Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding How Cooperatives Use Financial Reports to Make Decisions. Evidence from Agricultural Cooperatives in Rwanda

Cooperatives entities have a great contribution in socioeconomic development of nations, whether developing and developed countries. The National vision 2050 cooperatives are expected to contribute immensely. At regional level, cooperatives have been included in strategies to achieve Africa Agenda 2063. Internationally, cooperatives are among strategies to achieve the global agenda 2023 through the Sustainable Development Goals (SDGs- 2030)(MINICOM, 2018). Cooperatives activities contributed directly to achievement of some SDGs such as SDG 1: end poverty in all its forms everywhere especially help in enhancing resilience and production capacity, and better distribution of income among citizens. SDG-2: End hunger, achieve food security and improve nutrition and promote sustainable agriculture where 48% of registered cooperatives are distributed in the agricultural activities which are farming, livestock and fishing activities. They also contribute to SDG-5: Achieve gender equality and empower all women and girls. In this line cooperative entities are promoting members economic participation and provide basic framework for the economic and social empowerment of women and girls. SDG-8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. In this perspective cooperatives are considered as primary production entities which are to procure economic production in terms of diversification, processing, technological upgrading and innovation. This is job creation and value chains enhancement, then contribute to economic growth of respective nations.SDG-12: Ensure sustainable consumption and production patterns where cooperatives contribute to production activities such as agriculture activities (MINICOM, 2018).
Understanding resource-based competitiveness: competencies, business processes and alternative performance assessment

The analysis of how organizations orchestrate their resource and capabilities in order to create and/or develop a competitive advantage has increasingly drawn scholarly and policy attention. The collection of 14 papers in this special issue brings new insights into business competitiveness by emphasizing the multidimensional nature of the competitiveness construct. By adopting a multidisciplinary perspective that combines a variety of frameworks (economics, resource-based view, and business servitization), the papers in this special issue analyze the relationships underlying business competitiveness, its determinants as well as its strengths and weaknesses in different geographic contexts (Europe and Latin America). This editorial note first portrays business competitiveness as a multidimensional construct and argues that competitivenessenhancing strategies require an effective understanding of businesses’ resources and capabilities. Also, we provide a number of yet unresolved topics that deserve academic attention. JEL codes: C43, L2, M1, M2, M3, O14
When do Rayleigh and Weibull parametric models predict the same results ? A short note

The particular method selected for modelling overall and progressionfree survival is often important in HTAs. In most appraisals it appears conventional for investigators to employ six “standard” parametric distributions (exponential, Weibull, Gompertz, lognormal, loglogistic, and generalised gamma) to explore survival modelling. Taken together these can generate a wide variety of survival and hazard predictions (examples in Figure 1)
Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

Abstract A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.
The Dark Triad and Ethical Behavior

The purpose of this paper is to examine the effect of the dark triad among Indonesia accountants on their ethical behaviour. Mach IV scale is used to measure the Machiavellianism traits of accountants, the NPI-16 scale is used to assess the Narcissism traits, and PPI scale is used to measure the Psychopathy traits. This approach used 26 public accountant firms in Malang, Surabaya and Jakarta for a sample. We expect that the result of Machiavellianism has a negative influence on ethical behaviour. On other hand, we expect Narcissism and Psychopathy has a positive influence on ethical behaviour. The paper finds out that narcissism, and psychopathy have a positive influence on ethical behaviour, while Machiavellianism has a negative influence on ethical
From actin monomers to bundles: The roles of twinfilin and α-actinin in Drosophila melanogaster development

