Miracle-dz/UniFAR-FTUs huggingface dataset:
DETERMINANTS OF THE CURRENT ACCOUNT IN THE EU AND PIIGS

This paper investigates the relationship between current account balance and national savings, investment,budget deficit, and real exchange rate. Thesedeterminants of the current account are studied empirically byapplying the Generalized Method of Moments for a sample of 24 European countries. The sample data are divided into three different groups, PIIGS, EU-PIIGS, and EU in order to study the group-specific estimations. The main result of the paper is that the determinants of our model are capable of accurately explaining past movements in current accounts for all selected groups. Additionally, current account determinants in the economically weak members, PIIGS, behave slightly differently from the rest of the EU.
Cartesian tasks oriented friction compensation through a reinforcement learning approach

The paper describes an algorithm to compensate for the friction in the robot joints, while executing a target impedance controlled Cartesian task. The proposed method relies on the reinforcement learning procedure: given a target task in the Cartesian space and a joint space friction model, the algorithm is capable to adapt the friction model parameters based on a specified error function. The proposed error function correlates the Cartesian position tracking error to the joint space friction torques, allowing to independently learn the friction parameters for each joint. In such a way, the friction model parameters can be updated in subsequent iterations, compensating for the friction effects. The proposed algorithm has been validated through experiments. A target Cartesian motion has been specified (such as in a pick and place operation) and the proposed method has allowed to learn the friction model parameters. A Universal Robot UR10 has been used as a test platform, developing the impedance control with robot dynamics compensation.
Malware-aware processors: A framework for efficient online malware detection

Security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Therefore, software detectors are applied selectively and at a low frequency, creating opportunities for malware to remain undetected. In this paper, we propose Malware-Aware Processors (MAP) - processors augmented with an online hardware-based detector to serve as the first line of defense to differentiate malware from legitimate programs. The output of this detector helps the system prioritize how to apply more expensive software-based solutions. The always-on nature of MAP detector helps protect against intermittently operating malware. Our work improves on the state of the art in the following ways: (1) We define and explore the use of sub-semantic features for online detection of malware. (2) We explore hardware implementations and show that simple classifiers appropriate for such implementations can effectively classify malware. We also study different classifiers, develop implementation optimizations, and explore complexity to performance trade-offs. (3) We propose a two-level detection framework where the hardware classifier prioritizes the work of a more accurate but more expensive software defense mechanism. (4) We integrate the MAP implementation with an open-source x86-compatible core, synthesizing the resulting design to run on an FPGA.
Validation of the Forage Brassica Calculator: A fertiliser forecasting system for forage brassica crops grown in New Zealand. Pasja and bulb turnips

Forage brassica calculators can be used to assist fertiliser consultants and farmers plan nitrogen (N) and phosphorus (P) applications for site-specific brassica production. The cost of each nutrient and the predicted productivity are used to recommend both economic and environmentally sound application rates. In this study, simulated yield was that modelled by PARJIB (a model that analyses and forecasts yield responses to nutrients) from the soil available nutrient levels, the potential yield and the fertiliser rates. The actual (observed) yields are those measured from the field. Simulations for ‘Pasja’ (R2=0.96; root mean square of the deviation (RMSD) ≤3% (0.25 t/ha)) followed observed data closely with individual harvests at 60 and 90 days after sowing, consistent with observed data (R2>0.93; RMSD≤4% (0.20 t/ha)). The ‘Pasja’ model is sufficiently robust to predict site specific N and P fertiliser requirements. However, the model may need to be extended to more than two grazing events. Simulations for bulb turnips (R2=0.10; RMSD≤28.1% (2.70 t/ha)) was poor. Simulations predicted the same yield for different treatments within sites, while observed yields differed widely (4–12 t/ha). This was because experiments included establishment and seeding method treatments which are not currently accounted for by the calculators. Thus, practical use of the brassica calculators for assisting with fertiliser rates currently depends on other agronomic factors being managed optimally.
Using binaural and spectral cues for azimuth and elevation localization

