Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Rainfall Variability over Southern Ghana

2 Lancaster University, Lancaster, England; f.otu-larbi@lancaster.ac. uk * Correspondence: braimahmm@gmail.com Abstract: Rainfall variability has resulted in extreme events like devastating floods and droughts which is the main cause of human vulnerability to precipitation in West Africa. Attempts have been made by previous studies to understand rainfall variability over Ghana but these have mostly focused on the major rainy season of AprilJuly, leaving a gap in our understanding of the variability in the September-November season which is a very important aspect of the Ghanaian climate system. The current study seeks to close this knowledge gap by employing statistical tools to quantify variabilities in rainfall amounts, rain days and extreme precipitation indices in the minor rainfall season over Ghana. We find extremely high variability in rainfall with Coefficient of variation (CV) between 25.3% and 70.8%, and moderate to high variability in rain days (CV=14.0% 48.8%). Rainfall amount was found to be higher over the middle sector (262.7 mm – 400.2 mm) but lowest over the east coast (125.2 mm – 181.8 mm). Analysis of the second rainfall season using Mankandell Test presents a nonsignificant trend of rainfall amount, and extreme indices (R10, R20, R95p and R99p) for many places in southern Ghana. Rainfall Anomaly Indices show that the middle sector recorded above normal precipitation which is the opposite for areas in the transition zone. The result of this work provides a good understanding of rainfall in the minor rainfall season and may be used for planning purposes.
Data Mining Analysis of Lightning Strike Probability to Simple Structures

— In this data era, data mining and machine learning, in general, have been applied in various research fields. Designing adequate lightning protection systems requires a good understanding of how lightning interacts with grounded structures. The probability of a lightning strike to various points on a structure reflects the associated likelihood for a lightning strike to terminate on such points. In this study, the dataset generated by applying a numerical approach to the computation of the dynamic electro-geometrical model for a cuboid structure was analyzed using data mining techniques. Data classification and regression-based predictive analyses were carried out on the Orange data-mining platform and MATLAB. Cases with 100% classification accuracy were observed using the Random Forest and the AdaBoost algorithms.
Droughts and Agricultural Adaptation to Climate Change

This article analyses the effects of droughts and climate variability on short-term and medium-term adaptation of Colombian rural households. I measure drought in a Differencesin-Differences (DID) framework, as an alternative to the standard approaches decomposing the effects from climate and yearly weather deviations on agricultural productivity and those using the growing degree days and harmful degree days. In the short-term and mediumterm, rural households adapt to the drought of 2010 by increasing the total area planted in crops and livestock, (increasing also the total gross agricultural productivity in value terms) and by working more on the farm. The droughts also increased the use of external sources of water in the farm and made rural households postpone non-housing investments in the farm. I find heterogeneous effects according to the long run mean of temperature in the municipality. Higher temperature affects positively gross agricultural productivity in low-temperature municipalities but negatively high-temperature municipalities. Cereals and coffee seem to benefit from higher temperatures, while vegetables and fruits are more affected.
Theaflavin 3, 3′-Digallate Delays Ovarian Aging by Improving Oocyte Quality and Regulating Granulosa Cell Function

Ovarian aging refers to the gradual decline of ovarian function with increasing physiological age, manifested as decreased ovarian reserve, elevated aging-related markers, and reduced oocyte quality. With a declining female fertility and a growing aging population, it is urgent to delay ovarian aging to maintain fertility and improve the life quality of women. Theaflavin 3, 3′-digallate (TF3) is a naturally bioactive polyphenol compound extracted from black tea, and its antioxidant properties play an important role in maintaining human health and delaying aging; however, the effects of TF3 on female reproduction and ovarian function are not yet clear. Here, we show that TF3 can preserve primordial follicle pool, partially restore the estrous cycle, and increase the offspring number of aged mice. Meanwhile, TF3 gavage increased the number of oocytes retrieved, decreased the level of reactive oxygen species, increased the level of glutathione, and decreased the abnormal rate of oocyte spindle after ovulation induction. Moreover, TF3 inhibited human granulosa cell apoptosis and improved their antioxidative stress ability. High-throughput sequencing and small-molecule-targeted pharmacological prediction show that TF3 affects multiple pathways and gene expression levels, mainly involved in reproductive and developmental processes. It may also affect cellular function by targeting mTOR to regulate the autophagic pathway, thereby delaying the process of ovarian aging. This study shows that TF3 can be used as a potential dietary supplement to protect ovary function from aging and thereby improving the life quality of advanced-age women.
Generalization Error Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic

