Miracle-dz/UniFAR-FTUs huggingface dataset:
Consensus-based Distributed 3D Pose Estimation with Noisy Relative Measurements

In this paper we study consensus-based tributed estimation algorithms for estimating the global translation and rotation of each agent in a multi-agent system. We consider the case in which agents measure the noisy relative pose of their neighbors and communicate their estimates to agree upon the global poses in an arbitrary reference frame. The main contribution of this paper is a formal analysis that provides necessary and sufficient conditions to guarantee stability (in a Lyapunov sense) of the estimation system’s equilibria. We prove that consensus-based algorithms will diverge, even with arbitrarily small inconsistencies on the relative pose, unless the measurements satisfy minimum consistency conditions. We determine these consistency conditions for translation-only, rotation-only, and combined 3D pose estimation using the axis-angle rotation representation over undirected graphs. We then propose an initialization method based on these conditions that guarantees consistency and stability of the estimator’s equilibria. Additionally, we show that existing distributed estimation methods in literature exploit these conditions to guarantee convergence of their algorithms. Lastly, we perform simulations that show convergence when consistency conditions hold and divergence when they do not.
Rehabilitating the Regression : Honest and Valid Causal Inference through Machine Learning ∗

The linear regression suffers from several well-known flaws. First, specification choices by the researcher can affect inference. Second, the regression is primarily a correlative tool and generally does not estimate an average causal effect. We introduce a method that overcomes both shortcomings. First, the method combines a machine learning approach to control for background covariates with a regression to estimate the coefficient on the treatment variable of theoretical interest. Second, the method models the treatment variable as well as the outcome, allowing for the recovery of a causal effect. The method applies regardless of whether the treatment variable is a binary, continuous, or count variable. We prove that the method’s estimate is consistent for the average causal effect and that its standard errors are asymptotically valid and semiparametrically efficient. A simulation study and application to real-world datasets illustrates the method’s utility.
on P 2 P Credit Risk Assessment Model Based on RBM Feature Extraction — Take SME Customers as an Example

This paper combines the nonlinear dimensionality reduction method, and the Restricted Boltzmann machine (RBM algorithm), to assess the credit risk of P2P borrowers. After screening and processing many big data indicators, the most representative indicators are selected to build the P2P customer credit risk assessment model. In addition, after comparing the advantages and disadvantages of linear dimensionality reduction algorithm and nonlinear dimensionality reduction algorithm, this paper establishes a P2P enterprise customer credit risk assessment model based on RBM feature extraction combined with contrast divergence theory. It is concluded that the effect of RBM is better than that of PCA when the same model is selected. The Logistic model performs best in the three models when the same data feature extraction method is selected.
Population Health , and Epidemiology Informatics : Recent Research and Trends in the United States

Objectives: To survey advances in public and population health and epidemiology informatics over the past 18 months. Methods: We conducted a review of English-language research works conducted in the domain of public and population health informatics and published in MEDLINE or Web of Science between January 2015 and June 2016 where information technology or informatics was a primary subject or main component of the study methodology. Selected articles were presented using a thematic analysis based on the 2011 American Medical Informatics Association (AMIA) Public Health Informatics Agenda tracks as a typology. Results: Results are given within the context developed by Dixon et al., (2015) and key themes from the 2011 AMIA Public Health Informatics Agenda. Advances are presented within a socio-technical infrastructure undergirded by a trained, competent public health workforce, systems development to meet the business needs of the practice field, and research that evaluates whether those needs are adequately met. The ability to support and grow the infrastructure depends on financial sustainability. Conclusions: The fields of public health and population health informatics continue to grow, with the most notable developments focused on surveillance, workforce development, and linking to or providing clinical services, which encompassed population health informatics advances. Very few advances addressed the need to improve communication, coordination, and consistency with the field of informatics itself, as identified in the AMIA agenda. This will likely result in the persistence of the silos of public health information systems that currently exist. Future research activities need to aim toward a holistic approach of informatics across the enterprise.
Performance Measurement of Eulerian Kinetic Code on the Xeon Phi KNL