9/2005 Tiina E. Raevaara Functional Significance of Minor MLH1 Germline Alterations Found in Colon Cancer Patients 10/2005 Katja Pihlainen Liquid Chromatography and Atmospheric Pressure Ionisation Mass Spectrometry in Analysing Drug Seizures 11/2005 Pietri Puustinen Posttranslational Modifications of Potato Virus A Movement Related Proteins CP and VPg 12/2005 Irmgard Suominen Paenibacillus and Bacillus Related to Paper and Spruce Tree 13/2005 Heidi Hyytiainen Regulatory Networks Controlling Virulence in the Plant Pathogen Erwinia Carotovora Ssp. Carotovora 14/2005 Sanna Janhunen Different Responses of the Nigrostriatal and Mesolimbic Dopaminergic Pathways to Nicotinic Receptor Agonists 15/2005 Denis Kainov Packaging Motors of Cystoviruses 16/2005 Ivan Pavlov Heparin-Binding Growth-Associated Molecule (HB-GAM) in Activity-Dependent Neuronal Plasticity in Hippocampus 17/2005 Laura Seppa Regulation of Heat Shock Response in Yeast and Mammalian Cells 18/2005 Veli-Pekka Jaakola Functional and Structural Studies on Heptahelical Membrane Proteins 19/2005 Anssi Rantakari Characterisation of the Type Three Secretion System in Erwinia carotovora 20/2005 Sari Airaksinen Role of Excipients in Moisture Sorption and Physical Stability of Solid Pharmaceutical Formulations 21/2005 Tiina Hilden Affinity and Avidity of the LFA-1 Integrin is Regulated by Phosphorylation 22/2005 Ari Pekka Mahonen Cytokinins Regulate Vascular Morphogenesis in the Arabidopsis thaliana Root
Effect of microwave treatment on the cooking and macronutrient qualities of pulses

ABSTRACT The effect of microwave treatment to reduce the cooking times of five pulses, namely red lentil, chickpea, pigeon pea, mung bean, and pinto bean, were determined in this study. Pulses from 10 to 18% moisture contents were treated using 400 to 600 W microwaves for 14 to 56 s. The cooking times of microwave-treated pulses were significantly lower than that of the control samples. The lowest cooking time was observed for 18% moisture content chickpea and pigeon pea treated with 600 W for 56 s. The Fourier transform mid-infrared spectra in both lipids and fingerprint regions showed the macronutrients differences among the five pulses. Major changes were observed in the amide I region of microwave treated pulses. This effect of microwave treatment was higher in red lentil, chickpea, and mung bean than in pigeon pea and pinto bean at 10% moisture content. At 18% moisture content, the change of β-sheets to aggregates was observed in all pulses due to microwave treatment.
Coarse Expectations and Non-Fundamental Volatility

To model trading anomalies around major stock market milestones, I assume that traders use coarse Bayesian updating. Asset demands are characterized by discontinuities when expectations shift discretely; these demarcate market regimes. When heterogeneous traders and a linear market maker determine prices, the price discovery process converges in distribution to a Brownian motion. Notably, I obtain this result from stationary expectation errors rather than through exogenous processes. I establish a perfectly non-revealing equilibrium: because traders are different, their aggregated trades generate Brownian prices, yet this price process impedes traders from learning their differences. I then extend the result to equilibrium distributions characterized by heavy tails and highlight policies to curb non-fundamental volatility. JEL Classification: G14; G41; D84; D91.
Economic Assessment of Deep-Water Fields in China Under Present Fiscal Scheme

This study examines current fiscal system for the petroleum industry in China using three hypothetical oil field models which are assumed in the South China Sea and tries to find whether current regime can boost oil production in deep-water oil fields in South China Sea. The result is discouraging. All the fields are marginal and are not worth to invest. Because of low oil price, high cost and high government take, all three fields show negative post-tax NPV and negative NPV/I ratio. High case which is 250 million barrels reserve shows the highest IRR, 7.88%. Furthermore, it is found that Chinese fiscal system is regressive. When economic rents are lower, government takes higher share. The early revenue to government further aggravates burden to investors and discourage investment. From the sensitivity analysis, the biggest influencing factor is development cost including capital expenditure and drilling cost. The second and third ones are oil price and reserve. The final one is operating cost or OPEX which is very small in scale. From Monte Carlo simulation, the probability of loss for post-tax NPV is higher than 50% in most circumstances.
Hedge Funds Performance Persistence

