Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Cube-Pair Network for Hyperspectral Imagery Classification

Advanced classification methods, which can fully utilize the 3D characteristic of hyperspectral image (HSI) and generalize well to the test data given only limited labeled training samples (i.e., small training dataset), have long been the research objective for HSI classification problem. Witnessing the success of deep-learning-based methods, a cube-pair-based convolutional neural networks (CNN) classification architecture is proposed to cope this objective in this study, where cube-pair is used to address the small training dataset problem as well as preserve the 3D local structure of HSI data. Within this architecture, a 3D fully convolutional network is further modeled, which has less parameters compared with traditional CNN. Provided the same amount of training samples, the modeled network can go deeper than traditional CNN and thus has superior generalization ability. Experimental results on several HSI datasets demonstrate that the proposed method has superior classification results compared with other state-of-the-art competing methods.
Population and feeding structure of Steindachnerina notonota Miranda-Ribeiro, 1937 (Actinopterygii, Characiformes, Curimatidae) in Taperoá II dam, semi-arid region of Paraíba, Brazil

AIM: The present study investigated population structure, weight-length relationships, sex ratio and associations between feeding activity and reproductive period, diet and alimentary regime of Steindachnerina notonota at Taperoa II dam, semi-arid region of Paraiba State. METHODS: The region has the lowest pluviometric rates of Brazil. Fishes were collected during six sampling periods (October and December/2005, and March, June, August and October of 2006) encompassing two dry periods, and one rainy period. Cast, seine and gill nets were used to collect the individuals. RESULTS: Throughout the study period 120 individuals were collected, with total length ranging between 2.2 and 10.7 cm and weight between 0.31 and 43.1 g. A recruitment period, identified due to a higher abundance of small individuals in the population, was observed in the beginning of the rainy period of 2006, lasting until the beginning of the dry season. S. notonota showed a negative allometric growth type, i.e., individuals 'elongate' during their development. A significantly higher proportion of females were observed throughout the study period. The relationship between feeding and reproductive activities were not significant, given that individuals showed high feeding rates regardless of the intensity of their reproductive activities. Although the diet of S. notonota comprised several items such as plant debris, eggs, organic matter, phytoplankton, zooplankton, Nematoda, Ostracada, Conchostraca, among others, the most important feeding item was detritus, suggesting a predominant detritivirous feeding habit. CONCLUSIONS: Biological aspects of S. notonota observed in the present study suggest that this species has potential to attain great population sizes, as consequence of both diet and feeding dynamics, as well as its reproductive characteristics and growth type.
Women in Canada: A Gender-based Statistical Report

On February 22, 2017, Statistics Canada released the chapter ‘The girl child’ from the publication Women in Canada: A Gender-based Statistical Report. The chapter explores the diverse circumstances and experiences of girls aged 17 and under. The chapter describes the demographic characteristics of girls in Canada and presents several topics related to their well-being including: living arrangements, socioeconomic conditions, physical health and development, mortality, emotional and social health and development, child care, school readiness, education, and personal security. Data on Aboriginal children can be found in the following sections: Diversity among girls and boys in Canada, Living arrangements, and Socioeconomic conditions. http://www.statcan. gc.ca/pub/89-503-x/2015001/article/14680-eng.pdf
Using pre-lecture activities to enhance learner engagement in a large group setting

The disadvantage to students of beginning a module with no prior knowledge or inaccurate knowledge is well documented. For learners, the development of the necessary prior knowledge to facilitate their learning is essential. The use of screencasts, whether prior to or during class, is becoming more widespread. There is a need, however, to better understand how these are used and whether or not there is any impact on overall learner engagement and academic achievement when a component with instantaneous feedback (such as a multiple choice quiz) is embedded into the pre-lecture screencast activity. In this study, pre-learning activities consisting of screencasts and multiple choice quizzes were introduced to improve student engagement with the topic, gauge common misconceptions and give timely feedback to the students. An examination of screencast usage indicated that students did not predominantly nor exclusively employ the resources as originally intended, that is, in advance of lectures. Rather, students continued to access the activities across the module and often after the associated lecture. Implications are discussed with an acknowledgement of the importance of taking into account how learners prefer to use resources when designing and introducing new activities to modules.
Experience: Android Resists Liberation from Its Primary Use Case

Network connectivity is often one of the most challenging aspects of deploying sensors. In many countries, cellular networks provide the most reliable, highest bandwidth, and greatest coverage option for Internet access. While this makes smartphones a seemingly ideal platform to serve as a gateway between sensors and the cloud, we find that a device designed for multi-tenant operation and frequent human interaction becomes unreliable when tasked to continuously run a single application with no human interaction, a seemingly counter-intuitive result. Further, we find that economy phones cannot physically withstand continuous operation, resulting in a surprisingly high rate of permanent device failures in the field. If these observations hold more broadly, they would make mobile phones poorly suited to a range of sensing applications for which they have been rumored to hold great promise.
Resolving the Origin of Pseudo‐Single Domain Magnetic Behavior

