Co-skeletons:Consistent curve skeletons for shape families

We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data

Metaheuristic Algorithms in Artificial Intelligence with Applications to Bioinformatics, Biostatistics, Ecology and, the Manufacturing Industries

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. We apply a newly proposed nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA) and demonstrate its flexibility and out-performance relative to its competitors in a variety of optimization problems in the statistical sciences. In particular, we show the algorithm is efficient and can incorporate various cost structures or multiple user-specified nonlinear constraints. Our applications include (i) finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, (ii) estimating parameters in a commonly used Rasch model in education research, (iii) finding M-estimates for a Cox regression in a Markov renewal model and (iv) matrix completion to impute missing values in a two compartment model. In addition we discuss applications to (v) select variables optimally in an ecology problem and (vi) design a car refueling experiment for the auto industry using a logistic model with multiple interacting factors

Low-resolution facial expression recognition: A filter learning perspective

Abstract(#br)Automatic facial expression recognition has attracted increasing attention for a variety of applications. However, the problem of low-resolution generally causes the performance degradation of facial expression recognition methods under real-life environments. In this paper, we propose to perform low-resolution facial expression recognition from the filter learning perspective. More specifically, a novel image filter based subspace learning (IFSL) method is developed to derive an effective facial image representation. The proposed IFSL method mainly includes three steps: Firstly, we embed the image filter learning into the optimization process of linear discriminant analysis (LDA). By optimizing the cost function of LDA, a set of discriminative image filters (DIFs) corresponding to different facial expressions is learned. Secondly, the images filtered by the learned DIFs are added together to generate the combined images. Finally, a regression learning technique is leveraged for subspace learning, where an expression-aware transformation matrix is obtained using the combined images. Based on the transformation matrix, IFSL effectively removes irrelevant information while preserving useful information in the facial images. Experimental results on several facial expression datasets, including CK+, MMI, JAFFE, SFEW and RAF-DB, show the superior performance of the proposed IFSL method for low-resolution facial expression recognition, compared with several state-of-the-art methods

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines

Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward functions are specified in a symbolic language captured by Reward Machines, an increasingly popular automaton-inspired structure. We are interested in the case where the mapping of environment state to a symbolic (here, Reward Machine) vocabulary -- commonly known as the labelling function -- is uncertain from the perspective of the agent. We formulate the problem of policy learning in Reward Machines with noisy symbolic abstractions as a special class of POMDP optimization problem, and investigate several methods to address the problem, building on existing and new techniques, the latter focused on predicting Reward Machine state, rather than on grounding of individual symbols. We analyze these methods and evaluate them experimentally under varying degrees of uncertainty in the correct interpretation of the symbolic vocabulary. We verify the strength of our approach and the limitation of existing methods via an empirical investigation on both illustrative, toy domains and partially observable, deep RL domains.Comment: NeurIPS Deep Reinforcement Learning Workshop 202

QueryForm: A Simple Zero-shot Form Entity Query Framework

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input.Comment: Accepted to Findings of ACL 202

The abundance and host-seeking behavior of culicine species (Diptera: Culicidae) and Anopheles sinensis in Yongcheng city, people's Republic of China

<p>Abstract</p> <p>Background</p> <p>The knowledge of mosquito species diversity and the level of anthropophily exhibited by each species in a region are of great importance to the integrated vector control. Culicine species are the primary vectors of Japanese encephalitis (JE) virus and filariasis in China. <it>Anopheles sinensis </it>plays a major role in the maintenance of <it>Plasmodium vivax </it>malaria transmission in China. The goal of this study was to compare the abundance and host-seeking behavior of culicine species and <it>An. sinensis </it>in Yongcheng city, a representative region of <it>P. vivax </it>malaria. Specifically, we wished to determine the relative attractiveness of different animal baits versus human bait to culicine species and <it>An. sinensis</it>.</p> <p>Results</p> <p><it>Culex tritaeniorhynchus </it>was the most prevalent mosquito species and <it>An. sinensis </it>was the sole potential vector of <it>P. vivax </it>malaria in Yongcheng city. There were significant differences (P < 0.01) in the abundance of both <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>collected in distinct baited traps. The relative attractiveness of animal versus human bait was similar towards both <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus</it>. The ranking derived from the mean number of mosquitoes per bait indicated that pigs, goats and calves frequently attracted more mosquitoes than the other hosts tested (dogs, humans, and chickens). These trends were similar across all capture nights at three distinct villages. The human blood index (HBI) of female <it>An. sinensis </it>was 2.94% when computed with mixed meals while 3.70% computed with only the single meal. 19:00~21:00 was the primary peak of host-seeking female <it>An. sinensis </it>while 4:00~5:00 was the smaller peak at night. There was significant correlation between the density of female <it>An. sinensis </it>and the average relative humidity (P < 0.05) in Wangshanzhuang village.</p> <p>Conclusions</p> <p>Pigs, goats and calves were more attractive to <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>than dogs, humans, and chickens. Female <it>An. sinensis </it>host-seeking activity mainly occurred from 19:00 to 21:00. Thus, we propose that future vector control against <it>An. sinensis </it>and <it>Cx. tritaeniorhynchus </it>in the areas along the Huang-Huai River of central China should target the interface of human activity with domestic animals and adopt before human hosts go to bed at night.</p