KNL Sandy Bridge • Xeon Phi 7250 (68 cores, 16GB MCDRAM) • 96GB DDR4 • Intel Compiler Ver.17. 0.1 – Option: -ipo -ip -O3 -xMIC-AVX512 • Number of grids: 40*40*40*128*64*2 (~28GB > MCDRAM) • Elapsed time for 5 time steps is measured • Memory mode: Flat, Cache • Cluster mode: All2All, Hemisphere, Quadrant, SNC-2, SNC-4 ⇒ Memory and Cluster modes are selected from BIOS • Default environmental variables ©Intel Basic equations for collisionless space plasma: • Maxwell equations (for electromagnetic wave propagations) Computational load less than 0.1% • Collisionless Boltzmann equation with electromagnetic field (known as Vlasov equation for charged particle motions) f(x, y, z, vx, vy, vz)
A reproductive mode so far unknown in African ranids : Phrynobatrachus guineensis GuiBÉ & LAMOTTE , 1961 breeds in tree holes

In Tai National Park, Ivory Coast, males and tadpoles of Phrynobatrachus guineensis GUIBÉ & LAMOTTE, 1961 were found together in water-filled tree holes. Large, macrolecithal eggs that were singly attached to the aperture of one of the tree holes, belonged to this species. The tadpole of P. guineensis is described for the first time. Furthermore, this is the first report on an African ranid species breeding in tree holes. The significance of the simultaneous presence of up to five male P. guineensis within a tree hole remains unresolved.
“ CLASSIFICATION OF RECORDED CLASSICAL MUSIC USING NEURAL NETWORKS ”

As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These features are statistically manipulated, making a total of 40 features. Regarding the task of genre modeling, we train a feedforward neural network (FFNN) with the LevenbergMarquardt algorithm. A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all the abovementioned five genres together. We obtained 85% classification accuracy in the three-class problem, 90% in the two-class problem and 76% in the five-class problem. These results are encouraging and show that the presented methodology may be a good starting point for addressing more challenging tasks.
Comparison of structural and physicochemical properties of starches from fi ve coarse grains

Starch, one of the most important components of coarse grains, has received widespread attention because of its prominent potential health benefits. In the present study, we isolated starches from the grains of sorghum, tartary buckwheat, common buckwheat, mungbean, and pea and studied their structural and physicochemical properties. These five starches all showed the distinctive “Maltese cross” effect (birefringence) but significantly differed in morphology, size, and complexity of granules. Sorghum starch exhibited the lowest amylose content and highest weight-average molar mass. Mungbean contained more short amylopectin [degree of polymerization (DP) 6–12=23.4%]. Pea starch exhibited the highest amylose content, highest amylopectin average chain length, and lowest weight-average molar mass. The starches of sorghum, tartary buckwheat, and common buckwheat showed A-type crystallinity, whereas those of mungbean and pea showed C-type crystallinity. Our results provide useful information for the application of coarse grain starches in diverse industries.
Impact of Intellectual Capital Disclosure on Market Cap

The study aims to empirically investigate, the impact of Intellectual capital (IC) on financial aspects of the organizational performance and on market capitalization. The study also aims to develop a descriptive framework of the components of the IC. Using the components of the intellectual capital as parameters, the contents of the annual reports of a sample of 50 companies from BSE100 companies are analyzed over a three-year period from 2008-2009. Regression models are used to examine the relationship between components of IC and the selected financial parameters. The study analyses whether there is any conclusive evidence to support a definitive association between IC and financial performance of the sample companies. The data is further analyzed to establish association of reporting norms and its impact on financial performance over a cross section of industries for disparity in reporting. The study is for the period before new IFRS /accounting standards is implemented in India. However certain companies in the sample have voluntarily started adopting the new IFRS norms. The study is restricted to examining the impact of increased IC related disclosures on the firm’s performance. The study will provide a new dimension to evaluate the performance of the firms for the stakeholders. This study is relevant in the current times when the disclosure norms are on the verge of a major change. The overall disclosure norms in India have been relatively low. The study explores the areas of IC disclosure that can be useful and provide relevant information to the users of the annual reports.
Unsupervised Risk Discovery for Child Welfare Screening

Child protection agencies across the US are routinely tasked with the screening millions of cases in order to identify those that are likely to result in adverse outcomes. To help with this task, many agencies have implemented and deployed risk prediction models that use administrative data in order to aid call workers. Nonetheless, the use of these predictive models for child welfare has proven onerous, raising concerns that go from how to validate their efficacy, to how to guarantee the fair treatment of disadvantaged groups. In particular, one concern that has inspired significant recent work is the fact that the variable of interest is never observed: we never see what happens to a child if the case was not investigated. In this work, we propose to tackle this obstacle by using unsupervised methods to discover latent risk structures in the agencies’ administrative data, completely bypassing the “missing labels” predicament. We formulate two different risk models (with and without time dependencies among calls) and show (1) that the risk structures learned by both models are meaningful for experts, and (2) that taking into account time dependencies ends up hurting the performance on downstream predictive tasks.
Fault Injection Test Harness

