Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations

This paper studies the layout design of a robotic mobile fulfillment system with multiple workstations. This is a parts-to-picker storage system where robots hoist pods and bring them directly to the workstations for stationary pickers to retrieve required items. As few research efforts have focused on determining the optimal locations of workstations in such systems, we develop an integer programming model to determine the location of workstations to minimize the total traveling distance of robots. In addition, we investigate the near-optimal workstation location patterns (i.e., some general workstation configuration rules) in the context of both traditional and flying-V layouts. A series of experiments led to the following findings: (1) the flying-V layout can save 8∼26% of travel distance compared with the traditional layout, and the sacrifice of space use is only 2∼3% for medium or large warehouses; (2) instead of solving the optimization model, the proposed 2n rule and n+1 rule are simple and easily implemented ways to locate workstations, with travel distance gaps of less than 1.5% and 5% for traditional and flying-V layouts, respectively; and (3) the “optimal” cross-aisle angle (i.e., θ) in flying-V layout can be set as large as possible as long as the cross-aisle intersects the left or right edge of the warehouse.
(Nearly) Dimension Independent Private ERM with AdaGrad Ratesvia Publicly Estimated Subspaces

We revisit the problem of empirical risk minimziation (ERM) with differential privacy. We show that noisy AdaGrad, given appropriate knowledge and conditions on the subspace from which gradients can be drawn, achieves a regret comparable to traditional AdaGrad plus a well-controlled term due to noise. We show a convergence rate of O ( Tr ( G T ) /T ) , where G T captures the geometry of the gradient subspace. Since Tr ( G T ) = O ( √ T ) we can obtain faster rates for convex and Lipschitz functions, compared to the O (1 / √ T ) rate achieved by known versions of noisy (stochastic) gradient descent with comparable noise variance. In particular, we show that if the gradients lie in a known constant rank subspace, and assuming algorithmic access to an envelope which bounds decaying sensitivity, one can achieve faster convergence to an excess empirical risk of (cid:101) O (1 /εn ) , where ε is the privacy budget and n the number of samples. Letting p be the problem dimension, this result implies that, by running noisy Adagrad, we can bypass the DP-SGD bound (cid:101) O ( √ p/εn ) in T = ( εn ) 2 / (1+2 α ) iterations, where α ≥ 0 is a parameter controlling gradient norm decay, instead of the rate achieved by SGD of T = ε 2 n 2 . Our results operate with general convex functions in both constrained and unconstrained minimization.
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations

This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations. We derive the generalization error bounds of two-layer neural networks in the framework of the DRM for solving two prototype elliptic PDEs: Poisson equation and static Schrödinger equation on the d -dimensional unit hypercube. Speciﬁcally, we prove that the convergence rates of generalization errors are independent of the dimension d , under the a priori assumption that the exact solutions of the PDEs lie in a suitable low-complexity space called spectral Barron space. Moreover, we give sufﬁcient conditions on the forcing term and the potential function which guarantee that the solutions are spectral Barron functions. We achieve this by developing a new solution theory for the PDEs on the spectral Barron space, which can be viewed as an analog of the classical Sobolev regularity theory for PDEs.
Efficient Algorithms for Learning from Coarse Labels

For many learning problems one may not have access to fine grained label information; e.g., an image can be labeled as husky, dog, or even animal depending on the expertise of the annotator. In this work, we formalize these settings and study the problem of learning from such coarse data. Instead of observing the actual labels from a set $\mathcal{Z}$, we observe coarse labels corresponding to a partition of $\mathcal{Z}$ (or a mixture of partitions). Our main algorithmic result is that essentially any problem learnable from fine grained labels can also be learned efficiently when the coarse data are sufficiently informative. We obtain our result through a generic reduction for answering Statistical Queries (SQ) over fine grained labels given only coarse labels. The number of coarse labels required depends polynomially on the information distortion due to coarsening and the number of fine labels $|\mathcal{Z}|$. We also investigate the case of (infinitely many) real valued labels focusing on a central problem in censored and truncated statistics: Gaussian mean estimation from coarse data. We provide an efficient algorithm when the sets in the partition are convex and establish that the problem is NP-hard even for very simple non-convex sets.
Comment on ‘The case for in situ resource utilization for oxygen production on Mars by non-equilibrium plasmas’

It was highlighted in a very recent article by Guerra et al (2017 Plasma Sources Sci. Technol. 26 11LT01) the creation of oxygen out of the Mars atmosphere is viable. As this idea is not entirely new, this work aims at closing the gaps between this new article and some former work done by the author of this comment.
Applications of X-Ray Microanalysis in Botanical Research