This article investigate the performance persistence of 736 hedge funds monthly observations of nine style categories, as they include the largest number of hedge funds measured from 1990 to 2003. The sample is free from survivorship bias as it includes funds that has terminated, merged or unitized. The whole sample consists of 773 hedge funds. We measure performance using risk adjusted measures. High values of the Treynor, Sharpe and information ratio are an indication of a skillful manager that adds value in terms of better performance persistence of the funds under each style category. In contrast, low values of the Treynor, Sharpe and information ratio are an indication of a manager that shows low performance and no value in terms of stock picking ability. The results in term of Treynor, Sharpe and information ratio are mixed. There is mixed evidence of long-term performance persistence. All style categories display positive figures in terms of the share average returns. Positive returns indicate that the manager is skillful in terms of share picking ability.
Micro-expression Recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion

Micro-expressions recognition is a challenge because it involves subtle variations in facial organs. In this paper, first, we propose a novel pipeline to learn a facial graph (nodes and edges) representation to capture these local subtle variations. We express the micro-expressions with multi-patches based on facial landmarks and then stack these patches into channels while using a depthwise convolution (DConv) to learn the features inside the patches, namely, node learning. Then, the encoder of the transformer (ETran) is utilized to learn the relationships between the nodes, namely, edge learning. Based on node and edge learning, a learned facial graph representation is obtained. Second, because the occurrence of an expression is closely bound to action units, we design an A U-GCN to learn the action unit’s matrix by embedding and GCN. Finally, we propose a fusion model to introduce the action unit’s matrix into the learned facial graph representation. The experiments are comparing with SOTA on various evaluation criteria, including common classifications on CASME II and SAMM datasets, and also conducted following Micro-expression Grand Challenge 2019 protocol.
Selective Extraction of Lactic Acid from Aqueous Media through a Hydrophobic H-Beta Zeolite/PVDF Mixed Matrix Membrane Contactor

2-Hydroxypropionic acid, commonly known as lactic acid (LA), is a valuable chemical widely used for the manufacture of green solvents such as ethyl lactate and biodegradable polymers such as poly(lactic acid) (PLA). LA is manufactured by fermentation molasses and whey. Isolation of LA from aqueous broths by conventional methods is energy intensive. Reactive extraction through membranes using specific reagents could prove to be a cost-effective alternative for LA recovery. This study focuses on reactive separation of LA using a novel indigenously developed hydrophobic H-beta zeolite/polyvinylidene fluoride (PVDF) mixed matrix membrane. Experiments were conducted using a stirred cell assembly consisting of two bell shaped glass pipe reducers containing aqueous LA separated by the membrane from an organic solution of tri-n-octylamine (TOA) carrier in alcoholic medium. Effects of experimental parameters such as the concentration of TOA in organic phase and zeolite loading on the rate of acid extraction were e.
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks

Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have exhibited promising results, they still tend to provide incorrect results in highly cluttered scenes. We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial. Thus, in this work, we propose a novel framework that refines the output of such methods by utilizing a graph-based representation of instance masks. We train deep networks capable of sampling smart perturbations to the segmentations, and a graph neural network, which can encode relations between objects, to evaluate the perturbed segmentations. Our proposed method is orthogonal to previous works and achieves state-of-the-art performance when combined with them. We demonstrate an application that uses uncertainty estimates generated by our method to guide a manipulator, leading to efficient understanding of cluttered scenes. Code, models, and video can be found at https://github.com/chrisdxie/rice .
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach

We develop a probabilistic control algorithm, GTLProCo, for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The resulting algorithm not only achieves decentralized control of the swarm but also significantly improves scalability over state-of-the-art existing algorithms. Specifically, we study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently proposed language called graph temporal logic (GTL). By constraining the distribution of the swarm over the nodes of the graph, GTL can specify a wide range of properties, including safety, progress, and response. GTLProCo, with a computational complexity agnostic to the number of agents comprising the swarm, controls the density distribution of the swarm in a decentralized and probabilistic manner. To this end, it synthesizes a time-varying Markov chain modeling the time evolution of the density distribution under the GTL constraints. We first identify a subset of GTL, namely reach-avoid specifications, for which we can reduce the synthesis of such a Markov chain to either linear or semidefinite programs. Then, in the general case, we formulate the synthesis of the Markov chain as a mixed-integer nonlinear program (MINLP). We exploit the structure of the problem to provide an efficient sequential mixed-integer linear programming scheme with trust regions to solve the MINLP. We empirically demonstrate that our sequential scheme is at least three orders of magnitude faster than off-the-shelf MINLP solvers and illustrate the effectiveness of GTLProCo in several swarm scenarios.
Energetics of collapsible channel flow with a nonlinear fluid-beam model

