Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Cross-Validation on the Output of Multiple Testing Procedures

High dimensional data with sparsity is routinely observed in many scientific disciplines. Filtering out the signals embedded in noise is a canonical problem in such situations requiring multiple testing. The Benjamini–Hochberg procedure (BH) using False Discovery Rate control is the gold standard in large scale multiple testing. In Majumder et al. (2009) an internally cross-validated form of the procedure is used to avoid a costly replicate study and the complications that arise from population selection in such studies (i.e. extraneous variables). I implement this procedure and run extensive simulation studies under increasing levels of dependence among parameters and different data generating distributions and compare results with other common techniques. I illustrate that the internally cross-validated Benjamini–Hochberg procedure results in a significantly reduced false discovery rate, while maintaining a reasonable, though increased, false negative rate, and in a reduction to inherent variability under strong dependence structures when compared with the usual Benjamini–Hochberg procedure. In the discussion section, I describe some possibilities for relevant applications and future studies. Acknowledgements I must begin by thanking my thesis advisor Dr. Datta. Throughout the entire writing process he has been readily available and incredibly helpful. Whenever I would get stuck he always had a suggestion. Whenever I had questions he would have an explanation and references for further reading. Most of all, he was patient for which I am very thankful. I would also like to thank my committee members, Dr. Tipton and Dr. Zhang. Both professors have provided thoughtful and critical feedback, as well as encouragement throughout writing process. Their insights into improving the quality of my writing and probing questions challenged me to become a better student and Statistician. I am also thankful for my former coworkers at ASU-Beebe who encouraged me to return to college. The work we did compiling survey data during the grant writing process is part of the reason I decided to pursue this degree. I must also thank my family and friends. You all put up with me throughout my college career, and you’ve all encouraged me along the way. Pursuing a graduate degree is a lot of work, but it’s those people outside of school that are needed to keep students like myself grounded and sane. Thank you all for that. Josh Price Table of
Understanding the timing of cognitive processes with a variable rate neural code

Cognitive processes all require time, as they universally depend on information transmission between brain regions limited by physical and biological constraints. The time required for behavior also exhibits surprisingly lawful variation with task demands, success and failure, stimulus and response complexity, familiarity, practice and learning. Here we consider these regularities as consequences of constraints on information transmission, which we show provide rational predictions for timing effects across a surprising range of cognitive domains. We use a simple model for neural information transmission based on a variable-length rate coding model built with Poisson processes, Bayesian inference, and an entropybased decision threshold that simultaneously replicates a broad array of well-known reaction-time effects. By providing a principled connection between a high-level normative decision framework with time-dependent neural rate codes, we integrate several disjoint ideas in cognitive science through translating plausible constraints into information theoretic terms.
Hierarchy Response Learning for Neural Conversation Generation

The neural encoder-decoder models have shown great promise in neural conversation generation. However, they cannot perceive and express the intention effectively, and hence often generate dull and generic responses. Unlike past work that has focused on diversifying the output at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variational autoencoders. Specifically, a hierarchical response generation (HRG) framework is proposed to capture the conversation intention in a natural and coherent way. It has two modules, namely, an expression reconstruction model to capture the hierarchical correlation between expression and intention, and an expression attention model to effectively combine the expressions with contents. Finally, the training procedure of HRG is improved by introducing reconstruction loss. Experiment results show that our model can generate the responses with more appropriate content and expression.
Exploring Diverse Expressions for Paraphrase Generation

Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification. Recently, neural generative models have shown promising results in paraphrase generation. However, prior work mainly focused on single paraphrase generation, while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. Few works have been done to solve diverse paraphrase generation. In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. A reinforcement learning algorithm is applied to train our model. Our experiments on two real-world datasets demonstrate that our model not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.
Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training

One way to reduce network traffic in multi-node data-parallel stochastic gradient descent is to only exchange the largest gradients. However, doing so damages the gradient and degrades the model’s performance. Transformer models degrade dramatically while the impact on RNNs is smaller. We restore gradient quality by combining the compressed global gradient with the node’s locally computed uncompressed gradient. Neural machine translation experiments show that Transformer convergence is restored while RNNs converge faster. With our method, training on 4 nodes converges up to 1.5x as fast as with uncompressed gradients and scales 3.5x relative to single-node training.
A Highly Reliable Metadata Service for Large-Scale Distributed File Systems

