Miracle-dz/UniFAR-FTUs huggingface dataset:
MORPHOLOGICAL AND GENOTYPIC CHARACTERIZATION OF DIFFERENT LOTUS ( NELUMBO NUCIFERA GAERTN.) SAMPLES AVAILABLE IN BANGLADESH

Asian lotus ( Nelumbo nucifera Gaertn.), commonly known as sacred lotus is a basal eudicot. It has been grown and cultivated as food, medicine and for cultural, and religious activities. In the current study, samples were collected from six different locations to evaluate the variation among different lotus germplasm based on external morphological characteristics, as well as, to study the genetic variation and the molecular characterization. Analysis of variance showed a higher level of variations among the germplasm for all the morphological features. Based on the morphological features, a dendrogram was constructed to assess the linkage among the germplasm. The yellow lotus of Cumilla was considered superior among the germplasm studied. To assess the genetic diversity and the correct identification of lotus germplasm, molecular method “Barcoding” was performed. To achieve the goal, two plastidial regions: rpoB and rpoC1 were employed. The germplasm showing successful PCR were subjected to sequence analysis of their barcode genes. All the selected barcode genes showed successful identification of all the germplasm as N. nucifera in multilocus identification based on their sequences except for the germplasm of Rajshahi and also confirmed the yellowish lotus of Cumilla considered as a new cultivar N. nucifera ‘Gomoti’, newly found in Bangladesh. Genetic sequences obtained in the context of DNA barcoding had also been used to create a phylogenetic tree in which the germplasm were clustered into five main clades. The current study was successful in establishing an efficient protocol for the correct identification of lotus germplasm and was capable of establishing an elite gene source. Moreover, future studies are warranted to see the identifying capability and diverging power of the barcodes.
Friction-Adaptive Stochastic Predictive Control for Trajectory Tracking of Autonomous Vehicles

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We develop a stochastic nonlinear model-predictive controller (SNMPC) that learns the tire–road friction relationship online using standard automotive-grade sensors. We learn the tire-friction function using a Bayesian approach, where the friction curve is modeled as a Gaussian process. The estimator outputs the estimate of the tire-friction model as well as the uncertainty function of the estimate, which expresses the confidence in the model for different driving regimes. The SNMPC exploits the uncertainty estimate in its prediction model to take proper action. We validate the approach using the high-fidelity vehicle simulator CarSim and compare against various nominal NMPC approaches. The results indicate more than six times better performance for the proposed adaptive SNMPC in closed-loop cost over the simulation time.
rapid communication Osteocyte hypoxia: a novel mechanotransduction pathway

Osteocyte hypoxia: a novel mechanotransduction pathway. Am. J. Physiol. 277 ( Cell Physiol. 46): C598–C602, 1999. —Bone is a unique tissue in which to examine mechanotransduction due to its essential role in weight bearing. Within bone, the osteocyte is an ideal cellular mechanotransducer candidate. Because osteocytes reside distant from the blood supply, their metabolic needs are met by a combination of passive diffusion and enhanced diffusion, arising when the tissue is loaded during functional activity. Therefore, we hypothesized that depriving a bone of mechanical loading (and thus eliminating diffusion enhanced by loading) would rapidly induce osteocyte hypoxia. Using the avian ulna model of disuse osteope-nia, we found that 24 h of unloading results in signiﬁcant osteocyte hypoxia (8.4 6 1.8%) compared with control levels (1.1 6 0.5%; P 5 0.03). Additionally, we present preliminary data suggesting that a brief loading regimen is sufficient to rescue osteocytes from this fate. The rapid onset of the observed osteocyte hypoxia, the inhibition of hypoxia by brief loading, and the cellular consequences of oxygen deprivation are suggestive of a novel mechanotransduction pathway with implications across organ systems.
Expanding EV Charging Networks Considering Transportation Pattern and Power Supply Limit

For large-scale adoption of electric vehicles (EVs), a charging network is needed near the road. The charging station planning and deployment problem should consider the increasing penetration ratio of EVs over a long period of time, and the highly dynamic and location-dependent demands and power grid constraints. This paper focuses on the dynamic charging network design, i.e., how to optimize the charging station locations and the number of chargers in each station at different time stages with an increasing EV penetration ratio. For each candidate location, we first model its coverage area to estimate the dynamic EV charging requirements. Then, we formulate the problem at each time stage as profit maximization, which is a mixed-integer optimization problem. To make it tractable, we investigate the profitability of candidate locations and derive their upper and lower bounds on the expected profit. Then we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm, named RMCL-E, is proposed. Using real vehicle traces, simulation results show that the proposed algorithm can make a good trade-off between the service blocking probability and the construction cost to maximize the total profit, which is attractive for charging service providers.
Scalable Algorithms for Mining Graphs and Social Networks via Sampling