It is a common assumption that with just two microphones only the azimuth angle of a sound source can be estimated and that a third, orthogonal microphone (or set of microphones) is necessary to estimate the elevation of the source. Recently, using specially designed ears and analyzing spectral cues several researchers managed to estimate sound source elevation with a binaural system. In this work, we show that with two bionic ears both azimuth and elevation angle can be determined using both binaural (e.g. IID and ITD) and spectral cues. This ability can also be used to disambiguate signals coming from the front or back. We present a detailed analysis of both azimuth and elevation localization performance for binaural and spectral cues in comparison. We demonstrate that with a small extension of a standard binaural system a basic elevation estimation capacity can be gained.
Semilinear Predictability Minimization Produces Well-Known Feature Detectors

Predictability minimization (PMSchmidhuber 1992) exhibits various intuitive and theoretical advantages over many other methods for unsupervised redundancy reduction. So far, however, there have not been any serious practical applications of PM. In this paper, we apply semilinear PM to static real world images and find that without a teacher and without any significant preprocessing, the system automatically learns to generate distributed representations based on well-known feature detectors, such as orientation-sensitive edge detectors and off-centeron-surround detectors, thus extracting simple features related to those considered useful for image preprocessing and compression.
Assessing Sustainability in Coffee Farming Systems in Colombia

This study investigated sustainability challenges and benefits for coffee farming with different amounts of shade management in Colombia. Data was collected from literature studies, quantitative soil analyses and interviews with farmers and other experts. The results show that shade management of coffee farms does increase ecological sustainability, but in general gives lower yields of coffee. However, shaded coffee systems have the potential of increasing economic resilience for farmers by providing diversified income possibilities. The low and fluctuating coffee price of the global market has shown to be a major challenge of sustainability for Colombian small scale coffee
Artiﬁcial Intelligence in Medicine

Objectives: To obtain a balanced view on the role and place of expert knowledge and learning methods in building Bayesian networks for medical image interpretation. Methods and materials: The interpretation of mammograms was selected as the example medical image interpretation problem. Medical image interpretation has its own common standards and procedures. The impact of these on two complementary methods for Bayesian network construction was explored. Firstly, methods for the discretisation of continuous features were investigated, yielding multinomial distributions that were compared to the original Gaussian probabilistic parameters of the network. Sec-ondly, the structure of a manually constructed Bayesian network was tested by structure learning from image data. The image data used for the research came from screening mammographic examinations of 795 patients, of whom 344 were cancerous. Results: The experimental results show that there is an interesting interplay of machine learning results and background knowledge in medical image interpretation. Networks with discretised data lead to better classiﬁcation performance (increase in the detected cancers of up to 11.7%), easier interpretation, and a better ﬁt to the data in comparison to the expert-based Bayesian network with Gaussian probabilistic parameters. Gaussian probability distributions are often used in medical image interpretation because of the continuous nature of many of the image features. The structures learnt supported many of the expert-originated relationships but also revealed some novel relationships between the mammographic features. Using discretised features and performing structure learning on the mammographic data has further improved the cancer detection performance of up to 17% compared to the manually constructed Bayesian network model. Conclusion: Finding the right balance between expert knowledge and data-derived knowledge, both at the level of network structure and parameters, is key to using Bayesian networks for medical image interpretation. A balanced approach to building Bayesian networks for image interpretation yields more accurate and understandable Bayesian network models.
Eucalyptus: Integrating Natural Language Input with a Graphical User Interface

Abstract : This report describes Eucalyptus, a natural language (NL) interface that has been integrated with the graphical user interface of the KOALAS Test Planning Tool, a simulated Naval air combat command system. The multimodal, multimedia interface handles both imperative commands and database queries (either typed or spoken into a microphone) while still allowing full use of the original graphical interface. In this way the precision and consistency of direct manipulation is balanced and augmented by the descriptive power and reduced redundancy of NL. The two input media used together yield such powerful interaction techniques as deixis (simultaneous speech and pointing) and the ability to use mouse clicks and verbal referring expressions interchangeably. Finally, the system's discourse handling capability allows abbreviated NL follow-ups (anaphora and ellipsis) to receive full interpretations based on the prior interaction context, whether verbal or graphical. Natural language processing, Human-computer interface, Speech recognition.
LMP 1 Regulates Periodontal Ligament Progenitor Cell Proliferation and Differentiation