The applications of x-ray microanalysis in botanical research are reviewed, and the special problems posed in the preparation and analysis of botanical specimens and in the interpretation of results discussed. There are four main areas of research employing x-ray microanalysis: 1. effects of salinity on plants: examination of ion distributions in leaves and roots of both salt-tolerant and salt-sensitive higher plants and salt-tolerant algae and fungi; 2. effects of pollutants on plants: localization of heavy metals, particularly Pb, Cd, and Al, in algae, lichens and higher plants; 3. distributions of silicon in higher plants: silica distribution in roots and shoots, and medical implications of silica in caryopses of grasses; use of monosilicic acid as a water tracer; 4. elemental compositions of seeds bodies: elemental contents of seed ies from a variety of species, and Fe)+ on germination. and protein protein bodeffects of A number of miscellaneous applications of x-ray microanalysis are also discussed, including: 1. evaluation and development of preparation and analysis techniques; 2. lower plants: dinoflagellates, fungi, algae; 3. roots: ion uptake and transport; and 4. shoots: elemental distributions, chloroplasts, stomata, pulvini.
Grey Clustering Approach to Assess Sediment Quality in a Watershed in Peru

The evaluation of sediment quality is a relevant topic that involves the analysis of various parameters that are altered by natural or anthropogenic causes. Therefore, the Grey Clustering method provides an alternative to evaluate sediment quality. In the present study, the sediment quality of the Chontayacu river watershed was evaluated considering the results of the monitoring of twenty-three points carried out in the early evaluations by the Environmental Impact Evaluation Agency (OEFA by its Spanish acronym). These twenty-three points were separated into three blocks considering the monitoring points upstream of the Uchiza town center and the Chontayacu Alto and Chontayacu Bajo hydroelectric plants. Seven parameters were analyzed: As, Cd, Cr, Cu, Pb, Hg and Zn, which were compared with Canadian sediment quality standards for the protection of aquatic life. The results of the assessment showed that all points in the Chontayacu River were classified as having unlikely adverse biological effects from heavy metals. However, a quality ranking was established between the points of each block where it was found that points P3, P4 and P17 correspond to the lowest values for the high CH, low CH and CP Uchiza blocks respectively. Finally, the results obtained will provide integrated information for decision making by the competent authorities in Peru, as well as indicate the level of sediment contamination that should be taken into account for the proposal of hydroelectric projects that influence sediment transport and entrainment. Keywords—Grey clustering; sediment quality; watershed
Delivering Debt Relief through the Banking Sector: Lessons from the Irish Mortgage Market

How do banks design mortgage modifications when under regulatory expectation to issue them widely and quickly? Who do they allocate modifications to, and how deep are repayment cuts? Howmany modified loans end up re-defaulting? A large-scale Irish program allows us to tackle these questions using householdand loan-level data from 2012 to 2016. We show that the issuance of permanent modifications is not uniform across the credit risk distribution: those with a moderate ability to repay are more likely to receive a modification than those in deepest financial difficulty. Repayment cuts however are more generous as repayment capacity weakens, as long as borrowers have some surplus income available to service debt. Finally, deeper repayment cuts and lower payment-to-income ratios lower redefault rates, confirming prior evidence on the importance of liquidity for mortgage distress.
An Inexact Feasible Interior Point Method for Linear Optimization with High Adaptability to Quantum Computers

The use of quantum computing to accelerate complex optimization problems is a burgeoning research field. This paper applies Quantum Linear System Algorithms (QLSAs) to Newton systems within Interior Point Methods (IPMs) to take advantage of quantum speedup in solving Linear Optimization (LO) problems. Due to their inexact nature, QLSAs can be applied only to inexact variants of IPMs. Existing IPMs with inexact Newton directions are infeasible methods due to the inexact nature of their computations. This paper proposes an Inexact-Feasible IPM (IF-IPM) for LO problems, using a novel linear system to generate inexact but feasible steps. We show that this method has $\Ocal(\sqrt{n}L)$ iteration complexity, analogous to the best exact IPMs, where $n$ is the number of variables and $L$ is the binary length of the input data. Moreover, we examine how QLSAs can efficiently solve the proposed system in an iterative refinement (IR) scheme to find the exact solution without excessive calls to QLSAs. We show that the proposed IR-IF-IPM can also be helpful to mitigate the impact of the condition number when a classical iterative method, such as a Conjugate Gradient method or a quantum solver is used at iterations of IPMs. After applying the proposed IF-IPM to the self-dual embedding formulation, we investigate the proposed IF-IPM's efficiency using the QISKIT simulator of QLSA.
STABILITY ANALYSIS IN INDIAN MUSTARD (BRASSICA JUNCEA L. CZERN & COSS.) OVER NORMAL AND TERMINAL HEAT STRESS ENVIRONMENTS

