Miracle-dz/UniFAR-FTUs huggingface dataset:
State Estimation using the Particle Filter with Mode Tracking by

A particle filter is a data assimilation scheme that employs a fully nonlinear, non-Gaussian analysis step. Unfortunately as the size of the state grows the number of ensemble members required for the particle filter to converge to the true solution increases exponentially. To overcome this Vaswani [Vaswani N. , 2008, IEEE Transactions on Signal Processing, 56:4583-4597] proposed a new method known as mode tracking to improve the efficiency of the particle filter. When mode tracking, the state is split into two subspaces. One subspace is forecast using the particle filter, the other is treated so that its values are set equal to the mode of the marginal pdf. There are many ways to split the state. One hypothesis is that the best results should be obtained from the particle filter with mode tracking when we mode track the maximum number of unimodal dimensions. The three dimensional stochastic Lorenz equations with direct observations are used to test this hypothesis. It is found that mode tracking the maximum number of unimodal dimensions does not always provide the best result. The best choice of states to mode-track depends on the number of particles used.
Rapid RNA sequencing data analysis using serverless computing

We have used serverless AWS Lambda functions to align 640 million reads in less than 3 minutes, a speed-up of 500x over the single-threaded implementation. Using a hybrid cloud architecture and software modified to optimize disk transfers, an entire RNA sequencing workflow transforming multiplexed reads to transcript counts that originally took 29 hours can be completed in 18 minutes. This is a 100x improvement over the original single threaded implementation and 12x faster than an optimized cloud server-based implementation using 16 threads. The total cost of the analyses is $2.82 for 96 wells or 3 cents per multiplexed sample. This approach can be used for human datasets that are generated for single experiments and does not rely on processing large numbers of samples to achieve the performance gains. The workflow is publicly available under a M.I. T. license (https://github.com/BioDepot/RNA-seq-lambda).
CU FIND CU FIND

We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved > 94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered differences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science.
Understanding the characteristics of mass spectrometry data through the use of simulation

Background Mass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments. Methods We developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basic operating characteristics of MALDI or SELDI instruments. Results The simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role in broadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height. Conclusions The model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool for generating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra. Availability http://bioinformatics.mdanderson. org/cromwell.html
Average case complexity of Voronoi diagrams of n sites from the unit cube

We consider the expected number of Voronoi vertices (or number of Delaunay cells for the dual structure) for a set of n i.i. d. random point sites chosen uniformly from the unit d-hypercube [0, 1] d . We show an upper bound for this number which is linear in n, the number of random point sites, where d is assumed to be a constant. This result matches the trivial lower bound of n. This is an open problem since several years. In 1991, Dwyer [2] showed that for a uniform distribution from the unit d-ball the average number of Voronoi vertices is linear in n and it is commonly assumed that this holds for any reasonable probability distribution.
Computer Science in Education

Computer science or computing science (sometimes abbreviated CS) is the learning of the theoretical foundations of information and computation, and of practical techniques for their execution and application in computer systems. It is often described as the efficient study of algorithmic processes that produce, explain, and transform information. In this work, I use Data Mining algorithms from the field of computer science for the analysis process to prove experimentally and practically that how reliable, efficient and fast are these for the analysis of data in education? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained results will be tested by applying the approach to the databases and data warehouses of different sizes with different threshold values. The results produce will be of different strength from short to the largest sets of data list. By this, we may get the results for different purposes e.g. making future education plan.
Reinforcement Learning for Autonomous Mobility-on-Demand Systems

Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework1 to control the rebalancing of AMoD systems through graph neural networks. Crucially, we demonstrate that graph neural networks enable reinforcement learning agents to recover behavior policies that are significantly more transferable, generalizable, and scalable than policies learned through other approaches. Empirically, we show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks such as inter-city generalization, service area expansion, and adaptation to potentially complex urban topologies.
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution (SR) without additional auxiliary image remains a constant challenge due to its high-dimensional spectral patterns, where learning an effective spatial and spectral representation is a fundamental issue. Recently, implicit neural representations (INRs) are making strides as a novel and effective representation, especially in the reconstruction task. Therefore, in this work, we propose a novel HSI reconstruction model based on INR which represents HSI by a continuous function mapping a spatial coordinate to its corresponding spectral radiance values. In particular, as a specific implementation of INR, the parameters of the parametric model are predicted by a hypernetwork that operates on feature extraction using a convolution network. It makes the continuous functions map the spatial coordinates to pixel values in a content-aware manner. Moreover, periodic spatial encoding is deeply integrated with the reconstruction procedure, which makes our model capable of recovering more high-frequency details. To verify the efficacy of our model, we conduct experiments on three HSI datasets (CAVE, NUS, and NTIRE2018). Experimental results show that the proposed model can achieve competitive reconstruction performance in comparison with the state-of-the-art methods. In addition, we provide an ablation study on the effect of individual components of our model. We hope this article could serve as a potent reference for future research.
PROCESS — AN ADAPTIVE-LEARNING-BASED APPROACH AND CASE STUDY

