RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

Learning Analytics for Teacher’s Dashboard in a Course Result System

With the digital transformation of learning and the spread of online learning under the impact of COVID-19, huge amounts of learning data are being generated. How to utilize and analyze this learning data has led to a new field -- Learning Analytics (LA). LA demonstrates the collection, use and analysis of data generated by students in the learning process to predict student behavior and provide feedback. The goal of this thesis is to redesign a course result system (OSR) with LA functions. OSR is a results registration system used by the Department of Computer Science at Aalto University for over a decade. The development technology used by OSR is outdated and does not have the satisfying LA features for teachers. Meanwhile, this thesis tries to build a prototype of the new system based on the cloud platform (Salesforce). The research questions are: what is the state of art of the old system, what LA functions the new system needs, and what are the requirements of the new system. In order to answer these three research questions, this thesis first conducts a literature review. This thesis reviews the literature on OSR, A+, LA, cloud service models, and Salesforce. Through literature review and practical experience of OSR, this thesis obtains the state of art of the existing system as well as possible functional requirements and LA requirements. This thesis uses the constructive research method to design the new system. To gather functional needs and LA requirements from users, this thesis performed semi-structured interviews with teachers. After analyzing and summarizing the interview results, this thesis concludes the user needs. After comparing, merging and screening the requirements from the OSR and LA reviews in the Literature Review chapter, the requirements of the new system are summarized in this thesis. As for final requirements, the following three findings are made in this thesis. First of all, teachers are satisfied with the final grade calculation function and database function provided by the old system. The new system can follow the design of final grade calculation function of the old system. Second, although teachers were satisfied with most of the features of the old system, they spent a lot of time verifying the results. This suggests that they need a more reassuring system. Third, the old system provided only some statistical functions, and few teachers used them. Teachers hope the new system will provide them with more LA features, such as producing periodic reports for them to monitor students' progress in real time

Are Data-driven Explanations Robust against Out-of-distribution Data?

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts.Comment: In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 202

SMIL: Multimodal Learning with Severely Missing Modality

A common assumption in multimodal learning is the completeness of training data, i.e., full modalities are available in all training examples. Although there exists research endeavor in developing novel methods to tackle the incompleteness of testing data, e.g., modalities are partially missing in testing examples, few of them can handle incomplete training modalities. The problem becomes even more challenging if considering the case of severely missing, e.g., 90% training examples may have incomplete modalities. For the first time in the literature, this paper formally studies multimodal learning with missing modality in terms of flexibility (missing modalities in training, testing, or both) and efficiency (most training data have incomplete modality). Technically, we propose a new method named SMIL that leverages Bayesian meta-learning in uniformly achieving both objectives. To validate our idea, we conduct a series of experiments on three popular benchmarks: MM-IMDb, CMU-MOSI, and avMNIST. The results prove the state-of-the-art performance of SMIL over existing methods and generative baselines including autoencoders and generative adversarial networks. Our code is available at https://github.com/mengmenm/SMIL.Comment: In AAAI 2021 (9 pages

LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.Comment: Accepted for publication in Neurocomputing, 01/19/201

Linking PHYTOCHROME-INTERACTING FACTOR to Histone Modification in Plant Shade Avoidance

Shade avoidance syndrome (SAS) allows a plant grown in a densely populated environment to maximize opportunities to access to sunlight. Although it is well established that SAS is accompanied by gene expression changes, the underlying molecular mechanism needs to be elucidated. Here, we identify the H3K4me3/H3K36me3-binding proteins, Morf Related Gene (MRG) group proteins MRG1 and MRG2, as positive regulators of shade-induced hypocotyl elongation in Arabidopsis (Arabidopsis thaliana). MRG2 binds PHYTOCHROME-INTERACTING FACTOR7 (PIF7) and regulates the expression of several common downstream target genes, including YUCCA8 and IAA19 involved in the auxin biosynthesis or response pathway and PRE1 involved in brassinosteroid regulation of cell elongation. In response to shade, PIF7 and MRG2 are enriched at the promoter and gene-body regions and are necessary for increase of histone H4 and H3 acetylation to promote target gene expression. Our study uncovers a mechanism in which the shade-responsive factor PIF7 recruits MRG1/MRG2 that binds H3K4me3/H3K36me3 and brings histone-acetylases to induce histone acetylations to promote expression of shade responsive genes, providing thus a molecular mechanistic link coupling the environmental light to epigenetic modification in regulation of hypocotyl elongation in plant SAS

Evaluating the quantum optimal biased bound in a unitary evolution process

Seeking the available precision limit of unknown parameters is a significant task in quantum parameter estimation. One often resorts to the widely utilized quantum Cramer-Rao bound (QCRB) based on unbiased estimators to finish this task. Nevertheless, most actual estimators are usually biased in the limited number of trials. For this reason, we introduce two effective error bounds for biased estimators based on a unitary evolution process in the framework of the quantum optimal biased bound. Furthermore, we show their estimation performance by two specific examples of the unitary evolution process, including the phase encoding and the SU(2) interferometer process. Our findings will provide an useful guidance for finding the precision limit of unknown parameters.Comment: 11 pages, 3 figures, welcome comment

The critical roles and therapeutic implications of tuft cells in cancer

Tuft cells are solitary chemosensory epithelial cells with microvilli at the top, which are found in hollow organs such as the gastrointestinal tract, pancreas, and lungs. Recently, an increasing number of studies have revealed the chemotactic abilities and immune function of the tuft cells, and numerous efforts have been devoted to uncovering the role of tuft cells in tumors. Notably, accumulating evidence has shown that the specific genes (POU2F3, DCLK1) expressed in tuft cells are involved in vital processes related with carcinogenesis and cancer development. However, the interaction between the tuft cells and cancer remains to be further elucidated. Here, based on an introduction of biological functions and specific markers of the tuft cells, we have summarized the functional roles and potential therapeutic implications of tuft cells in cancers, including pancreatic cancer, lung cancer, gastric cancer, colon cancer, and liver cancer, which is in the hope of inspiring the future research in validating tuft cells as novel strategies for cancer therapies