of “Scalable Algorithms for Mining Graphs and Social Networks via Sampling”: Sampling based and randomized algorithms are powerful tools in studying big data problems. In this thesis we provide sampling based algorithms for mining graphs and social network in three different contexts: Detecting Valuable Information. Detecting new information and events in a dynamic system by probing individual nodes has many practical applications: discovering new webpages, analyzing influence properties in network, and detecting failure propagation in electronic circuits or infections in public drinkable water systems. In practice, it is infeasible for anyone but the owner of the network (if existent) to monitor all nodes at all times. We study the constrained setting when the observer can only probe a small set of nodes at each time step to check whether new pieces of information (items) have reached those nodes. Centrality Maximization. Betweenness centrality (BWC) is a fundamental centrality measure in social network analysis. Given a large-scale network, how can we find the most central nodes? This question is of great importance to many key applications that rely on BWC, including community detection and understanding graph vulnerability. Despite the large amount of work on scalable approximation algorithm design for BWC, estimating BWC on large-scale networks remains a computational challenge. In this paper, we study the Centrality Maximization problem (CMP): given a graph G = (V,E) and a positive integer k, find a set S∗ ⊆ V that maximizes BWC subject to the cardinality constraint |S∗| ≤ k. We present an efficient randomized algorithm that provides a (1 − 1/e − )-approximation with high probability, where > 0. Our results improve the current state-of-the-art result [127]. Influence Estimation. Social networks are important communication and information media. Individuals in a social network share information and influence each other through their social connections. Understanding social influence and information diffusion is a fundamental research endeavor and it has important applications in online social advertising, viral marketing, and trending topic prediction. We first study the Targeted-Influence problem (TIP): Given a network G = (V , E) and a model of influence, we want to be able to estimate in real-time (say in a few seconds per query) the influence of an arbitrary subset of users S over an arbitrary subset of users T , for any possible query (S;T ), S, T ⊆ V . To do so, we allow an efficient preprocessing. Finally, we conclude the thesis by studying the problem of Conditional Influence Estimation (CIE) whose goal is to estimate the influence of a node given that a cascade started from that node has already reached a group of nodes (target nodes).
Nominal Anti-Unification with Atom-Variables

Anti-unification is the task of generalizing a set of expressions in the most specific way. It was extended to the nominal framework by Baumgarter, Kutsia, Levy and Villaret, who defined an algorithm solving the nominal anti-unification problem, which runs in polynomial time. Unfortunately, when an infinite set of atoms are allowed in generalizations, a minimal complete set of solutions in nominal anti-unification does not exist, in general. In this paper, we present a more general approach to nominal anti-unification that uses atom-variables instead of explicit atoms, and two variants of freshness constraints: NL A -constraints (with atom-variables), and Eqr -constraints based on Eq uivalence r elations on atom-variables. The idea of atom-variables is that different atom-variables may be instantiated with identical or different atoms. Albeit simple, this freedom in the formulation increases its application potential: we provide an algorithm that is finitary for the NL A -freshness constraints, and for Eqr -freshness constraints it computes a unique least general generalization. There is a price to pay in the general case: checking freshness constraints and other related logical questions will require exponential time. The setting of Baumgartner et al. is improved by the atom-only case, which runs in polynomial time and computes a unique least general generalization.
A Graphical Proof Theory of Logical Time

Logical time is a partial order over events in distributed systems, constraining which events precede others. Special interest has been given to series-parallel orders since they correspond to formulas constructed via the two operations for “series” and “parallel” composition. For this reason, series-parallel orders have received attention from proof theory, leading to pomset logic, the logic BV , and their extensions. However, logical time does not always form a series-parallel order; indeed, ubiquitous structures in distributed systems are beyond current proof theoretic methods. In this paper, we explore how this restriction can be lifted. We design new logics that work directly on graphs instead of formulas, we develop their proof theory, and we show that our logics are conservative extensions of the logic BV .
Serendipity as a Catalyst. Knowledge Generation in Interdisciplinary Research

(2019). Serendipity as a Catalyst. Knowledge Generation in Interdisciplinary Research. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1853-1869.
THE DYNAMICS OF DISINFORMATION IN ELECTORAL VIOLENCE IN NIGERIA