Graph embedding, which represents real-world entities in a mathematical space, has enabled numerous applications such as analyzing natural languages, social networks, biochemical networks, and knowledge bases. It has been experimentally shown that graph embedding in hyperbolic space can represent hierarchical tree-like data more effectively than embedding in linear space, owing to hyperbolic space’s exponential growth property. However, since the theoretical comparison has been limited to ideal noiseless settings, the potential for the hyperbolic space’s property to worsen the generalization error for practical data has not been analyzed. In this paper, we provide a generalization error bound applicable for graph embedding both in linear and hyperbolic spaces under various negative sampling settings that appear in graph embedding. Our bound states that error is polynomial and exponential with respect to the embedding space’s radius in linear and hyperbolic spaces, respectively, which implies that hyperbolic space’s exponential growth property worsens the error. Using our bound, we clarify the data size condition on which graph embedding in hyperbolic space can represent a tree better than in Euclidean space by discussing the bias-variance trade-off. Our bound also shows that imbalanced data distribution, which often appears in graph embedding, can worsen the error.
Performance measurement and management in a VUCA world

It is widely recognised that performance measurement systems (PMS) are important for the management of a factory, business enterprise or public sector organisation. However, the case can be made that the development of PMS and organisational forms are inextricably linked. As the economic, technical and trading environment evolves, organisations need to adapt, and so do the measurement and management systems. In this editorial, we will retrace the history of PMS from early factory costing systems through to the multidimensional approaches widely used today. As the world evolves, organisations and PMS need to evolve too with the papers in this special issue reflecting this. These papers lead us to reconsider the future of PMS as climate change, the internet of things (IoT) and new technology drive us forward towards a complex and uncertain future.
Genetic Dissection of the Mixing Properties of Wheat Flour (Triticum aestivum L.) Using Unconditional and Conditional QTL Mapping

Wheat (Triticum aestivum L.) flour mixing properties are essential quality parameters in the dough development process. Limited research on superior alleles for mixing properties has restricted their molecular improvement, and other factors related to the complex traits have been ignored. A molecular map of 9576 polymorphic markers in the RIL population (F8:9) (Shannong01-35/Gaocheng9411) was constructed to evaluate mixing property effects in three environments. The parents were selected with markedly distinct high-molecular-weight glutenin subunits (HMW-GS). This study not only evaluated mixing properties using conventional unconditional QTL mapping but also evaluated the relationships between protein-related traits using conditional QTL mapping. The analyses identified most additive QTLs for major mixing properties on chromosomes 1A, 1B, and 1D. Two major loci (1A.1-15 and 1D-1) associated with mixing properties have confirmed the important contributions of Glu-A1 and Glu-D1 to wheat quality at the QTL level, which were mainly affected by the gluten index. Another important locus, 1B.1-24 (associated with midline peak value and midline peak width, with high phenotypic variations explained), might represent a new variation distinct from Glu-B1. The favored alleles came from Gaocheng9411. Several mixing properties shared the same QTLs (1B.1-6 and 1A.1-15), indicating tight linkage or pleiotropism. Genotype-by-environment (G×E) interactions were also investigated in the present study. The QTL results in our study may improve our understanding of the genetic interrelationships between mixing properties and protein-related traits.
Variation of biomechanical properties of newly formed bone tissue determined by nanoindentation as a function of healing time

nanoindentation as a function of healing time R. Vayron, E. Barthel, V. Mathieu, E. Soffer, F. Anagnostou and G. Haiat* Modélisation et Simulation Multi Echelle, CNRS, Université Paris-Est, UMR CNRS 8208, 61, Avenue du Général de Gaulle, 94010 Créteil Cedex, France; Laboratoire CNRS/Saint Gobain UMR 125, Surface du Verre et Interfaces, 39, Quai Lucien Lefranc, 93303 Aubervilliers Cedex, France; Laboratoire de Recherches Orthopédiques, CNRS, Université Paris 7, UMR CNRS 7052, 10, Avenue de Verdun, 75010 Paris, France
LinKRID: Vetting Imbalance Reference Counting in Linux kernel with Symbolic Execution

Linux kernel employs reference counters, which record the number of references to a shared kernel object, to track its lifecycle and prevent memory errors like use-after-free. However, the usage of reference counters can be tricky and often error-prone, especially considering unique kernel conventions of managing reference counters (e.g., external vs. internal reference counters). In this paper, we aim to automatically discover incorrect usage of reference counters, overcoming two key challenges: (1) scalability and (2) the aforementioned unique kernel conventions. Specifically, we develop a tiered program analysis based solution to efficiently and precisely check the imbalances between the change in the actual number of references and the corresponding reference counter. We apply our tool to the 4.14. 0 kernel (with allyesconfig) and find 118 bugs, out of which 87 are new. The result shows our tool is scalable and effective.
CMAR_A_341448 9007..9013