This chapter proposes a graph-model-based approach to prioritizing the test process. Tests are ranked according to their preference degrees which are determined indirectly, i.e., through classifying the events. For construction of the groups of events, an unsupervised neural network is trained by adaptive competitive learning algorithm. A case study demonstrates and validates the approach.
PLDL: a novel method for label distribution learning

: The nature, volume and orientation of data have been changed a lot in the last few years. The changed situation has beckoned data scientists to modify traditional algorithms and innovate new methods for processing new type of high volume, extremely complex data. One of the challenges is label ambiguity in the data, where the distribution of the significance of the labels matters. In this paper, a new method named Probabilistic Label Distribution Learning (PLDL) has been proposed for a computing degree of the belongingness. It is based on a proposed new Label Probability Density Function (LPDF) derived from Parzon estimate. The LPDF has been used in Algorithm Adoption K-Nearest Neighbors (AA-KNN) for Label Distribution Learning (LDL). Probability density estimators are used to estimate this ambiguity for each and every label. The overall degree of the belongingness of unseen instance has been evaluated on various real datasets. Comparative performance evaluation in terms of prediction accuracy of the proposed PLDL has been made with Algorithm adaptation KNN, Multilayer Perceptron, Levenberg-Marquardt neural network and layer recurrent neural for Label Distribution Learning. It has been observed that the increase in prediction accuracy for the proposed PLDL is highly statistically significant for most of the real datasets when compared with the standard algorithms for LDL.
Automatic Distinction of Arguments and Modiiers: the Case of Prepositional Phrases

The automatic distinction of arguments and modiiers is a necessary step for the automatic acquisition of subcategorisation frames and argument structure. In this work, we report on supervised learning experiments to learn this distinction for the diicult case of prepositional phrases attached to the verb. We develop statistical indicators of linguistic diagnostics for argumenthood, and we approximate them with counts extracted from an annotated corpus. We reach an accuracy of 86.5%,over a baseline of 74% , showing that this novel method is promising in solving this diicult problem.
Information Extraction based Approach for the NTCIR-10 1CLICK-2 Task

We describe a framework incorporating several information extraction methods for the NTCIR-10 One Click Access Task. Our framework first classifies a given query into pre-defined query types, and then extracts sentences and key-value pairs which seem relevant to the query. At this time, our framework adjusts the weights of the extraction methods depending on the judged class of the query. Finally, our framework summarizes ranked sentences and key-value pairs considering diversity.
of Soft Robots of Soft Robots of Soft Robots of Soft Robots of

R obotics has undergone a profound revolution in the past 50 years, moving from the laboratory and research institute to the factory and home. Kinematics and dynamics theories have been developed as the foundation for robot design and control, based on the conventional definition of robots: a kinematic chain of rigid links. Currently, the boundaries among materials, structures, biology, intelligence, and robotics are blurring. We have a much wider interpretation of what a robot is. The past decade has seen the increasing use of soft materials (Young’s modulus on the order of kilopascals to megapascals) to build robots, which are generally referred to as soft robots. This new generation of robots, originally inspired by natural lives, has grown rapidly and is enabling new robot abilities for applications ranging from wearable devices and biomedical engineering to search and rescue in unstructured environments [1]–[3]. Instead of relying on sliding or rolling motions as in conventional rigid robots, soft robots produce mobility based on the inherent compliance of soft materials. This fundamental change enables the integration of multiple functions into simple topologies by embedding actuators and sensors to build fully functional machines that can perform complex tasks. Here, the physical presence of soft robots plays a central role in generating adaptable behaviors. The body design Digital Object Identifier 10.1109/MRA.2020. 3024280
The Critical Success Factor Method: Establishing a Foundation for Enterprise Security Management

Abstract : Every organization has a mission that describes why it exists (its purpose) and where it intends to go (its direction). The mission reflects the organization's unique values and vision. Achieving the mission takes the participation and skill of the entire organization. The goals and objectives of every staff member must be aimed toward the mission. However, achieving goals and objectives is not enough. The organization must perform well in key areas on a consistent basis to achieve the mission. These key areas unique to the organization and the industry in which it competes can be defined as the organization's critical success factors. The critical success factor (CSF) method is a means for identifying these important elements of success. It was originally developed to align information technology planning with the strategic direction of an organization. However, in research and fieldwork undertaken by members of the Survivable Enterprise Management (SEM) team at the Software Engineering Institute, it has shown promise in helping organizations guide, direct, and prioritize their activities for developing security strategies and managing security across their enterprises. This report describes the critical success factor method and presents the SEM team's theories and experience in applying it to enterprise security management.
Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.
Lithospheric Alteration, Intraplate Crustal Deformation, and Topography in Eastern China