Many massive data processing applications nowadays often need long, continuous, and uninterrupted data accesses. Distributed file systems are used as the back-end storage to provide the global namespace management and reliability guarantee. Due to increasing hardware failures and software issues with the growing system scale, metadata service reliability has become a critical issue as it has a direct impact on file and directory operations. Existing metadata management mechanisms can provide fault tolerance capability to some level but are inadequate. They often have limitations in system availability, state consistence, and performance overhead and lack an effective mechanism to offer metadata reliability. This paper introduces a novel highly reliable metadata service to address these issues in large-scale file systems. Different from traditional strategies, this proposed reliable metadata service adopts a new active-standby architecture for fault tolerance and uses a holistic approach to improve file system availability. A new shared storage pool (SSP) is designed for transparent metadata synchronization and replication between active and standby servers. Based on the SSP, a new policy called multiple actives multiple standbys (MAMS) is presented to perform metadata service recovery in case of failures. A new global state recovery strategy and a smart client fault tolerance mechanism are achieved to maintain the continuity of metadata service. We have implemented such highly reliable metadata service in a prototype file system CFS (Clover file system) and conducted extensive tests to evaluate it. Experimental results confirm that it can significantly improve file system reliability with fast failover under different failure scenarios while having negligible influence on performance. Compared with typical reliability designs in Hadoop Avatar, Hadoop HA, and Boom-FS file systems, the mean-time-to-recovery (MTTR) with the highly reliable metadata service was reduced by 80.23, 65.46 and 28.13 percent, respectively.
Multiple Futures Prediction

Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others. Towards these goals, we introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene. Our framework is data-driven and learns semantically meaningful latent variables to represent the multimodal future, without requiring explicit labels. Using a dynamic attention-based state encoder, we learn to encode the past as well as the future interactions among agents, efficiently scaling to any number of agents. Finally, our model can be used for planning via computing a conditional probability density over the trajectories of other agents given a hypothetical rollout of the ego agent. We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets.
FACIAL RECOGNITION, AUTONOMOUS LEARNING, BASED ON IMAGE FRAGMENTATION

This paper uses variations in the image fragmentation approach, considering the number of fragments, number of samples per person, number of hyperplanes and size in the region of interest. Computational vision techniques, in particular gradient histogram (HOG) as feature space descriptor, automatic learning methods, such as vector support machines (SVM) for model construction and a face detection algorithm, are used to replicate these experiments in a real time context. Tests are performed on a population of 40 people, samples taken in uncontrolled situations, in this way some states are discarded, refining the parameters until arriving at a configuration that complies with delivering the best results in this experiment and under these conditions.
Implementation of a Self-Enforcing Network to Identify Determinants of the WiFi Quality on German Highspeed Trains

—In this paper, we demonstrate how to analyze the WiFi data of the German highspeed trains called InterCityExpress (ICE) on the basis of a neural network. To achieve this, we apply a Self-Enforcing Network with cue validity factors to underline the importance of selected features. It is shown that the quality of the WiFi connection, in terms of the rate of downloads and the latency, can be grouped and explained by just a few determinants. We will show where the network coverage is especially good or bad and suggest ways to improve this quality to enhance the comfort of traveling on the highspeed trains and therefore to possible expand the proﬁts of the operating company.
Singularity analysis and dexterity performance on a novel parallel mechanism with kinematic redundancy

This article presents a novel spatial parallel mechanism with kinematic redundancy. The design strategy and evolution of the proposed mechanism is introduced, and kinematic model of the mechanism is established. To simplify singularity analysis of this kind of mechanism, the virtual plane method which can separate the whole parallel mechanism into two parts is presented. The relative Jacobian matrices are established and illustrated with singularity configurations of three types. Kinematic performance is obtained to see redundancy effects on the mechanism. The orientational workspace is obtained by the regions of orientational angles with varied platform position. It shows the orientational workspace of the redundant mechanism is significantly larger. Evaluation of condition number demonstrates the proposed mechanism can clearly stay away from singularities with a large range of rotational angles. A trajectory example is conducted to further prove the proposed mechanism can produce a large range of rotational angles without meeting with singularities.
Concentration of risk measures: A Wasserstein distance approach