The term “pseudo‐single domain” (PSD) has been used to describe the transitional state in rock magnetism that spans the particle size range between the single domain (SD) and multidomain (MD) states. The particle size range for the stable SD state in the most commonly occurring terrestrial magnetic mineral, magnetite, is so narrow (~20–75 nm) that it is widely considered that much of the paleomagnetic record of interest is carried by PSD rather than stable SD particles. The PSD concept has, thus, become the dominant explanation for the magnetization associated with a major fraction of particles that record paleomagnetic signals throughout geological time. In this paper, we argue that in contrast to the SD and MD states, the term PSD does not describe the relevant physical processes, which have been documented extensively using three‐dimensional micromagnetic modeling and by parallel research in material science and solid‐state physics. We also argue that features attributed to PSD behavior can be explained by nucleation of a single magnetic vortex immediately above the maximum stable SD transition size. With increasing particle size, multiple vortices, antivortices, and domain walls can nucleate, which produce variable cancellation of magnetic moments and a gradual transition into the MD state. Thus, while the term PSD describes a well‐known transitional state, it fails to describe adequately the physics of the relevant processes. We recommend that use of this term should be discontinued in favor of “vortex state,” which spans a range of behaviors associated with magnetic vortices.
Modeling and Forecasting Inflation in Zimbabwe : a Generalized Autoregressive Conditionally Heteroskedastic ( GARCH )

Of uttermost importance is the fact that forecasting macroeconomic variables provides a clear picture of what the state of the economy will be in future (Sultana et al, 2013). Nothing is more important to the conduct of monetary policy than understanding and predicting inflation (Kohn, 2005). Inflation is the scourge of the modern economy and is feared by central bankers globally and forces the execution of unpopular monetary policies. Inflation usually makes some people unfairly rich and impoverishes others and therefore it is an economic pathology that stands in the way of any sustainable economic growth and development. Models that make use of GARCH, as highlighted by Ruzgar & Kale (2007); vary from predicting the spread of toxic gases in the atmosphere to simulating neural activity but Financial Econometrics remains the leading discipline and apparently dominates the research on GARCH. The main objective of this study is to model monthly inflation rate volatility in Zimbabwe over the period July 2009 to July 2018. Our diagnostic tests indicate that our sample has the characteristics of financial time series and therefore, we can employ a GARCH – type model to model and forecast conditional volatility. The results of the study indicate that the estimated model, the AR (1) – GARCH (1, 1) model; is indeed an AR (1) – IGARCH (1, 1) process and is not only appropriate but also the best. Since the study provides evidence of volatility persistence for Zimbabwe’s monthly inflation data; monetary authorities ought to take into cognisance the IGARCH behavioral phenomenon of monthly inflation rates in order to design an appropriate monetary policy.
Functional Level During the First Year After Moderate and Severe Traumatic Brain Injury: Course and Predictors of Outcome

Background : The objective of this study was to describe the functional level during the first year after moderate and severe traumatic brain injury (TBI), and to evaluate the predictive impact of pre-injury and injury-related factors. Methods : A cohort of 65 patients with moderate (N = 21) or severe (N = 44) TBI were examined with FIM (Functional Independence Measure) at admission and discharge from the rehabilitation clinic (on average two months after injury) and at 12 months, and with GOSE (Glasgow Outcome Scale Extended) at 12 months after injury. Possible predictors were analyzed in a regression model using FIM total score at 12 months as outcome. Results : All mean FIM scores improved significantly from injury to discharge from sub-acute rehabilitation. In the later period from discharge to 12 months after injury, the mean FIM motor score improved in severe TBI but not in moderate TBI patients. The mean FIM cognitive scores did not improve in any of the groups. At 12 months, 95% with moderate TBI had a FIM score from 109 - 126 (functionally independent) compared to 74% with severe TBI. Functional global outcome as assessed by GOSE was “good recovery” in 52% with moderate TBI versus 33% in severe TBI, “moderate disability” in 33% with moderate TBI versus 31% in severe TBI, and “severe disability” in 14% with moderate TBI versus 36% in severe TBI. Predictors such as PTA duration (B = -0.209), GCS admission rehabilitation (B = 5.058) and LOS rehabilitation (B = 0.458) explained 47% of the FIM variance 12 months post injury. Conclusions : The greatest improvement after moderate and severe TBI was in the sub-acute phase during the stay in a specialized rehabilitation unit. Residual disability was reported in 47% of moderate TBI patients as measured by GOSE at 12 months post injury indicating the importance of post-acute rehabilitation for these patients. Longer stays at the rehabilitation unit, a short PTA period and a high GCS score at admission to rehabilitation were positive predictors of functional level (FIM) at 12 months follow-up demonstrating that these factors are common predictors of early and late TBI phases. doi:10.4021/jnr20w
Fast A Algorithms for Multiple Sequence Alignment