1Department of Otolaryngology and Hepatobiliary, Chengdu Fifth People’s Hospital, Chengdu, 611130, Sichuan, People’s Republic of China; 2Nursing Department, Chengdu Fifth People’s Hospital, Chengdu, 611130, Sichuan, People’s Republic of China; 3Intensive Care Unit, Chengdu Fifth People’s Hospital, Chengdu, 611130, Sichuan, People’s Republic of China; 4Department of Oncology, Chengdu Wenjiang District People’s Hospital, Chengdu, 611130, Sichuan, People’s Republic of China; 5Department of Nursing, Shenzhen People’s Hospital (2nd Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, Guangdong, People’s Republic of China
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning

Sparse tensors appear frequently in federated deep learning, either as a direct artifact of the deep neural network’s gradients, or, as a result of an explicit sparsiﬁ-cation process. Existing communication primitives are agnostic to the challenges of deep learning; consequently, they impose unnecessary communication overhead. This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored to federated deep learning. DeepReduce decomposes sparse tensors into two sets, values and indices, and allows both independent and combined compression of these sets. We support a variety of standard compressors, such as Deﬂate for values, and Run-Length Encoding for indices. We also propose two novel compression schemes that achieve superior results: curve-ﬁtting based for values, and bloom-ﬁlter based for indices. DeepReduce is orthogonal to existing gradient sparsiﬁers and can be applied in conjunction with them, transparently to the end-user, to signiﬁcantly lower the communication overhead. As a proof of concept, we implement our approach on TensorFlow and PyTorch. Our experiments with real models demonstrate that DeepReduce transmits 320% less data than existing sparsiﬁers, without affecting accuracy. Code is available at https://github.com/hangxu0304/DeepReduce .
RETRACTED: Comprehensive evaluation analysis of mental health status of poverty-stricken college students at present age with interval-valued intuitionistic fuzzy information

∗Corresponding author. Yile Dong, College of Tourism, HuaQiao University, Quanzhou, Fujian, 362021, P. R. China. Tel. /Fax: +86 0595 22692792; E-mail: dongyile1986@163.com. promote education fair and promote the coordinated development of education, the State Council of the People’s Republic of China promulgated Views on the Establishment and Improvement of the Financial Assistance Policy System for Poor Students in Undergraduate Universities, Higher Vocational Schools and Secondary Vocational Schools in May 2007. This marked that the financial assistance policy system for poor students had been fully established, including state scholarships, state scholarships for encouraging students, state grants, free education for normal
Deep learning based multi-batch calibration for classification in various omics

Background: The amount of available biological data has exploded since the emergence of high-throughput technologies, which is not only revolting the way we recognize molecules and diseases but also bringing novel analytical challenges to bioinformatics analysis. In the last decade, deep learning has become a dominant technique in data science. However, classification accuracy is plagued with domain discrepancy. Notably, in the presence of multiple batches, domain discrepancy typically happens between individual batches. The recently proposed pair-wise adaptation approach may be suboptimal as it fails to eliminate the external factors across multiple batches and takes the classification task into account simultaneously. Results: We propose a joint deep learning framework for integrating batch effect removal and classification upon various omics data. To this end, we validate it on two private metabolomics (MALDI MS) datasets and one public transcriptomics (scRNA-seq) dataset. Especially for the former, we have achieved the highest diagnostic accuracy (ACC), with notable ~10% improvement than over state-of-the-art methods. Overall, these results indicate that our approach removes batch effect more effectively than conventional methods and yields more accurate classification results for smart diagnosis.
The Interface Between Semantic Change and Polysemy: A Case Study on shang 'above' in Chinese

This study investigates the diachronic developments of 2749 instances of shang in historical texts from Chinese corpora. We use Tyler and Evans (2003)‘s Principled Polysemy Model to test the various senses associated with shang. It has been shown that there are close relations between the semantic change and polysemy of Chinese spatial word shang in which new meanings of shang occurred based on existing meanings and both old and newer meanings can coexist for a long period of time. Our research also displays how spatial concepts are coded linguistically by Chinese speakers based on various mechanisms including conceptual metaphor, invited inference, constructional change, causative morphology and word-class shift. By displaying the way shang ̳above‘ evolved throughout the history of written Chinese to result in the current polysemy network, this study contributes to studies on semantic change in Chinese and reveals why we use spatial words as the way we do.
Multiclass Boosting and the Cost of Weak Learning