Known finite-sample concentration bounds for the Wasserstein distance between the empirical and true distribution of a random variable are used to derive a two-sided concentration bound for the error between the true conditional value-at-risk (CVaR) of a (possibly unbounded) random variable and a standard estimate of its CVaR computed from an i.i. d. sample. The bound applies under fairly general assumptions on the random variable, and improves upon previous bounds which were either one sided, or applied only to bounded random variables. Specializations of the bound to sub-Gaussian and sub-exponential random variables are also derived. A similar procedure is followed to derive concentration bounds for the error between the true and estimated Cumulative Prospect Theory (CPT) value of a random variable, in cases where the random variable is bounded or sub-Gaussian. These bounds are shown to match a known bound in the bounded case, and improve upon the known bound in the sub-Gaussian case. The usefulness of the bounds is illustrated through an algorithm, and corresponding regret bound for a stochastic bandit problem, where the underlying risk measure to be optimized is CVaR.
Supervisory control of energy storage in Rubber Tyred Gantry cranes

Container terminals are crucial elements in the global trade of goods, however they are also responsible for massive greenhouse gases emissions. One of the key elements in a terminal is the Rubber Tyred Gantry (RTG) crane, which is used to stack containers in the yard. The energy efficiency of this machine can be greatly improved with the use of energy storage by taking advantage of the energy recovered when lowering a container. This thesis presents a study on supervisory control systems for energy storage, designed to determine the instantaneous power output that provides the best benefits with the limited resources provided by the energy storage device. Background research and data analysis provide indication on the energy flows in the system, leading to the development of an RTG crane model, which is validated using data measured on a real crane. The addition of the model of a flywheel energy storage system produces a hybrid RTG crane model, which is used to simulate three proposed supervisory control strategies developed specifically for this application. The characteristics of the supervisory control systems and results from the simulation are compared and analysed in detail in order to determine the quality of the control systems in a real application.
Cognitive Decision Support for Industrial Product Life Cycles: A Position Paper

Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps. Keywords–Product Life Cycle; Validation; Big Data Value Chain.
A Method of Cylindrical Panoramic Video Stitching

In this paper, we propose a cylindrical stitching method which based on template frame image to realize 360° panoramic video stitching. Firstly, we selected template frame to determine the camera parameters and distortion coefficient. And, removing the image distortion. After that, extracting the template frame feature and making rough matching with the feature points, and then eliminating the feature points of mismatching by the improved feature point screening method. Then, feature points are projected on the cylinder in advance for subsequent video image fusion. Finally, image fusion is carried out after the cylinder projection of the video image. The speed was greatly improved in the subsequent video splicing by using camera parameters, distortion coefficient and transformation matrix of template frame image. In addition, the CUDA platform was adopted for GPU parallel processing in order to further improve the speed to meet the real-time requirements. Finally, we realize real-time video stitching with 30 frames per second.
A New Pansharpening Method Using Objectness Based Saliency Analysis and Saliency Guided Deep Residual Network

Pansharpening is a fundamental and crucial task in the remote sensing community. For remote sensing images, there is a significant difference in demands for spatial and spectral resolution in different regions. From this perspective, we propose a new pansharpening method using objectness based saliency analysis and saliency guided deep residual network to boost the fusion accuracy. We first develop an objectness based saliency analysis by incorporating texture feature and objectness measurements to estimate saliency values in images and thereby help discriminate different demands for spatial improvement and spectral preservation. Inspired by the impressive performance of deep learning, we subsequently construct a saliency guided deep residual network to implement pansharpening. In addition, in order to produce images with subtler details, we design a new loss function, the normalized mean square error, particularly for the pansharpening task. Experiments support the superiority of our proposal over six competing methods.
The Effect of Online Customization on Consumers’ Happiness and Purchase Intention and the Mediating Roles of Autonomy, Competence, and Pride of Authorship

