Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies analysing crime-scene behaviour and offenders ’ characteristics attempt to answer the question ‘ what kind of person commits what kind of crime ?

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THE EFFECT OF CONSUMER AWARENESS ON CONSUMER ETHNOCENTRISM AND LOYALTY REGARDS TO THE FUNCTIONS OF INTERNATIONAL BRANDS

The aim of this study to examine the relationships among consumer awareness, ethnocentrism, and loyalty in accordance with the functions of international brands. Our samples were 275 hypermarkets customers that randomly intercepted while shopping in famous hypermarket using an interviewer-administered questionnaire. The structural equation modeling technique was used to test the model. The results show that consumer awareness positively influences consumer ethnocentrism, while consumer awareness influences consumer loyalty. The results also reveal that consumer ethnocentrism influences consumer loyalty in a positive manner. We recommend that marketers consider consumer awareness, ethnocentrism, and loyalty with brand functions in order to design marketing programs.
ANALYSIS OF CLIMATE DATA AND THE ASSOCIATED RISKS TO MAIZE PRODUCTION IN SEMI-ARID EASTERN KENYA

Climate change is one of the most important constraints to agricultural production and food insecurity in marginal areas that rely on rain-fed production systems. Climate data for two analogue locations in semi-arid eastern Kenya was analyzed using Instat Plus V3.4 statistical package with the aim of determining changes and variability in seasonal and annual rainfall and temperatures, percentages of seasons with adequate rainfall for maize production, probabilities of dry spells in October–December rainfall season, and accumulated rainfall and soil moisture content relations at different maize growth stages. Analysis gave declining trends in both seasonal and annual rainfall amounts at rates between 0.15 and 2.1 mm per annum and increase of 0.04C and 0.03C in mean maximum and minimum annual temperatures, respectively. This is indicative of changes in climate and hence the need for early exploration of necessary mitigation strategies by all stakeholders and policy makers. There were high chances (> 90% probability) of dry spells in October and high percentages of seasons having less than 250 mm rainfall compared to those with more than 450 mm. Rainfall was low and poorly distributed over the crop growing season and soil moisture remained low even during periods of high crop water demand signifying the high risk involved in maize production and the need for introduction and popularization of alternative crops as well as water conservation measures during months of high rainfall. It also calls for specification of specific crops to be grown in the two seasons with different rainfall expectations.
Estimation of rice evapotranspiration using a microlysimeter technique and comparison with FAO Penman-Monteith and Pan evaporation methods under Moroccan conditions

using a microlysimeter technique and comparison with FAO Penman-Monteith and Pan evaporation methods under Moroccan conditions. Abstract – This paper reviews field measurements of evapotranspiration from paddy rice fields (ET) in an experimental station in the Gharb region of Morocco, during the summer seasons in 1995 and 1996. The results showed that the seasonal average water consumption of rice was 6.7 mm·day –1 with a maximum value of 8.3 mm·day –1 during the panicle enlargement stage (R2). The average daily ET for 1996 was compared with US Class ‘A’ open pan evaporation (Ep) and with reference evapotranspiration (ET 0 ) calculated using a validated FAO Penman-Monteith equation. Both methods gave good estimates of ET with a correlation coefficient of 0.78 ( P < 0.001, slope = 1.06) with Ep and 0.79 with ET 0 ( P < 0.001, slope = 1.3). The derived mean crop coefficients were 1.06 and 1.3, respectively, for the average of the two years. The cumulated ET over the growing season was nearly equal to the cumulated Ep, and greater by about 20% of cumulated ET 0 . This superiority might be attributed to an advective energy transferred from areas surrounding the rice zone activated by wind speed. advective transmise des zones sèches, entourant la zone rizicole, activée par le déplacement des vents. riz Oryza ) évapotranspiration / cultural évapotranspiration de
Learning to Reuse Translations: Guiding Neural Machine Translation with Examples

In this paper, we study the problem of enabling neural machine translation (NMT) to reuse previous translations from similar examples in target prediction. Distinguishing reusable translations from noisy segments and learning to reuse them in NMT are non-trivial. To solve these challenges, we propose an Example-Guided NMT (EGNMT) framework with two models: (1) a noise-masked encoder model that masks out noisy words according to word alignments and encodes the noise-masked sentences with an additional example encoder and (2) an auxiliary decoder model that predicts reusable words via an auxiliary decoder sharing parameters with the primary decoder. We define and implement the two models with the state-of-the-art Transformer. Experiments show that the noise-masked encoder model allows NMT to learn useful information from examples with low fuzzy match scores (FMS) while the auxiliary decoder model is good for high-FMS examples. More experiments on Chinese-English, English-German and English-Spanish translation demonstrate that the combination of the two EGNMT models can achieve improvements of up to +9 BLEU points over the baseline system and +7 BLEU points over a two-encoder Transformer.
Soluto-thermo-hydrodynamics influenced evaporation of sessile droplets