Abstract We consider flow along a finite-length collapsible channel driven by a fixed upstream flux, where a section of one wall of a planar rigid channel is replaced by a plane-strain elastic beam subject to uniform external pressure. A modified constitutive law is used to ensure that the elastic beam is energetically conservative. We apply the finite element method to solve the fully nonlinear steady and unsteady systems. In line with previous studies, we show that the system always has at least one static solution and that there is a narrow region of the parameter space where the system simultaneously exhibits two stable static configurations: an (inflated) upper branch and a (collapsed) lower branch, connected by a pair of limit point bifurcations to an unstable intermediate branch. Both upper and lower static configurations can each become unstable to self-excited oscillations, initiating either side of the region with multiple static states. As the Reynolds number increases along the upper branch the oscillatory limit cycle persists into the region with multiple steady states, where interaction with the intermediate static branch suggests a nearby homoclinic orbit. These oscillations approach zero amplitude at the upper branch limit point, resulting in a stable tongue between the upper and lower branch oscillations. Furthermore, this new formulation allows us to calculate a detailed energy budget over a period of oscillation, where we show that both upper and lower branch instabilities require an increase in the work done by the upstream pressure to overcome the increased dissipation.
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems

Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single processing step, such as symbol detection, or replace multiple steps jointly by a single neural network (NN). These techniques demonstrate promising results but often assume perfect channel state information (CSI) or fail to satisfy the interpretability and scalability constraints imposed by practical systems. In this paper, we propose a new strategy which preserves the benefits of a conventional receiver, but enhances specific parts with ML components. The key idea is to exploit the orthogonal frequency-division multiplexing (OFDM) signal structure to improve both the demapping and the computation of the channel estimation error statistics. Evaluation results show that the proposed ML-enhanced receiver beats practical baselines on all considered scenarios, with significant gains at high speeds.
The d-critical structure on the Quot scheme of points of a Calabi Yau 3-fold

The Artin stack $\mathcal M_n$ of $0$-dimensional sheaves of length $n$ on $\mathbb A^3$ carries two natural d-critical structures in the sense of Joyce. One comes from its description as a quotient stack $[\textrm{crit}(f_n)/\textrm{GL}_n]$, another comes from derived deformation theory of sheaves. We show that these d-critical structures agree. We use this result to prove the analogous statement for the Quot scheme of points $\textrm{Quot}_{\mathbb A^3}(\mathscr O^{\oplus r},n) = \textrm{crit}(f_{r,n})$, which is a global critical locus for every $r>0$, and also carries a derived-in-flavour d-critical structure besides the one induced by the potential $f_{r,n}$. Again, we show these two d-critical structures agree. Moreover, we prove that they locally model the d-critical structure on $\textrm{Quot}_X(F,n)$, where $F$ is a locally free sheaf of rank $r$ on a projective Calabi-Yau $3$-fold $X$. Finally, we prove that the perfect obstruction theory on $\textrm{Hilb}^n\mathbb A^3=\textrm{crit}(f_{1,n})$ induced by the Atiyah class of the universal ideal agrees with the critical obstruction theory induced by the Hessian of the potential $f_{1,n}$.
Application of deep reinforcement learning for Indian stock trading automation

In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made.
A Survey on Adversarial Image Synthesis

Generative Adversarial Networks (GANs) have been extremely successful in various application domains. Adversarial image synthesis has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already demonstrated the great potential of using GAN in image synthesis. In this paper, we provide a taxonomy of methods used in image synthesis, review different models for text-to-image synthesis and image-to-image translation, and discuss some evaluation metrics as well as possible future research directions in image synthesis with GAN.
Two extremal problems in the light of Lex graphs

Extremal problems involving independent sets are much studied. Two of the most important extremal problems in this context are concerned with the sharp upper bounds for the number of independent sets of fixed size and the independence number. In literature, these sharp upper bounds are derived in completely different contexts. In this paper, we show that both of these sharp upper bounds can be derived by considering Lex graphs. More exactly, they depend on two parameters of a sequence defined on them.