Disinformation is regarded as an act that involves concocting and spreading deliberately information that is not true with the aim of causing harm and threat to the intended targets. The central theme in disinformation is that the spreader is fully aware the information or news item is false but shares to achieve a speciﬁc objective of injuring the reputation of someone, institution, groups, or country. Disinformation is misleading information that has the function of misleading someone (Fallis 2015). This means disinformation deals with publishing misleading information and the source/publisher derives gains from the information disorder. It must be said that disinformation is as old as man but has assumed more prominent space in recent times because of its destructive and devastating eﬀects on the society, following the introduction of digital technology. Wardle and Derakhshan (2017) state that ‘disinformation including misinformation has gained ground in the community of activists, scholars, and non proﬁt apologists attempting to understand and mitigate these conditions. ’ In addition, renewed interest of scholars among others has led to further interrogation of the concept and practice of disinformation. he study examines the dynamics of disinformation in electoral violence in Nigeria. Many studies have been done on role of disinformation in electoral violence in the Western world, yet the role of disinformation in electoral violence remains understudied in Nigeria. This is the gap the study intends to ﬁll. Data for the study are collected from archives, newspapers, journals, books and internets sources including social media platforms – Facebook, WhatsApp, Twitter and Instagram which are analysed qualitatively using historical approach. The study interrogates the change and continuity in the role of disinformation in electoral violence from the era of traditional media to the emergence of digital technology, 1964 to 2019. The study also suggests measure to tackle the menace posed by disinformation, especially in the Nigerian political space. ABSTRACT
Specialized, MSE-optimal m-estimators of the rule probability especially suitable for machine learning

The paper presents an improved sample based ruleprobability estimation that is an important indicator of the rule quality and credibility in systems of machine learning. It concerns rules obtained, e.g., with the use of decision trees and rough set theory. Particular rules are frequently supported only by a small or very small number of data pieces. The rule probability is mostly investigated with the use of global estimators such as the frequency-, the Laplace-, or the m-estimator constructed for the full probability interval [0,1]. The paper shows that precision of the rule probability estimation can be considerably increased by the use of m-estimators which are specialized for the interval [phmin, phmax] given by the problem expert. The paper also presents a new interpretation of the m-estimator parameters that can be optimized in the estimators.
Online Weighted Cardinality Joint Replenishment Problem with Delay

We study a generalization of the classic Online Joint Replenishment Problem (JRP) with Delays that we call the Online Weighted Cardinality JRP with Delays. The JRP is an extensively studied inventory management problem wherein requests for different item types arrive at various points in time. A request is served by ordering its corresponding item type. The cost of serving a set of requests depends on the item types ordered. Furthermore, each request incurs a delay penalty while it is left unserved. The objective is to minimise the total service and delay costs. In the Weighted Cardinality JRP, each item type has a positive weight and the cost of ordering is a non-decreasing, concave function of the total weight of the item types ordered. This problem was first considered in the offline setting by Cheung et al. (2015) but nothing is known in the online setting. Our main result is a deterministic, constant competitive algorithm for this problem.
Bayesian Continuous-Time Tucker Decomposition

Tensor decomposition is a dominant framework for multiway data analysis and prediction. Although practical data often contains timestamps for the observed entries, existing tensor decomposition approaches overlook or under-use this valuable temporal information. They either drop the timestamps or bin them into crude steps and hence ignore the temporal dynamics within each step or use simple parametric time coefﬁcients. To overcome these limitations, we propose Bayesian Continuous-Time Tucker Decomposition (BCTT). We model the tensor-core of the classical Tucker decomposition as a time-varying function, and place a Gaussian process prior to ﬂexibly estimate all kinds of temporal dynamics. In this way, our model maintains the interpretability while is ﬂexi-ble enough to capture various complex temporal relationships between the tensor nodes. For ef-ﬁcient and high-quality posterior inference, we use the stochastic differential equation (SDE) representation of temporal GPs to build an equivalent state-space prior, which avoids huge kernel matrix computation and sparse/low-rank approximations. We then use Kalman ﬁltering, RTS smoothing, and conditional moment matching to develop a scalable message-passing inference al-gorithm. We show the advantage of our method in simulation and several real-world applications.
Nonmyopic (cid:15) -Bayes-Optimal Active Learning of Gaussian Processes

A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic (cid:15) -Bayes-optimal active learning ( (cid:15) -BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily developed myopic/greedy algorithms or performed exploration and exploitation separately. To perform active learning in real time, we then propose an anytime algo-rithm based on (cid:15) -BAL with performance guarantee and empirically demonstrate using synthetic and real-world datasets that, with limited budget, it outperforms the state-of-the-art algorithms.
Receptor Polymorphisms in Major Depression: Focus on glucocorticoid sensitivity and neurocognitive functioning

