Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Inventory Conversion Period on Liquidity of Equity Securities of Companies at Nairobi Securities Exchange

Liquidity has a considerable influence on performance of equity securities of firms listed at the securities market. Liquidity is considered as the readiness, capability with which a security could either be sold or bought at securities exchange. Cash conversion period emanating from Working Capital elements is preferred as essential on determining the efficiency of transaction functions within the organization. Apart from internal managerial effects of individual firms on Cash Conversion Cycle that streamlines performance, liquidity as a measure of how the equity securities perform on the securities exchange market is relevant to investors since firms do fail because of illiquidity of assets. An organization’s success, rally on the investors’ matching of the internal and external managerial effects of the organization. Cash Conversion Period reflects internal effects and liquidity at the securities exchange provides external effects of the organization while trading at the securities market. This study was to determine the effect of Cash Conversion Period on Liquidity of Equity Securities of firms listed at Nairobi Securities Exchange. This study considered target population of the entire listed firms during the closing of the financial year 2016 and embraced a study period of ten years. The study applied panel data that consisted of secondary information for all companies found from publicized audited financial reports. Census technique was used on entire population of 61 companies since it was comparatively moderate and manageable. This study applied Descriptive research design anchored on pragmatism philosophy since the design could depict and describe the situation of the population the way it really was. E-views software was applied for descriptive and inferential statistical analysis and more so on fixed and random effects on panel data for regression analysis. Findings on Cash Conversion Period indicated a positive coefficient and significant probability value which meant a decrease or increase in Cash Conversion Period had a significant effect on Liquidity of Equity Securities at Nairobi Securities Exchange. This study recommended for further research on the independent variable since Cash Conversion Period was just but one of components of working capital components, hence results could be different when combined with other independent variables and the regressed on Liquidity of equity securities at the securities exchange market. Keywords: Liquidity, Cash Conversion Period, Firm Size, Liquidity DOI: 10.7176/RJFA/11-22-03 Publication date: November 30 th 2020
Decoder-Side Motion Vector Refinement in VVC: Algorithm and Hardware Implementation Considerations

This paper presents an overview of the decoder-side motion vector refinement (DMVR) algorithm in the Versatile Video Coding (VVC) standard. The proposed DMVR algorithm aims to increase the prediction accuracy of the blocks coded in merge mode using the bilateral matching-based refinement method. Compared with previous decoder-side motion vector derivation approaches, the proposed method significantly increases the coding efficiency without signaling additional side information. Furthermore, the hardware implementation considerations of the DMVR design are particularly focused in this study. This paper details and analyzes the novel features of DMVR contributing to the increase in coding efficiency and the reduction in computational complexity and implementation difficulty. Experimental results based on the VVC test model version 8.0 demonstrate that average Bjøntegaard Delta rate savings of 0.80 % and 2.81 % are achieved for the “tool-off” and “tool-on” test configurations, respectively. Moreover, 4 % additional decoding time and negligible additional external memory bandwidth requirements of DMVR based on the common test conditions for VVC are reported.
Source apportionment of PAHs in road sediments by CMB models: considering migration loss process

Certain gaps always exist between the pollution source apportionment model principles and the actual environmental processes and the efforts to narrow the gaps are always valuable. In this study, it was found that the source profiles calculated by positive matrix factor analysis (PMF) were not much similar to the measured source profiles of collected real pollution sources samples. Results suggested that this difference was due to the changes that happened in the process of pollutant transportation from sources to environmental media such as transportation loss and reactions. To simulate this process, in this study, a modified chemistry mass balance (CMB) application method was proposed. Selective consideration of atmospheric reaction kinetics of certain longdistance migratory sources, such as pollutants from industrial and coke ovens that are far from urban centers, improved the accuracy of CMB model analysis for source diagnostics of polycyclic aromatic hydrocarbons in road sediments. At the same time, sources close to the receptor, such as traffic sources and resident sources, were applied in the CMB model without considering attenuation.
My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism

Significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades. However, little research has been put to understand and interpret the black-box nature of these deep-learning based scoring models. Recent work shows that automated scoring systems are prone to even common-sense adversarial samples. Their lack of natural language understanding capability raises questions on the models being actively used by millions of candidates for life-changing decisions. With scoring being a highly multi-modal task, it becomes imperative for scoring models to be validated and tested on all these modalities. We utilize recent advances in interpretability to find the extent to which features such as coherence, content and relevance are important for automated scoring mechanisms and why they are susceptible to adversarial samples. We find that the systems tested consider essays not as a piece of prose having the characteristics of natural flow of speech and grammatical structure, but as `word-soups' where a few words are much more important than the other words. Removing the context surrounding those few important words causes the prose to lose the flow of speech and grammar, however has little impact on the predicted score. We also find that since the models are not semantically grounded with world-knowledge and common sense, adding false facts such as ``the world is flat'' actually increases the score instead of decreasing it.
Power Normalizations in Fine-Grained Image, Few-Shot Image and Graph Classification