Boosting is an algorithmic approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. In this work we study multiclass boosting with a possibly large number of classes or categories. Multiclass boosting can be formulated in various ways. Here, we focus on an especially natural formulation in which the weak hypotheses are assumed to belong to an “easy-to-learn” base class, and the weak learner is an agnostic PAC learner for that class with respect to the standard classiﬁcation loss. This is in contrast with other, more complicated losses as have often been considered in the past. The goal of the overall boosting algorithm is then to learn a combination of weak hypotheses by repeatedly calling the weak learner. We study the resources required for boosting, especially how they depend on the number of classes k , for both the booster and weak learner. We ﬁnd that the boosting algorithm itself only requires O (log k ) samples, as we show by analyzing a variant of AdaBoost for our setting. In stark contrast, assuming typical limits on the number of weak-learner calls, we prove that the number of samples required by a weak learner is at least polynomial in k , exponentially more than the number of samples needed by the booster. Alternatively, we prove that the weak learner’s accuracy parameter must be smaller than an inverse polynomial in k , showing that the returned weak hypotheses must be nearly the best in their class when k is large. We also prove a trade-off between number of oracle calls and the resources required of the weak learner, meaning that the fewer calls to the weak learner the more that is demanded on each call.
Finding Bipartite Components in Hypergraphs

Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ this process to design a polynomial-time algorithm that approximately finds bipartite components in a hypergraph. We theoretically prove the performance of our proposed algorithm, and compare it against the previous state-of-the-art through extensive experimental analysis on both synthetic and real-world datasets. We find that our new algorithm consistently and significantly outperforms the previous state-of-the-art across a wide range of hypergraphs.
Brand Identity-image Congruence: A Framework for Business-to-business Banks in South Africa

ABSTRACT Purpose A fundamental element of brand-building is communicating what a brand is and what it stands for. Clarifying a brand’s identity may yield sustained competitive advantage. Moreover, aligning a brand’s identity to its image is vital in business-to-business (B2B) services, where a firm’s actual character and performance serve as critical sources of differentiation. This study proposes a comprehensive Brand Identity-Image Congruence (BI-IC) framework for B2B banks within South Africa. Methodology An interpretivist qualitative research design was adopted. The BI-IC framework was derived from an empirical exploration of the applicability of the Brand Identity Prism (BIP) in selected B2B banks. To explore the applicability of the BIP dimensions, depth interviews were conducted with bank marketing executives who were instrumental in brand strategy formulation. Findings The findings revealed that some BIP dimensions (physique, personality, culture and relationships) were applicable to the study context, while reflection and self-image were inapplicable in the same sector. Consequently, an amended version of the BIP is submitted in the form of a BI-IC conceptual framework for B2B banks. Originality/value This study contributes to two areas of the brand identity school: (i) it empirically explored the applicability of the BIP in a new context; and (ii) the BI-IC framework is the first of its kind as there is no universally accepted B2B bank Brand Identity-Image Congruence framework. Therefore, this study makes a unique contribution to brand management literature.
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in recent years and has found many scientiﬁc and engineering applications. However, a key challenge arising from many cooperative MARL algorithm designs (e.g., the actor-critic framework) is the policy evaluation problem, which can only be conducted in a decentralized fashion. In this paper, we focus on decentralized MARL policy evaluation with nonlinear function approximation, which is often seen in deep MARL. We ﬁrst show that the empirical decentralized MARL policy evaluation problem can be reformulated as a decentralized nonconvex-strongly-concave minimax saddle point problem. We then develop a decentralized gradient-based descent ascent algorithm called GT-GDA that enjoys a convergence rate of O (1 /T ) . To further reduce the sample complexity, we pro-pose two decentralized stochastic optimization algorithms called GT-SRVR and GT-SRVR I , which enhance GT-GDA by variance reduction techniques. We show that all algorithms all enjoy an O (1 /T ) convergence rate to a stationary point of the reformulated minimax problem. Moreover, the fast convergence rates of GT-SRVR and GT-SRVR I imply O ( (cid:15) − 2 ) communication complexity and O ( m √ n(cid:15) − 2 ) sample complexity, where m is the number of agents and n is
The African

Despite the richness of African proverbs, they have been muted in information systems (IS) research. This explicitly contributes to the lag in developing information systems and technology solutions for African people by African researchers in African terms. It is on this basis that two related objectives were formulated: (1) to examine the role which African proverbs can play in further advancing IS research, and (2) to understand how the concept can be employed to indigenize IS research. Qualitative data were collected from different sources, and the dimension of social change from structuration theory was employed to examine the role African proverbs can significantly play in advancing IS research. The analysis reveals that African proverbs can be employed in all stages of IS research, from problematizing the phenomenon, data collection to data analysis and interpretation of findings.