An experimentation was conducted involving 39 genotypes of Indian mustard (Brassica juncea L. Czern & Coss.) comprising of 9 thermo-tolerant lines, 3 high yielding testers and their 27 F1 hybrids by sowing them at two locations (Varanasi and Mirzapur) and three dates of sowing (i.e. timely, late and very late sown) to identify genotypes which are stable and high yielding in overall and especially in poor (terminal heat stress) environment. The evaluation trials were laid out in randomized block design with 3 replications at each date of sowing at both the locations. All the genotypes were tested for 3 stability parameters i.e. mean, bi and S di. The analysis of variance (ANOVA) was conducted on eight characters (plant height, primary branches/plant, siliqua/plant, seeds/siliqua, test weight, seed yield/plant, oil content percent and oil yield tons/ha) for 6 environments individually as well as pooled over the environments. The pooled ANOVA revealed significant GxE interactions for seed yield and oil yield. The hybrid PM-28 × NRCHB-101 was revealed to be stable genotype for most of the characters including plant height, siliqua per plant, test weight and seed yield per plant. Among the parents, PM-26 was found stable for primary branches and test weight, RH-406 for test weight, RGN-236 for seed per siliqua, JD-6 for plant height and Giriraj for oil yield. These parental genotypes can be included in further breeding programs as they exhibited stable performance over the environments. The parents PM-26, PM-28, RH-406, RGN-229 and BPR-541-4 which were found stable for seed yield in the ‘poor’ (heat stress) environment (bi<1,S 2 d) could be exploited further in stress breeding programs or can be recommended for cultivation in late sown conditions where the crop faces heat stress in the terminal stage due to delay in sowing time. On the other hand, the hybrids such as PM-28 × NRCHB-101, JD-6 ×NRCHB-101, PM-28 × Vardan, RH-406 × NRCHB-101, RH-749 × Vardan and RGN-236 × NRCHB-101, which were found stable for more than a single character simultaneously, could be subjected to generation advancement for developing stable varieties across the environments.
Artificial staining of raw microscopy images using CycleGANs

Raw images from a microscope usually need processing in order to be inspected. Researchers use chemical agents to stain the samples and highlight the relevant structures that need to be examined, however this is a often a laborious, costly and risky procedure. In this thesis, a CycleGAN is used with the aim to automate the chemical staining of raw microscopy cell images. CycleGAN’s unsupervised learning approach is ideal, addressing the lack of available data sets in the process, so that grayscale cell images can be colorized digitally, avoiding the difficulties of chemical staining. We experiment with different techniques and examine whether they can lead to the best possible results. We find that the addition of spectral normalization stabilizes the training of the CycleGAN without adding computational cost. Moreover, we observe that by utilizing two different learning rates for the generators and discriminators as well as using asynchronous updates for the GAN components, we can reach a better convergence point and generate the best staining results, with our best experiment engulfing a structural similarity index measure (SSIM) of 0.881 and a color difference score give by the ∆E∗94 metric of 12.73. The cycle consistent nature of CycleGAN can be used to produce paired samples for use in other endeavors. Overall, the tuned up version of CycleGAN was capable of generating realistic samples, however there were obvious problems with the localization of the peroxisome stain even in our best set of results. Among the limitations were the lack of adequate computational resources plus the data suffered from ’color bleeding’, a phenomenon that causes stains to leak out of their target structures, making the learning of structures and by extension the staining, more challenging. Therefore, more data are needed, preferably with a reduced bleeding effect in order to get more faithful results.
INFLUENCE OF STUDENTS' AND TEACHERS' ATTITUDES ON ADOLESCENTS' STUDY HABITS IN SECONDARY SCHOOLS IN EDUCATIONAL DISTRICT II OF LAGOS STATE

This study investigated adolescents' characteristic features and teachers’ attributes and their influence on the study habits of adolescents in Secondary Schools (SSS 11) in Lagos State. I H. The sample comprised two hundred (200) students which comprised sixty-nine (69) science-inclined, seventy-one (71) commercial-inclined and sixty (60) art-inclined students. The data were collected using the researcher-made questionnaire. Descriptive and inferential statistics were employed in the analyses of the data. The appropriate statistical tools used to test the generated hypotheses were Pearson Product Moment Correlation coefficient (PPMC) to establish the magnitude and direction of the relationship between the two variables in hypotheses one and two, and oneway ANOVA to compare the mean effects of the existing groups in the variable. The mean and the standard deviation were also determined. All hypotheses were tested at 0.05 level of significance. Concerning teacher’s characteristics, the finding showed a calculated r-value of 0.45, which is positive and significant in magnitude than r-critical (r-crit. = 0.195). It indicated a positive relationship between teachers' characteristics and adolescents' study habits. For the students’ attitude, the calculated r-value of 0.36 is positively significant. It has further indicated a positive relationship between adolescents' attitudes and their study habits because both variables are correlated positively.
Inferential social learning: cognitive foundations of human social learning and teaching