Power Normalizations (PN) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature maps. Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling (SOP). As the main goal of this paper is to study Power Normalizations, we investigate the role and meaning of MaxExp and Gamma, two popular PN functions. To this end, we provide probabilistic interpretations of such element-wise operators and discover surrogates with well-behaved derivatives for end-to-end training. Furthermore, we look at the spectral applicability of MaxExp and Gamma by studying Spectral Power Normalizations (SPN). We show that SPN on the autocorrelation/covariance matrix and the Heat Diffusion Process (HDP) on a graph Laplacian matrix are closely related, thus sharing their properties. Such a finding leads us to the culmination of our work, a fast spectral MaxExp which is a variant of HDP for covariances/autocorrelation matrices. We evaluate our ideas on fine-grained recognition, scene recognition, and material classification, as well as in few-shot learning and graph classification.
A well-balanced positivity preserving cell-vertex finite volume method satisfying the discrete maximum-minimum principle for coupled models of surface water flow and scalar transport

We develop a new finite volume method using unstructured mesh-vertex grids for coupled systems modeling shallow water flows and solute transport over complex bottom topography. Novel well-balanced positivity preserving discretization techniques are proposed for the water surface elevation and the concentration of the pollutant. For the hydrodynamic system, the proposed scheme preserves the steady state of a lake at rest and the positivity of the water depth. For the scalar transport equation, the proposed method guarantees the positivity and a perfect balance of the scalar concentration. The constant-concentration states are preserved in space and time for any hydrodynamic field and complex topography in the absence of source terms of the passive pollutant. Importantly and this is one of the main features of our approach is that the novel reconstruction techniques proposed for the water surface elevation and concentration satisfy the discrete maximum-minimum principle for the solute concentration. We demonstrate, in a series of numerical tests, the well-balanced and positivity properties of the proposed method and the accuracy of our techniques and their potential advantages in predicting the solutions of the shallow water-transport model.
A Comparison Between Meshless Radial Basis Function Collocation Method and Finite Element Method for Solving Poisson and Stokes Problems

Steady and unsteady Poisson and Stokes equations are solved using mesh dependent Finite Element Method and meshless Radial Basis Function Collocation Method to compare the performances of these two numerical techniques across several criteria. The accuracy of Radial Basis Function Collocation Method with multiquadrics is enhanced by implementing a shape parameter optimization algorithm. For the time-dependent problems, time discretization is conducted using Backward Euler Method. The performances are assessed over the accuracy, runtime, condition number, and ease of implementation criteria. Three kinds of errors were calculated; least square error, root mean square error and maximum relative error. To calculate the least square error while using meshless Radial Basis Function Collocation Method, a novel technique is implemented. Imaginary numerical solution surfaces are created and then the volume between those imaginary surfaces and the analytic solution surfaces is calculated, enabling a fair error calculation. Lastly, all solutions are put together and solution trends are observed over the number of solution nodes vs. runtime, accuracy vs. runtime, and accuracy vs. the number of nodes. The assessment indicates the criteria under which Finite Element Method perform better and those when Radial Basis Function Collocation Method outperforms its mesh dependent counterpart.
Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography

Neural network based algorithms has shown success in many applications. In image processing, Convolutional Neural Networks (CNN) can be trained to categorize facial expressions of images of human faces. In this work, we create a system that masks a student's face with a emoji of the respective emotion. Our system consists of three building blocks: face detection using Histogram of Gradients (HoG) and Support Vector Machine (SVM), facial expression categorization using CNN trained on FER2013 dataset, and finally masking the respective emoji back onto the student's face via homography estimation. (Demo: https://youtu.be/GCjtXw1y8Pw) Our results show that this pipeline is deploy-able in real-time, and is usable in educational settings.
A Neural Network with Plane Wave Activation for Helmholtz Equation