FITH (Fault Injection Test Harness) is a tool for validating driver robustness. Without changing existing code, it can intercept arbitrary MMIO/PIO access and IRQ handler in driver. Firstly I’ll first list the requirements and design for Fault Injection. Next, we discuss a couple of new generally useful implementation in FITH 1. KMMIO the ability to dynamically hook into arbitrary MMIO operations. 2. KIRQ the ability to hook into an arbitrary IRQ handler, Then I’ll demonstrate how the FITH can help developers to trace and identify tricky issues in their driver. Performance benchmark is also provided to show our efforts in minimizing the impact to system performance. At last, I’ll elaborate on current and future efforts and conclude.
SELECTIVE CHEMICAL INTERVENTION IN BIOLOGICAL SYSTEMS : THE SMALL MOLECULE TOOL , ( S )-(-)-BLEBBISTATIN

Selective small molecule inhibitors of protein function provide a method of studying biological processes that is often complementary to classical genetic and RNAi-based approaches. This article focuses on a recently identified small molecule known as blebbistatin. We review blebbistatin's discovery, biological characterization, selectivity and continuing use. Synthetic chemistry has played a key role in the blebbistatin story and we also review our recent work relating to the asymmetric synthesis and absolute stereochemical assignment of the active enantiomer, (S)-(-)-blebbistatin. High-throughput synthetic approaches to blebbistatin analogues are discussed and a novel analogue is described that has significantly improved physical properties for use in fluorescence-based imaging experiments on live cells. This article looks to emphasize the multidisciplinary nature of research projects in chemical genetics.
EEG based Emotion Recognition using SVM and LibSVM

In this paper, we solely focus on the EEG dataset. Using SVM classifier with external library LibSVM (3.23), we have classified our EEG SEED dataset and have achieved tremendous improvement in the accuracy and performance. Moreover, we have listed and explained the different approaches followed to improvise the performance and accuracy of our dataset. Further, we have also compared various existing approaches performed on our dataset using various classifiers - ELM, SVM with KNN, SVM with SEED dataset and SVM with DEAP dataset. By using library LibSVM (3.23), we increased the performance of each run by 4% finally resulting with 79.38% accuracy having tensor flow environment.
Ideas for Using Large-Scale Corpora to Improve Verification of Emotion Appropriateness in Japanese

We present a set of ideas for improving the verification of emotion appropriateness in Japanese language. Emotion appropriateness verification is a new method for discovering not only what are the emotions conveyed by a user in an utterance also whether they are appropriate for the context they are used in. We present ideas to improve this method with the use of several corpora. The corpora we plan to use are Amazon reviews, Web as corpus and two corpora of natural conversations to improve the method and provide conversational strategies for implementation of the method into a conversational agent.
Stable Fuzzy Control of an Electromagnetic Actuated Clutch

This paper presents aspects concerning the stable design of fuzzy control solutions for the position control of an electromagnetic actuated clutch. The mathematical modelling of the plant is first solved offering the plant models expressed as first principle nonlinear models, linearized models, dynamic Takagi-Sugeno fuzzy models and Tensor Product models derived from Linear Parameter-Varying models. The PI controllers are tuned by the Modulus Optimum method. The Takagi-Sugeno PI-fuzzy controllers are tuned on the basis of the modal equivalence principle which maps the parameters of the linear PI controllers onto the parameters of the fuzzy ones. The Takagi-Sugeno state feedback fuzzy controllers are designed by Parallel Distributed Compensation to obtain the state feedback gain matrices in the consequents of the rules. The stability of all fuzzy control systems is guaranteed in terms of deriving stability conditions expressed as Linear Matrix Inequalities (LMIs) and solving the LMIs.
CIM — still the solution for manufacturing industry Sev

In today’s economic environment, enterprises that solely rely on traditional technologies cannot meet dynamic customer demands. Hence, proactive enterprises seek the application of intelligent and integrated manufacturing systems in order to meet the customer’s demands and be the winners in the competitive market. Although, with available technologies and systems in Computer Integrated Manufacturing (CIM) and its related technologies, the application of CIM in manufacturing enterprises is a reality and can meet the need of the enterprises; today mangers in many enterprises are confused with varying technologies and new terminologies that prevail in the public domain. To make adoption and implementation issues complicated, there are many researchers pursing similar concepts but in different names to solve part of the issues that are or can be addressed by a CIM system. Therefore, this paper summarises the evolution of manufacturing technologies that are associated with developments towards a CIM system, and reviewed some of the new terminologies and technologies, which were proposed during the last four decades. This review is aimed at overcoming the confusion with the new terminologies that have been generated in the past four decades. Further, this paper articulates that all these new proposals are in deed the sub-system or sub-solutions of CIM. Finally, this article focuses on latest research developments in CIM and provides a stepwise justification methodology towards a CIM system for a small or a medium enterprise. r 2007 Published by Elsevier Ltd.
Methods of Assessing the Evolution of Science : A Review