The multiple alignment of the sequences of DNA and proteins is applicable to various important elds in molecular biology. Although the approach based on Dynamic Programming is well-known for this problem, it requires enormous time and space to obtain the optimal alignment. On the other hand, this problem corresponds to the shortest path problem and the A algorithm, which can e ciently nd the shortest path with an estimator, is usable. This paper directly applies the A algorithm to multiple sequence alignment problem with more powerful estimator in more than two dimensional case and discusses the improvement of this approach utilizing an upper bound of the shortest path length. The algorithm to provide the upper bound is also proposed in this paper.
Persistent gender inequity in us undergraduate engineering: looking to Jordan and Malaysia for factors to their success in achieving gender parity

For more than three decades, the US federal government, industry and professional engineering societies has contributed millions of dollars to increase the number of women in US engineering programs with minimal impact. The research published on how to address the on-going United States (US) national challenge of increasing gender parity in undergraduate engineering programs is almost entirely US centric. The authors of this paper reached across borders and outside the STEM education literature to gain a different perspective on the US problem of persistent gender segregation in undergraduate engineering education. As we compared the issue of gender parity between the US, Jordan and Malaysia, three previously unexplored areas began to take shape: 1. The US has potentially inaccurately scoped the problem, 2. Different factors seem to contribute to greater gender equity in undergraduate engineering programs in Jordan and Malaysia than in the US, and 3. A sociological framework for analysis and interpretation (not previously published in the engineering education literature) helps us better understand the core causes of gender inequity in advanced industrialized countries, such as the US. Once we better understand the core causes, effective solutions can be designed. The purpose of this paper is to begin to re-scope the problem of increasing the number of women in engineering education in the US, identify potential factors that contribute to gender equity in Jordan and Malaysia, and to propose future areas of robust cross-national engineering education research.
Investor Sentiment and Price Discovery: Evidence from the Pricing Dynamics between the Futures and Spot Markets

This study examines the role of investor sentiment in the pricing dynamics between the spot and futures markets. The empirical evidence suggests that investor sentiment has a positive impact on price volatility and the bid–ask spread on both the spot and futures markets, which induces higher arbitrage risk and trading costs during high sentiment periods. Consequently, during high sentiment periods, informed traders become less willing to leverage their information advantages on the futures market, which diminishes the futures markets’ leading informational role and contributions to price discovery. Our findings provide support for the theory of limits to arbitrage.
Revisiting historical climatic signals to better explore the future: prospects of water cycle changes in Central Sahel

Abstract. Rainfall and climatic conditions are the main drivers of natural and cultivated vegetation productivity in the semiarid region of Central Sahel. In a context of decreasing cultivable area per capita, understanding and predicting changes in the water cycle are crucial. Yet, it remains challenging to project future climatic conditions in West Africa since there is no consensus on the sign of future precipitation changes in simulations coming from climate models. The Sahel region has experienced severe climatic changes in the past 60 years that can provide a first basis to understand the response of the water cycle to non-stationary conditions in this part of the world. The objective of this study was to better understand the response of the water cycle to highly variable climatic regimes in Central Sahel using historical climate records and the coupling of a land surface energy and water model with a vegetation model that, when combined, simulated the Sahelian water, energy and vegetation cycles. To do so, we relied on a reconstructed long-term climate series in Niamey, Republic of Niger, in which three precipitation regimes can be distinguished with a relative deficit exceeding 25% for the driest period compared to the wettest period. Two temperature scenarios (+2 and +4 °C) consistent with future warming scenarios were superimposed to this climatic signal to generate six virtual future 20-year climate time series. Simulations by the two coupled models forced by these virtual scenarios showed a strong response of the water budget and its components to temperature and precipitation changes, including decreases in transpiration, runoff and drainage for all scenarios but those with highest precipitation. Such climatic changes also strongly impacted soil temperature and moisture. This study illustrates the potential of using the strong climatic variations recorded in the past decades to better understand potential future climate variations.
Scientific Labor Mobility, Market Value, and Knowledge Flows