The present article experimentally and theoretically probes the evaporation kinetics of sessile saline droplets. Observations reveal that presence of solvated ions leads to modulated evaporation kinetics, which is further a function of surface wettability. On hydrophilic surfaces, increasing salt concentration leads to enhanced evaporation rates, whereas on superhydrophobic surfaces, it first enhances and reduces with concentration. Also, the nature and extents of the evaporation regimes (constant contact angle or constant contact radius) are dependent on the salt concentration. The reduced evaporation on superhydrophobic surfaces has been explained based on observed (via microscopy) crystal nucleation behaviour within the droplet. Purely diffusion driven evaporation models are noted to be unable to predict the modulated evaporation rates. Further, the changes in the surface tension and static contact angles due to solvated salts also cannot explain the improved evaporation behaviour. Internal advection is observed (using PIV) to be generated within the droplet and is dependent on the salt concentration. The advection dynamics has been used to explain and quantify the improved evaporation behaviour by appealing to the concept of interfacial shear modified Stefan flows around the evaporating droplet. The analysis leads to accurate predictions of the evaporation rates. Further, another scaling analysis has been proposed to show that the thermal and solutal Marangoni advection within the system leads to the advection behaviour. The analysis also shows that the dominant mode is the solutal advection and the theory
Dissipative signatures of dynamical phases and transitions

Living and non-living active matter consumes energy at the microscopic scale to drive emergent, macroscopic behavior including traveling waves and coherent oscillations. Recent work has characterized non-equilibrium systems by their total energy dissipation, but little has been said about how dissipation manifests in distinct spatiotemporal patterns. We introduce a novel measure of dissipation we term the entropy production factor (EPF) to quantify how time reversal symmetry is broken in field theories across scales. We use the EPF to characterize a dynamical phase transition in simulations of the Brusselator, a prototypical biochemically motivated non-linear oscillator. The EPF quantifies the transition of dissipation from local to global coherent oscillations in space and time and integrating it gives a bound on the entropy production rate. The EPF measures how microscopic irreversibility propagates to larger length and time scales and integration through frequency space bounds the net dissipation of stochastic processes.
Financial ratios and stock returns reappraised through a topological data analysis lens

Firm financials are well-established predictors of stock returns, being the basis for both the traditional econometric, and growing Machine Learning, asset pricing literature. Employing topological data analysis ball mapper (TDABM), we revisit the association between seven of the most commonly studied financial ratios and stock returns. Upon outlining the methodology to the finance literature, this paper offers three key contributions to the study of asset pricing. Firstly, the characteristic space is visualised to showcase non-monotonic relationships in multiple dimensions that were as yet unseen. Secondly, the means through which neural networks and random forest regressions fit stock returns is also visualised, showing where Machine Learning is contributing to understanding. Finally, an initial application of TDABM for the segmentation of the cross-section is posited, with significant abnormal returns identified. Collectively these three expositions signpost the value of TDABM for financial researchers and practitioners alike. The scope for benefit is limited only by the availability of information to the analyst.
Regularized Fine-grained Meta Face Anti-spoofing

Face presentation attacks have become an increasingly critical concern when face recognition is widely applied. Many face anti-spoofing methods have been proposed, but most of them ignore the generalization ability to unseen attacks. To overcome the limitation, this work casts face anti-spoofing as a domain generalization (DG) problem, and attempts to address this problem by developing a new meta-learning framework called Regularized Fine-grained Meta-learning. To let our face anti-spoofing model generalize well to unseen attacks, the proposed framework trains our model to perform well in the simulated domain shift scenarios, which is achieved by finding generalized learning directions in the meta-learning process. Specifically, the proposed framework incorporates the domain knowledge of face anti-spoofing as the regularization so that meta-learning is conducted in the feature space regularized by the supervision of domain knowledge. This enables our model more likely to find generalized learning directions with the regularized meta-learning for face anti-spoofing task. Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Extensive experiments on four public datasets validate the effectiveness of the proposed method.
Automatic Contract Insertion with CCBot