This paper proposes a plane wave activation based neural network (PWNN) for solving Helmholtz equation, the basic partial differential equation to represent wave propagation, e.g. acoustic wave, electromagnetic wave, and seismic wave. Unlike using traditional activation based neural network (TANN) or $sin$ activation based neural network (SIREN) for solving general partial differential equations, we instead introduce a complex activation function $e^{\mathbf{i}{x}}$, the plane wave which is the basic component of the solution of Helmholtz equation. By a simple derivation, we further find that PWNN is actually a generalization of the plane wave partition of unity method (PWPUM) by additionally imposing a learned basis with both amplitude and direction to better characterize the potential solution. We firstly investigate our performance on a problem with the solution is an integral of the plane waves with all known directions. The experiments demonstrate that: PWNN works much better than TANN and SIREN on varying architectures or the number of training samples, that means the plane wave activation indeed helps to enhance the representation ability of neural network toward the solution of Helmholtz equation; PWNN has competitive performance than PWPUM, e.g. the same convergence order but less relative error. Furthermore, we focus a more practical problem, the solution of which only integrate the plane waves with some unknown directions. We find that PWNN works much better than PWPUM at this case. Unlike using the plane wave basis with fixed directions in PWPUM, PWNN can learn a group of optimized plane wave basis which can better predict the unknown directions of the solution. The proposed approach may provide some new insights in the aspect of applying deep learning in Helmholtz equation.
Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval

Existing methods for open-retrieval question answering in lower resource languages (LRLs) lag significantly behind English. They not only suffer from the shortcomings of non-English document retrieval, but are reliant on language-specific supervision for either the task or translation. We formulate a task setup more realistic to available resources, that circumvents document retrieval to reliably transfer knowledge from English to lower resource languages. Assuming a strong English question answering model or database, we compare and analyze methods that pivot through English: to map foreign queries to English and then English answers back to target language answers. Within this task setup we propose Reranked Multilingual Maximal Inner Product Search (RM-MIPS), akin to semantic similarity retrieval over the English training set with reranking, which outperforms the strongest baselines by 2.7% on XQuAD and 6.2% on MKQA. Analysis demonstrates the particular efficacy of this strategy over state-of-the-art alternatives in challenging settings: low-resource languages, with extensive distractor data and query distribution misalignment. Circumventing retrieval, our analysis shows this approach offers rapid answer generation to many other languages off-the-shelf, without necessitating additional training data in the target language.
Modeling huge photoinduced spin polarons in intrinsic magnetic semiconductors

In intrinsic magnetic semiconductors, the absorption of a single photon can generate a spin polaron, whose magnetic moment reaches many thousands of Bohr magnetons. Here we investigate these huge photoinduced spin polarons, using Monte Carlo simulations. In antiferromagnetic semiconductors, photoinduced spin polarons are most efficiently generated in the whole temperature interval up to the phase transition, whereas in ferromagnetic semiconductors much larger spin polarons can be photoinduced, but only around the phase transition temperature. Because Monte Carlo simulations are computationally expensive, we developed an analytical model, based on Weiss field theory. Although the Weiss model does not provide as much information as a Monte Carlo simulation, such as spin texture and fluctuations, it yields formulas that can be used to estimate instantly the expected photoinduced spin polaron size in many intrinsic magnetic semiconductors.
More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach

Conditional Selective Inference (SI) has been actively studied as a new statistical inference framework for data-driven hypotheses. For example, conditional SI framework enables exact (non-asymptotic) inference on the features selected by stepwise feature selection (SFS) method. The basic idea of conditional SI is to make inference conditional on the selection event. The main limitation of existing conditional SI approach for SFS method is the loss of power due to over-conditioning for computational tractability. In this paper, we develop more powerful and general conditional SI method for SFS by resolving the over-conditioning issue by homotopy continuation approach. We conduct several experiments to demonstrate the effectiveness and efficiency of our proposed method.
Detection of the hard X-ray non-thermal emission from Kepler’s supernova remnant

We report the first robust detection of the hard X-ray emission in the 15--30\,keV band from Kepler's supernova remnant with the silicon PIN-type semiconductor detector of the hard X-ray detector (HXD-PIN) onboard Suzaku. The detection significance is 7.17 $\sigma$ for the emission from Kepler's entire X-ray emitting region. The energy spectrum is found to be well reproduced by a single power-law function with a photon index of $3.13^{+1.85+0.69}_{-1.52-0.36}$, where the first and second errors represent 90\%-statistical and systematic errors, respectively. The X-ray flux is determined to be 2.75$_{-0.77-0.82}^{+0.78+0.81}\times10^{-12}$\,erg\,s$^{-1}$\,cm$^{-2}$ in the 15--30\,keV band. The wider-band X-ray spectrum in the 3--30\,keV band, where the soft X-ray Suzaku/XIS spectrum is combined, shows that the non-thermal component does not have a significant X-ray roll-off structure. We find that the broad-band energy spectrum from the radio band, X-ray data of this work, and TeV upper limits can be reproduced with the one-zone leptonic model with a roll-off energy of $\nu_{\mathrm{roll}}=1.0\times10^{17}$\,Hz and magnetic field strength of $B>40\,\mathrm{\mu G}$. Application of the diagnostic method using indices in the soft and hard X-ray band to the data indicates that the maximum energy of the accelerated electrons in Kepler's SNR is limited by the age of the remnant. The indication is consistent with the results of the one-zone leptonic modeling.
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning

