distinctly different from felids and other basal feliforms. Despite the attractive notion that early barbourofelines arose from a Miocene ancestor that also gave rise to felids, the basicranial evidence argues against this view. http://zoobank.org/urn:http://lsid:zoobank.org:pub:2DE98DBC-4D02-4E18-9788-0B0D8587E73F

Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time. In our framework, each data instance is pushed into a cascade of deep neural networks with increasing sizes, and a selection module is used to sequentially determine when a sufficiently accurate classifier can be used for this data instance. The cascade of neural networks and the selection module are jointly trained in an end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between the computational cost and the predictive accuracy. Our method is able to simultaneously improve the accuracy and efficiency by learning to assign easy instances to fast yet sufficiently accurate classifiers to save computation and energy cost, while assigning harder instances to deeper and more powerful classifiers to ensure satisfiable accuracy. With extensive experiments on several image classification datasets using cascaded ResNet classifiers, we demonstrate that our method outperforms the standard well-trained ResNets in accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100 datasets and 66% on the ImageNet dataset, respectively

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest

Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling

We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.Comment: 5 pages, 2 figure