Existing static analysis tools require significant programmer effort. On large code bases, static analysis tools produce thousands of warnings. It is unrealistic to expect users to review such a massive list and to manually make changes for each warning. To address this issue we propose CCBot (short for CodeContracts Bot), a new tool that applies the results of static analysis to existing code through automatic code transformation. Specifically, CCBot instruments the code with method preconditions, postconditions, and object invariants which detect faults at runtime or statically using a static contract checker. The only configuration the programmer needs to perform is to give CCBot the file paths to code she wants instrumented. This allows the programmer to adopt contract-based static analysis with little effort. CCBot's instrumented version of the code is guaranteed to compile if the original code did. This guarantee means the programmer can deploy or test the instrumented code immediately without additional manual effort. The inserted contracts can detect common errors such as null pointer dereferences and out-of-bounds array accesses. CCBot is a robust large-scale tool with an open-source C# implementation. We have tested it on real world projects with tens of thousands of lines of code. We discuss several projects as case studies, highlighting undiscovered bugs found by CCBot, including 22 new contracts that were accepted by the project authors.
ASSESSMENT OF THE EFFECT OF EDUCATION PROGRAM ON KNOWLEDGE, ATTITUDE AND PREVENTIVE BEHAVIORS OF THE BARBERS IN BABOL ABOUT HEPATITIS B (2003)

Introduction: Hepatitis are the most prevalent liver diseases. After tuberculosis and malaria, hepatitis is the most prevalent contagious disease in the world. Nearly 350 millions people in the world are chronic carriers of hepatitis B and 3 of our people in Iran are carriers of this virus (approximately 2, 500.000). Hepatitis B is the primary factor of chronic hepatitis, liver deficiency and liver cancer in Iran. The prevalence rate of hepatitis B in barbers compared with other groups is higher and the first general action towards preventing hepatitis B is health education to general population and especially to the groups at risk. Therefore, the aim of this study is to determine the effect of education on knowledge, attitude and preventive behaviors of barbers in Babol. Methods: We conducted an interventional study with quasi experiment in which 80 individuals of barbers in Babol were chosen by using random systematic method. The tool for gathering information was: a questionnaire filled out with interview and observation. The questionnaire has four parts: The first part deals with the individual characteristics, the second part deals with the questions based on knowledge and the third part deals with the questions based on attitude and the fourth deals with the preventive actions of hepatitis disease. The interview and observation were done in two stages before and after the health education program. Results: The finding showed that the rate of good knowledge before the education was 32.5 and it increased up to 93.8 after the education and the positive attitude (81 to 100) and suitable health behaviors after the education showed an increase rate from 7.5 to 18.8. As a whole, there was a significant (p<0.001) between the average of knowledge, attitude and health behaviors, before and after the education. Conclusion: The results of this study indicated that education has an effective role on the promotion of knowledge, attitude and health behaviors of barbers regarding hepatitis B. Therefore it is advised that we should consider to have re-educational program and continuing education programs for barbers by our health workers in case of contagious disease especially hepatitis B
Neural Random Forest Imitation

We present Neural Random Forest Imitation - a novel approach for transforming random forests into neural networks. Existing methods produce very inefficient architectures and do not scale. In this paper, we introduce a new method for generating data from a random forest and learning a neural network that imitates it. Without any additional training data, this transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is fully differentiable and can be combined with the feature extraction in a single pipeline enabling further end-to-end processing. Experiments on several real-world benchmark datasets demonstrate outstanding performance in terms of scalability, accuracy, and learning with very few training examples. Compared to state-of-the-art mappings, we significantly reduce the network size while achieving the same or even improved accuracy due to better generalization.
Deep learning for Sunyaev–Zel’dovich detection in Planck