In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort. Due to complex interactions among traffic participants, however, it is very hard to achieve accurate prediction for a long horizon. To address this challenge, we propose prediction by anticipation, which views interaction in terms of a latent probabilistic generative process wherein some vehicles move partly in response to the anticipated motion of other vehicles. Under this view, consecutive data frames can be factorized into sequential samples from an action-conditional distribution that effectively generalizes to a wider range of actions and driving situations. Our proposed prediction model, variational Bayesian in nature, is trained to maximize the evidence lower bound (ELBO) of the log-likelihood of this conditional distribution. Evaluations of our approach with prominent AD datasets NGSIM I-80 and Argoverse show significant improvement over current state-of-the-art in both accuracy and generalization.
A recombinant cholera toxin b subunit variant (CTB-KDEL) exhibits unique colon mucosal healing effects that have therapeutic implications for inflammatory bowel disease.

S AND PRESENTATIONS Oral Presentations: National/International Meetings 1. Royal J, Baldauf K, Kouokam J, Matoba N. “Plant-made cholera toxin B subunit as a candidate oral immunotherapeutic agent against ulcerative colitis” Plant-Based Vaccines, Antibodies & Biologics. June 8 – 10, 2015, Lausanne, Switzerland. 2. Royal J, Oh Y, Galandiuk S, Matoba N. “Epicertin, A Cholera Toxin B Subunit variant, Enhances Intestinal Would Healing in a Mouse Acute Colitis Model and Human Ulcerative Colitis Colon Explants via an Unfolded Protein Response” Digestive Disease Week 2018, June 2 – 5, 2018, Washington, DC. Oral Presentations: Local/Regional Meetings 3. Royal J, Baldauf K, Matoba N. “Plant-Made Cholera Toxin B Subunit: A Candidate Oral Immunotherapeutic Agent Enhances Colonic Mucosal Wound Healing” Center for Predictive Medicine Retreat, November 2016, Henryville, IN. Poster Presentations: National/International Meetings 1. Royal J, Baldauf K, Yaddanapudi K, Dryden GW, Matoba N. “Oral Administration of a Cholera Vaccine Antigen Facilitates Colonic Mucosal Healing in a Murine Model of Colitis” Digestive Disease Week 2017, Chicago, IL, May 6 – 9, 2017. 2. Royal J, Youngjun Oh, Galandiuk S, Matoba N. “Epicertin Enhances Intestinal Wound Healing In A Mouse Colitis Model And Human Ulcerative Colitis Colon Explants” Crohn’s and Colitis Congress, 2018, Las Vegas, NV Poster Presentations: Local/Regional Meetings 1. Royal J, Nelson B, Baldauf K, Kouokam C, Matoba N. “Plant-Made Cholera Toxin B Subunit, an Orally Active Anti-inflammatory Protein, in an Acute Colitis Mouse Model: Investigation of Effective Dose and Time of Administration” Research Louisville 2014, Louisville, KY 2. Royal J, Husk A, Hamorsky K, Bennet L, Matoba N. “Plant-Made Anti-TNFα Monoclonal Antibody; an Infliximab Biosimilar against Intestinal Inflammation and
Objective Bayesian analysis for the differential entropy of the Gamma distribution

The present paper introduces a fully objective Bayesian analysis to obtain the posterior distribution of an entropy measure. Notably, we consider the gamma distribution, which describes many natural phenomena in physics, engineering, and biology. We reparametrize the model in terms of entropy, and different objective priors are derived, such as Jeffreys prior, reference prior, and matching priors. Since the obtained priors are improper, we prove that the obtained posterior distributions are proper and that their respective posterior means are finite. An intensive simulation study is conducted to select the prior that returns better results regarding bias, mean square error, and coverage probabilities. The proposed approach is illustrated in two datasets: the first relates to the Achaemenid dynasty reign period, and the second describes the time to failure of an electronic component in a sugarcane harvest machine.
Stochastic Approximation for Online Tensorial Independent Component Analysis

Independent component analysis (ICA) has been a popular dimension reduction tool in statistical machine learning and signal processing. In this paper, we present a convergence analysis for an online tensorial ICA algorithm, by viewing the problem as a nonconvex stochastic approximation problem. For estimating one component, we provide a dynamics-based analysis to prove that our online tensorial ICA algorithm with a specific choice of stepsize achieves a sharp finite-sample error bound. In particular, under a mild assumption on the data-generating distribution and a scaling condition such that d/T is sufficiently small up to a polylogarithmic factor of data dimension d and sample size T , a sharp finite-sample error bound of Õ( √ d/T ) can be obtained.