Social learning is often portrayed as a passive process of copying and trusting others. This view, however, does not fully capture what makes human social learning so powerful: social information is often 'curated' by helpful teachers. I argue that both learning from others (social learning) and helping others learn (teaching) can be characterized as probabilistic inferences guided by an intuitive understanding of how people think, plan, and act. Consistent with this idea, even young children draw rich inferences from evidence provided by others and generate informative evidence that helps others learn. By studying social learning and teaching through a common theoretical lens, inferential social learning provides an integrated account of how human cognition supports acquisition and communication of abstract knowledge.
Machine Learning Classification Algorithms for Predictive Analysis in Healthcare

– Machine Learning and Artificial Intelligence has gained much attention from researchers in healthcare and medical sciences. Today volume, velocity, and variety of healthcare data rapidly increased, therefore there is a need of an efficient machine learning tool which will enhance prediction accuracy in healthcare. The main purpose of this paper is to find the best and most suitable algorithm for prediction and diagnosis of diseases and application of machine learning for heathcare systems. This paper also provides an overview of the data science concepts from data mining technique to machine learning classification algorithms.abstract summarizes, in one paragraph (usually), the major aspects of the entire paper in the following prescribed sequence.
THE PLACE OF EDUCATIONAL ADMINISTRATORS IN PROMOTING E-LEARNING IN NIGERIAN UNIVERSITIES FOR GLOBAL COMPETITIVENESS

The study explored the place of educational administrators in promoting e-learning in Nigerian universities for global competitiveness. The study was guided by three specific purposes and three corresponding research questions. A descriptive survey research design was adopted. The populations comprised all Faculty Deans, Head of Departments, Head different Units and lecturers of the Ebonyi State University, Abakaliki. A simple random sampling technique was used while the sample size was two hundred respondents; comprising of seven Faculty Deans, twenty H.O. Ds, thirteen Unit Heads and one hundred and sixty lecturers drawn from different Faculties of the institution. The instrument for data collection was a structured questionnaire developed by the researcher with the title, “Roles of University Managers in promoting E-learning Questionnaire (RUMEQ)”. The questionnaire was subjected to face and content validity, while a reliability coefficient of 0.78 was gotten using a Cronbach Alpha Reliability Estimate. The instrument was administered personally by the researcher while the data collected was analyzed in relation with the research questions; using mean and standard deviation. The benchmark for the acceptance value for each item was 2.50 and above. This indicates that any item with a mean score of 2.49 or below was not accepted and items with mean values of 2.50 and above were accepted. The study revealed that educational administrators of every university are major key players in the management of funds, provision of necessary facilities and staff training towards optimizing e-learning in Nigerian universities. It was recommended among others that Federal and State governments in Nigeria should show more commitments in funding university education in the country; while administrators of universities should be more proactive in evolving viable option for financial sustainability in order to optimize e-learning in Nigerian universities.
Pattern Discovery from Student Feedback: Identifying Factors to Improve Student Emotions in Learning

—Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classiﬁcation using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.
A Study on the meaning of work and job embeddedness affecting the creative behavior of organization members

This study attempted to confirm the factors that the meaning of work perceived by organization members and job embeddedness affects creative behavior. In addition, we tried to confirm the relationship between the meaning of work and job embeddedness. On the other hand, we tried to verify the mediating effect of job embeddedness between the meaning of work and creative behavior. Accordingly, the following results and implications were derived through the hypotheses set for the members of small and medium-sized enterprises. First, the meaning of work showed a positive effect on job embeddedness and creative behavior, and it was empirically analyzed that job embeddedness was improved on creative behavior. In order to improve the creative behavior of organizational members, the need for advancement of the management and training system to systematically improve and strengthen the meaning of work and job embeddedness has emerged. Second, job embeddedness showed a mediating effect between the meaning of work and creative behavior. In other words, the employee's job embeddedness acted as a direct or indirect factor according to the improvement of the meaning of work and creative behavior. Accordingly, as one of the major factors in human resource management and strategic human resource development for the advancement of the sustainable management system, the need for institutional establishment and implementation was required as it became more efficient and effective.