Interest in the characterization and evolution of science has sparked a vast amount of literature. The manner in which this field of knowledge is depicted is highly diversified, giving rise to different styles of reporting the history of science. Notwithstanding the relevance of the subject for a broader understanding of how knowledge is produced, certified and appropriated, a simple and informative framework is missing. Proposing a simple categorization of the methods for assessing the evolution of science, our study highlights the changing process of scientific traditions, portraying the evolution of science as shifting towards hybrid and quantitative methodologies, namely those based on bibliometric techniques, as they arguably rely less on the judgments and perceptions of researchers, and have a higher degree of certainty.
The Influence of Transplanted Trees on Soil Microbial Diversity in Areas Affected by Coal Mining Subsidence of the Loess Plateau in China

Soil microbial diversity in areas affected by coal mining subsidence is closely associated 17 with vegetation restoration. In this study, we compared the diversity of soil bacteria and fungi under 18 different vegetation restoration modes in the subsidized Daliuta coal mining region in western China. 19 The dominant bacteria, Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria, were found 20 at abundances of 29.43–34.68%, 15.87–24.75%, 13.09–19%, and 12.06–15.36%, respectively. The 21 dominant fungi, Ascomycetes and Zygomycetes, had abundances of 23.96–71.08% and 10.42–56.26%, 22 respectively. The diversity indices (Sobs, Shannon, and Chao1) of the rhizosphere soil bacteria and 23 fungi were significantly lower in the primary Stipa breviflora phytocommunity than in the 24 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 September 2019 © 2019 by the author(s). Distributed under a Creative Commons CC BY license. 2 phytocommunities of transplanted trees. Among physicochemical soil parameters, total nitrogen (TN), 25 total phosphorus (TP), water content (WC), and pH affected soil bacterial diversity, and available 26 phosphorus (AP) and TN affected bacterial community structure the most. Furthermore, WC affected 27 soil fungal diversity, whereas TP and TN mostly affected the fungal community structure. However, 28 edaphic factors did not uniformly affect all microbial groups. Although TN, WC, and AP significantly 29 influenced the species richness of Actinobacteria and Proteobacteria (p<0.05), TP was significantly 30 negatively correlated with species richness in Acomycota, Basidiomycota, and Zygomycota (p<0.05). 31 However, TN influenced the species richness of Zygomycota (p<0.05) . Furthermore, we found that 32 the rhizosphere microbial diversity of the CK phytocommunity differed from that of the transplanted 33 tree communities in the study area; i.e., the transplanted trees promoted soil microbial diversity in 34 phytocommunities, and moreover, different edaphic factors varied in their effect on the community 35 composition of rhizosphere bacteria and fungi. We will continue to monitor the soil microbial diversity 36 in the study area with the goal to provide guidance for environmental remediation of areas affected by 37 coal mining subsidence. 38
StreetOCRCorrect: An Interactive Framework for OCR Corrections in Chaotic Indian Street Videos

Obtaining a high-quality OCR output in smart cities, with human-in-the-loop, is an interesting problem for surveillance and other similar applications. Achieving high accuracy while reading license plates in the real world videos is cumbersome due to complexities like multiple vehicles, high-density traffic in spatial and temporal domains, varying camera angles and illumination, occlusions and multiple resolutions. We present a modular framework for OCR corrections in the chaotic Indian traffic videos that especially involve complex license plate patterns. Such patterns are obtained from a state-of-the-art deep learning model trained on video frames. Since such a model reads the text from videos (instead of images), we incorporate multi-frame consensus for generating suggestions in our framework. To ease the correction process, our human-interactive framework first breaks down the multi-vehicle videos into multiple clips, each containing a single vehicle from the video using an object detector and a tracker. Our framework then provides suggestions for an individual vehicle using multi-frame consensus. Our framework then selectively presents these extracted clips to the user to verify/correct the predictions with minimal human efforts via interactive suggestions. Such high-quality output can be used to continuously update a large database for surveillance and can be further used to improve the accuracy of deep models in the complex real-world scenarios.