: Previously, it has been suggested that hypothalamic pituitary adrenal (HPA) axis dysregulation and as a consequence increased cortisol levels, is not only a state phenomenon, but may also be a trait phenomenon in mood disorders. Cortisol exerts its effects mainly by binding to the glucocorticoid receptor (GR) and, in particular of interest in certain brain regions, the mineralocorticoid receptor (MR). Several GR polymorphisms have been shown to be associated with altered sensitivity of the HPA axis. Recently, the GR polymorphisms Bcl I and ER22/23EK have been associated with unipolar depression in several studies. In addition, the ER22/23EK polymorphism seems to be associated with a decreased risk of dementia in healthy individuals. Also during a depressive episode carriers of this ER22/23EK variant demonstrated a tendency towards better cognition, as measured by divided attention tests. cognitive
Sharpness of Tire Angle Determination Based on Point Cloud Dataset

In the tire production line, it is usually necessary to trim the blank tires to finished tires according to the sharpness of tire angle. If the determination of tire angle is made by mistakes, it will cause the tire to be a waste for choosing a wrong scraper. In order to determine the sharpness of tire angle accurately, point cloud acquisition camera was used to collect the data of tires in the production line. Based on the point cloud data of tires, a framework to detect the sharpness of tire angle was proposed. In the proposed framework, a locating method using image processing algorithms such as morphological operation, connected component labeling and corner point detection was used to locate the tire angle. A ridge detection algorithm including surface normal vector and principal curvature computation was used to determine the type of tire angle. A number of point cloud datasets of different tires experimental results show that the proposed framework has higher accuracy and efficiency to determine whether the tire angle is sharp or round. Keywords—point cloud; sharpness; tire angle
Facies-based FWI for anisotropic media: A North Sea case study

Elastic full-waveform inversion (FWI) can substantially increase the resolution of reservoir characterization using seismic data. However, FWI for realistic anisotropic media suffers from the trade-offs between the medium parameters and strongly relies on the accuracy of the initial model. Here, we employ a regularization methodology that utilizes geologically consistent information to reduce the inversion nonlinearity and crosstalk between the parameters. The geologic constraints are obtained from well logs and interpolated along major horizons in the migrated image. The algorithm, designed for transversely isotropic media with a tilted symmetry axis (TTI) is applied to ocean-bottom data acquired at Volve ﬁeld in the North Sea. The facies-based constraints help build high-resolution velocity ﬁelds and accurately image the reservoir region. In particular, the developed algorithm substantially increases the resolution of the P- and S-wave symmetry-direction velocities and other parameters at the reservoir level. The facies-based FWI also provides robust estimates of density, which is inverted simultaneously with the velocity ﬁelds. Overall, even relatively sparse prior information proves to be sufﬁcient for the proposed methodology to achieve a much higher spatial resolution than the unconstrained FWI. The wave equation 2 is solved using a forth-order (in space) and second-order (in time) ﬁnite-difference algorithm. We apply the convolutional PML (perfectly matched layers) absorbing boundary conditions on the sides of the model but the top represents a free surface. The waveﬁeld is excited by a point explosive source. The source signal is a bandlimited spike (to avoid grid dispersion) with a central frequency of 9 Hz. As in Singh et al. (2020b), we apply the domain-decomposition method (Bohlen, 2002) to reduce computational cost.
Understanding the Business Relevance of ESG Issues

In the last decade, an increasing number of companies have released environmental, social and governance (ESG) data, fueled both by voluntary disclosure and regulatory mandates. This came, partly, as a response to increasing demand by investors to understand how companies were managing issues that could be potentially material for business performance. In line with that, a growing literature suggests that ESG issues are economically meaningful thereby associated with stock returns (Edmans 2011; Dimson et al. 2015; Lins, Servaes and Tamayo 2017; Bolton and Kacperczyk 2020; Dai et al. 2020; Welch and Yoon 2021).
Novel Family of Single-Stage Buck–Boost Inverters Based on Unfolding Circuit

This paper describes a novel family of single-phase single-stage buck–boost inverters using output unfolding circuits. Operation principles and component design guidelines along with modulation techniques are presented and discussed. The simulation results confirm all theoretical statements. Experimental setup of the most promising solution is assembled and tested, where the efficiency for different operation modes is analyzed. Finally, the pros and cons along with applications of the proposed solutions are discussed in the conclusions.