This paper studies the relationship between a firm's market value and the mobility of scientific and technical personnel in its sectoral and geographic proximity. I uncover distinct positive and negative effects of mobility on market value that operate through various channels. I document a positive effect of scientific labor mobility on market value through knowledge assets embedded within technologically similar firms: firms having large stocks of external knowledge in their disposal benefit from increased mobility of scientific and technical personnel. On the other hand, firms that lack large external stocks of knowledge suffer a negative effect. The detrimental effect of mobility is larger for firms in more competitive industries, supporting the hypothesis that estimates capture the losses due to outbound knowledge and human capital. However, such losses are not significantly different for firms with different levels of R&D intensity. The positive and negative effects of scientific labor mobility, on average, are of similar magnitudes, making the average firm "break even" in terms of its net impact. These results are consistent with previous findings, and provide further insight into why innovative firms cluster in industrial districts.
Motivated Errors

Myriad environments allow for the possibility of confusion. Agents may appeal to such confusion—or the possibility of making an honest mistake—to justify their behavior. In three sets of experiments involving thousands of subjects, we document evidence of such motivated errors. We document this evidence in a simple environment in which the scope for errors is small and in more complex environments in which subjects display correlation neglect and an anchoring bias. (JEL C91, D12, D64, D81, D83, D91)
The Usefulness of Digital Badges in Higher Education: Exploring the Students’ Perspectives

Many students entering higher education (HE) today have never known life without the internet. By the time students enter HE, many have been exposed to playing digital games and consider them a very useful learning tool. However, utilising gamification for student engagement and student learning in HE has not been investigated thoroughly, and this paper attempts to contribute to this emerging field of study as suggested by Gibson et al. (2015) and Reid et al. (2015). A survey investigating the usefulness of digital badges for student learning and engagement was distributed to two hundred and fifty-seven (275) undergraduate students at the College of Business, University College Dublin. The results suggest that the incorporation of digital badges into a module is beneficial as they can help students organise their study, maintain and track their progress, and motivate them to engage with module content throughout the semester. The survey results also provide some evidence that digital badges can make a positive contribution to student engagement within a module, particularly where they are directly linked with the module assessment requirements. Overall, digital badges have the potential to be a highly effective pedagogical tool that can also positively impact on the learning experience more generally.
Geometry Aware Constrained Optimization Techniques for Deep Learning

In this paper, we generalize the Stochastic Gradient Descent (SGD) and RMSProp algorithms to the setting of Riemannian optimization. SGD is a popular method for large scale optimization. In particular, it is widely used to train the weights of Deep Neural Networks. However, gradients computed using standard SGD can have large variance, which is detrimental for the convergence rate of the algorithm. Other methods such as RMSProp and ADAM address this issue. Nevertheless, these methods cannot be directly applied to constrained optimization problems. In this paper, we extend some popular optimization algorithm to the Riemannian (constrained) setting. We substantiate our proposed extensions with a range of relevant problems in machine learning such as incremental Principal Component Analysis, computating the Riemannian centroids of SPD matrices, and Deep Metric Learning. We achieve competitive results against the state of the art for fine-grained object recognition datasets.
Filtered Likelihood for Point Processes

Point processes are widely used in finance and economics to model the timing of defaults, market transactions, unemployment spells, births, and a range of other events. We develop and analyze likelihood estimators for the parameters of a marked point process and incompletely observed explanatory factors that influence the arrival intensity and mark distribution. We establish an approximation to the likelihood and analyze the convergence and large-sample properties of the associated estimators. Numerical results illustrate the behavior of our estimators.
Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market

We examine the notion that financial products which cater to investors’ behavioral biases can yield high trading activity and thus be profitable for issuers. Our setting considers options with a callback feature, namely, callable bull/bear contracts (CBBCs). Such contracts have high skewness when close to callback and thus appeal to cumulative prospect theory preferences. CBBCs with high skewness earn negative average returns, and issuers’ gross profits vary positively with CBBC skewness. Over the 2009–2014 period, issuers earn gross profits of about $1.67 billion by trading CBBCs on the Hang Seng Index. These findings highlight the role of behavioral finance in financial innovation.
Trade Liberalization and Export Competitiveness: Evidence from the EU-ECOWAS Trade

This study investigates the impact of EU trade liberalisation on the export competitive ness of ECOWAS countries. Trade liberalisation is captured by import tariffs in the EU market whiles export competitiveness is captured by the export value of ECOWAS countries. The study uses the gravity model, fixed and random effects as well as the Hausman test for a country pair panel data of 15 ECOWAS and EU 15 countries for the period beginning 1995 to 2014. The study finds a positive impact of trade liberalisation on export competitiveness and estimates the export value of ECOWAS countries to increase by 0.487% for every 1% reduction of import tariffs in the EU.