ABSTRACT Advanced information communication technology and production technologies such as 3D printing have enhanced consumersâ€™ personalized experiences. Customization as one of the noticeable personalization is attracting attention to both consumers and companies because of its significant leverage on transformation of production-consumption paradigm. This study aims to identify the effect of online customization experiences on consumersâ€™ happiness and behavioral intention. The findings suggest that customization experiences have a positive effect on happiness, and autonomy, competence, and pride of authorship play a mediating role between the two variables. In addition, happiness has a positive effect on purchase intention, and an indirect effect of happiness between online customization experience and purchase intention was found. This study suggests that creative activities via computers can bring happiness to consumers and increase purchase intention. Therefore, practitioners need to consider maximizing autonomy, competence, and pride of authorship to enhance consumersâ€™ happiness and purchase intention when consumers interact with computers.
Solving Interpretable Kernel Dimension Reduction

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing nonlinear relationships. The standard strategy is to first map the data into a high dimensional feature space using kernels prior to a projection onto a low dimensional space. While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret. Alternatively, Interpretable KDR (IKDR) is different in that it projects onto a subspace \textit{before} the kernel feature mapping, therefore, the projection matrix can indicate how the original features linearly combine to form the new features. Unfortunately, the IKDR objective requires a non-convex manifold optimization that is difficult to solve and can no longer be solved by eigendecomposition. Recently, an efficient iterative spectral (eigendecomposition) method (ISM) has been proposed for this objective in the context of alternative clustering. However, ISM only provides theoretical guarantees for the Gaussian kernel. This greatly constrains ISM's usage since any kernel method using ISM is now limited to a single kernel. This work extends the theoretical guarantees of ISM to an entire family of kernels, thereby empowering ISM to solve any kernel method of the same objective. In identifying this family, we prove that each kernel within the family has a surrogate $\Phi$ matrix and the optimal projection is formed by its most dominant eigenvectors. With this extension, we establish how a wide range of IKDR applications across different learning paradigms can be solved by ISM. To support reproducible results, the source code is made publicly available on \url{https://github.com/ANONYMIZED}.
An Improved Agent Strategy Training Method Based on DIAL

The traditional multi-agent method has a slow convergence speed and needs to train many parameter groups. If no algorithm optimization is performed, it takes a lot of time to reach a large number in order to achieve optimal results in the whole process. We optimize the parameter settings in the traditional algorithm training process, accelerate the convergence speed of the intelligent body in learning, and achieve the convergence effect faster.
Convolutional network-based method for wall-climbing robot direction angle measurement

This paper proposes a vision-based scheme for the 2D direction angle detection of wall-climbing robots. Firstly, we propose a method based on image geometric transformation to transform a camera image into a front view image of the wall, as the position and direction angle of the robot can be detected from the transformed image to eliminate the need for calibration of the camera’s internal and external parameters. Secondly, we propose the AngleNet model to detect the 2D direction angle of the wall-climbing robot. The mean and maximum error of predicted direction angle on the 556 samples of the test set are 1.1° and 3.8°, respectively. The single image processing time of AngleNet on the GPU is only 1.7 ms, which satisfies the demands of real-time processing. Thirdly, we propose a training sample expansion strategy, which greatly decreases the workload of annotating training samples for AngleNet. We also visualized the attention map of the trained AngleNet, which demonstrates that when performing direction angle detection, AngleNet relies on the visual features extracted from the wall-climbing robot rather than on the background.
Single-Use Paper Cups Circularity Improvement and Environmental Impact Mitigation Measures for Lappeenranta University of Technology Campus

This study examines the generation and treatment of disposable single-use paper cups at Lappeenranta University of Technology (LUT) campus located in Finland. The study was carried out within the LUT campus considering take-away venues and the waste collection system, with the intersection of the local waste treatment system. The University was considered as a closed system where different activities and services are taking place. This work contributes to a better understanding of newly adopted circularity measures and application possibilities. The research attempted to evaluate the environmental impacts, and reduction options of disposable single-use paper cups within the Lappeenranta University of Technology (LUT) campus, and measured the circularity indicator of single-use paper cups.