Topography can be directly observed, but what controls topographic variation is not readily known. To investigate a prominent and remarkably consistent along‐trend topographic gradient belt (TGB) in intraplate eastern China and its connection to ongoing deformation and geodynamics, we examine the regional density structure of the crust and mantle lithosphere. We first use S velocities to derive an initial density and then refine the density model by joint inversion of topography and gravity. We use the final density model and receiver functions to strip away topographic contributions from the crust, and we subsequently estimate the thickness of the mantle lithosphere required to satisfy flexural isostasy. Crustal buoyancy overcompensates for the elevation change across the TGB. East of the TGB (eastern north China craton and southeastern south China), the lithosphere is thin (~ 90 km), suggesting that not only the eastern north China craton but also much of the eastern China has experienced lithospheric dismemberment. The TGB behaves as an important contour for the lithospheric thickness, except for the northeast China and Qinling‐Dabie. Proposed refertilization of the altered mantle lithosphere in the east side of TGB would result in even lower lithospheric thickness, while hydration and asthenosphere upwelling have the opposite effect. This systematic calculation gives a regional analysis of eastern China and could be a useful guide for the regions lacking seismic investigations.
An Assessment of Secondary School Teachers Uses of ICT's: Implications for Further Development of ICT's Use in Nigerian Secondary Schools.

The use of ICTs in Nigeria and African countries generally is increasing and dramatically growing. However, while there is a great deal of knowledge about how ICTs are being used in developed countries, there is not much information on how ICTs are being used by teachers in developing countries. This study examined Nigeria secondary school teachers' uses of ICTs and its implications for further development of ICTs use in Nigerian secondary schools. The study through census drawn on 700 teachers from twenty five purposefully selected private secondary schools in Ibadan, Oyo state, Nigeria. This comprised 430 males and 270 females. Their age ranged from 25 - 45 years with a mean age of 35 years. A modified instrument tagged Teachers ICT use survey adapted from ICT survey indicator for teachers and staff by UNESCO (2004) and ICT Teachers Survey by New Zealand Ministry of Education MINEDU (1999) were used for the collection of data. The results showed that teachers generally have access to ICTs in their various schools except e-mail and Internet because their schools are not connected. Technical support are lacking in the schools and teachers lack of expertise in using ICT was indicated as being the prominent factors hindering teachers readiness and confidence of using ICTs during lesson. Furthermore, the results show that teachers perceived ICT as being easier and very useful in teaching and learning. For continuous uses of ICTs by teachers, it was recommended among others that teacher training and professional development oriented policies should support ICT-related teaching models that encourage both students and teachers to play an active role in teaching/learning activities. And that emphasis must be placed on the pedagogy behind the use of ICTs for teaching/learning.
Analyzing The Effect Of Top Management Support On Information Analyzing The Effect Of Top Management Support On Information System (IS) Performance Across Organizations And Industries System (IS) Performance Across Organizations And Industries Using Hierarchical Using Hierarchical

Top management support has long been conceivable as an important factor for the success of IS projects. Due to the hierarchical nature of an organization, a cross-level interaction can occur among nested levels. Thus, using inappropriate statistical analysis can cause misleading results and lost of information. This study provides two contributions to the IS research. First, Hierarchical Linear Modeling (HLM) was used to explain the cross-level interaction between organizational level and industry level. Second, unlike other studies focusing on an organizational level, this study considers top management support at the industry level and examines the mediating role of top management support between the two levels.
Hidden Structure: Using Network Methods to Map Product Architecture

In this paper, we describe an operational methodology for characterising the architecture of complex technical systems and demonstrate its application to a large sample of software releases. Our methodology is based upon directed network graphs, which allows us to identify all of the direct and indirect linkages between the components in a system. We use this approach to define three fundamental architectural patterns, which we label core-periphery, multi-core, and hierarchical. Applying our methodology to a sample of 1,286 software releases from 17 applications, we find that the majority of releases possess a “core-periphery” structure. This architecture is characterized by a single dominant cyclic group of components (the “Core”) that is large relative to the system as a whole as well as to other cyclic groups in the system. We show that the size of the Core varies widely, even for systems that perform the same function. These differences appear to be associated with different models of development—open, distributed organizations develop systems with smaller Cores, while closed, co-located organizations develop systems with larger Cores. Our findings establish some “stylized facts” about the fine-grained structure of large, real-world technical systems, serving as a point of departure for future empirical work.
Sports Analytics : Designing a Volleyball Game Analysis Decision-Support Tool Using Big Data

The data is so complex that no human can comprehensively conduct the analysis. A Computer-Aided Analysis Tool (CAAT) is needed to analyze the underlying trends contributing to the wins and losses as well as provide a meaningful recommendation to improve the overall team performance in a volleyball game. The CAAT determines the probability of each transition that can occur in a volleyball game, uses an Absorbing Markov Chain to evaluate how events influence the point scoring probability, and runs a Monte Carlo Simulation to analyze how random variations in transition probabilities, caused by extreme conditional scenarios can affect the team performance and end result of a game. Four design alternatives were identified through analysis of historic data and evaluated for improving team performance through specific skill improvement training: 1) Increasing aces; 2) Increasing kills; 3) Increasing blocks; 4) Decreasing errors. A utility analysis was conducted to determine the most effective design alternative to achieve the target level of performance. Based on the utility analysis, the GMU's women's and men's teams must focus on increasing their blocks. Out of 10 blocks, at least 9 should lead to a point for the men and 3 should lead to a point for the women in order to achieve the target level of performance.