The Planck collaboration has extensively used the six Planck HFI frequency maps to detect the Sunyaev–Zel’dovich (SZ) effect with dedicated methods, for example by applying (i) component separation to construct a full-sky map of the y parameter or (ii) matched multi-filters to detect galaxy clusters via their hot gas. Although powerful, these methods may still introduce biases in the detection of the sources or in the reconstruction of the SZ signal due to prior knowledge (e.g. the use of the generalised Navarro, Frenk, and White profile model as a proxy for the shape of galaxy clusters, which is accurate on average but not for individual clusters). In this study, we use deep learning algorithms, more specifically, a U-net architecture network, to detect the SZ signal from the Planck HFI frequency maps. The U-net shows very good performance, recovering the Planck clusters in a test area. In the full sky, Planck clusters are also recovered, together with more than 18 000 other potential SZ sources for which we have statistical indications of galaxy cluster signatures, by stacking at their positions several full-sky maps at different wavelengths (i.e. the cosmic microwave background lensing map from Planck, maps of galaxy over-densities, and the ROSAT X-ray map). The diffuse SZ emission is also recovered around known large-scale structures such as Shapley, A399–A401, Coma, and Leo. Results shown in this proof-of-concept study are promising for potential future detection of galaxy clusters with low SZ pressure with this kind of approach, and more generally, for potential identification and characterisation of large-scale structures of the Universe via their hot gas.
Four accuracy bounds and one estimator for frequency estimation under local differential privacy

We present four lower bounds on the mean squared error of both frequency and distribution estimation under Local Differential Privacy (LDP). Each of these bounds is stated in a different mathematical `language', making them all interesting in their own right. Two are particularly important: First, for a given LDP protocol, we give formulas for the optimal frequency and distribution estimators for the aggregator. This theoretically `solves' the issue of postprocessing, although it may be computationally infeasible in practice. We present methods to approximate the optimal estimator, yielding an estimator that outperforms state of the art methods. Second, we give a lower bound on MSE in terms of the LDP parameter $\varepsilon$, giving a quantitative statement of the privacy-utility tradeoff in LDP. In the high privacy domain, this improves on existing results by giving tighter constants even where earlier results consider a worst-case error metric, or giving constants where up to now, no constants were known.
Trajectory growth lower bounds for random sparse deep ReLU networks

This paper considers the growth in the length of one-dimensional trajectories as they are passed through random deep ReLU networks. We generalise existing results, providing an alternative, simpler method for lower bounding expected trajectory growth through random networks, for a more general class of weights distributions, including sparsely connected networks. We illustrate this approach by deriving bounds for sparseGaussian, sparse-uniform, and sparse-discrete-valued random nets. We prove that trajectory growth can remain exponential in such networks, with the sparsity parameter appearing in the base of the exponent.
Quantum-Chemistry Based Design of Halobenzene Derivatives With Augmented Affinities for the HIV-1 Viral G4/C16 Base-Pair

The HIV-1 integrase (IN) is a major target for the design of novel anti-HIV inhibitors. Among these, three inhibitors which embody a halobenzene ring derivative (HR) in their structures are presently used in clinics. High-resolution X-ray crystallography of the complexes of the IN-viral DNA transient complex bound to each of the three inhibitors showed in all cases the HR ring to interact within a confined zone of the viral DNA, limited to the highly conserved 5′CpA 3′/5′TpG 3′ step. The extension of its extracyclic CX bond is electron-depleted, owing to the existence of the “sigma-hole. ” It interacts favorably with the electron-rich rings of base G4. We have sought to increase the affinity of HR derivatives for the G4/C16 base pair. We thus designed thirteen novel derivatives and computed their Quantum Chemistry (QC) intermolecular interaction energies (ΔE) with this base-pair. Most compounds had ΔE values significantly more favorable than those of the HR of the most potent halobenzene drug presently used in clinics, Dolutegravir. This should enable the improvement in a modular piece-wise fashion, the affinities of halogenated inhibitors for viral DNA (vDNA). In view of large scale polarizable molecular dynamics simulations on the entirety of the IN-vDNA-inhibitor complexes, validations of the SIBFA polarizable method are also reported, in which the evolution of each ΔE(SIBFA) contribution is compared to its QC counterpart along this series of derivatives.
Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families

The correspondence between residual networks and dynamical systems motivates researchers to unravel the physics of ResNets with well-developed tools in numeral methods of ODE systems. The Runge-Kutta-Fehlberg method is an adaptive time stepping that renders a good trade-off between the stability and efficiency. Can we also have an adaptive time stepping for ResNets to ensure both stability and performance? In this study, we analyze the effects of time stepping on the Euler method and ResNets. We establish a stability condition for ResNets with step sizes and weight parameters, and point out the effects of step sizes on the stability and performance. Inspired by our analyses, we develop an adaptive time stepping controller that is dependent on the parameters of the current step, and aware of previous steps. The controller is jointly optimized with the network training so that variable step sizes and evolution time can be adaptively adjusted. We conduct experiments on ImageNet and CIFAR to demonstrate the effectiveness. It is shown that our proposed method is able to improve both stability and accuracy without introducing additional overhead in inference phase.