LMP1 is an intracellular scaffold protein that contains a PDZ domain and three LIM domains. LMP1 has multiple functions including regulating mesenchymal stem cell (MSC) osteogenesis. Gene delivery of LMP1 induces bone formation in vivo in heterotopic and orthotopic sites. However, little is known about the physiological function and gene regulatory mechanisms of LMP1 in MSCs at the molecular level. Periodontal ligament (PDL) cells are a unique progenitor cell population that can differentiate into multiple cell types, including osteoblasts, adipocytes or chondrocytes. This study sought to determine the physiological function and gene regulatory mechanisms of LMP1 in PDL cells at the molecular level. We show that LMP1 is upregulated in early stage of PDL cell osteogenic differentiation. Stable gene knockdown of LMP1 by shRNA inhibits DNA synthesis and corresponding cell proliferation in PDL cells, and further leads to decreased mineralization in vitro. Overexpression of LMP1 increases cell proliferation, and PDZ and ww-interacting domains are not enough to mediate this effect. Further, we found that in PDL cells, LMP1 is a downstream target gene of TGF-β1 that is an early signal critical in preosteoblast proliferation and differentiation. TGF-β1 stimulates PDL cell proliferation, however, this effect is compromised when LMP1 is knocked down. We further identified that the activation of TAK1-JNK/p38 kinase cascade is involved in the LMP1 gene regulation by TGF-β1. We conclude that LMP1 is a downstream gene of TGFβ1, involved in PDL cell proliferation. Our findings advance the understanding of the physiological function of LMP1, and define a regulatory mechanism of LMP1 in PDL progenitor cells and other MSCs.
A multiuser approach to narrowband cellular communications

We compare three receivers for coded narrowband transmission affected by fading and co-channel interference. The baseline receiver is based on conventional diversity reception with maximal-ratio combining. A multiuser approach allows us to derive a maximum-likelihood multiuser receiver and its reduced-complexity suboptimal version. Finally, a decorrelating diversity receiver, which seeks a tradeoff between performance and complexity, is studied. Closed-form performance parameters are derived for all the proposed receivers in the case of coded coherent PSK and independent frequency nonselective Rayleigh fading.
Semaphorin 4 A enhances lung fibrosis through activation of Akt via PlexinD 1 receptor

Semaphorin 4A plays a regulatory role in immune function and angiogenesis. However, its specific involvement in controlling lung fibrosis, a process that is closely related to angiogenesis and inflammation is still poorly understood. In the present study, we show that treatment of Sema4A on normal lung fibroblasts induces expression of proteins that contribute to a contractile phenotype, including α-smooth muscle actin (α-SMA), ezrin, moesin, and paxillin. We confirm that Sema4A enhances the ability of lung fibroblasts to contract collagen gel. Sema4A treatment led to resistance to apoptosis in normal lung fibroblasts. Relative to normal lung fibroblasts, fibroblasts cultured from scars of patients with the fibrotic disease Systemic Sclerosis (SSc) showed elevated Sema4A secretion, enhanced α-SMA, ezrin, moesin, and paxillin expression, and high ability to induce collagen gel contraction. Using neutralizing antibody against Sema4A receptor, PlexinD1, we found that endogenous Sema4A signalling in SSc fibroblast was through PlexinD1 receptor. We then identified the signalling mechanism through which Sema4A-PlexinD1 promotes the ability of normal fibroblasts to contract a collagen gel matrix. Western blot analysis showed that Sema4A activated the Akt pathway in lung fibroblasts, and the specific inhibitor of Akt pathway, Akt inhibitor III, blocked the ability of Sema4A to promote the ability of lung fibroblasts to contract a collagen gel matrix. Thus, blocking Sema4APlexinD1-Akt cascades might be beneficial in reducing pulmonary fibrosis.
Experiments on quality evaluation of embedded software in Japan robot software design contest

As a practical opportunity for educating Japanese young developers in the field of embedded software development, a software design contest involving the design of software to automatically control a line-trace robot, and conduct running performance tests was held. In this paper,we give the results of the contest from the viewpoint of software quality evaluation. We create a framework for evaluating the software quality which integrated design model quality and the final system performance, and conduct analysis using the framework. As a result of analysis,it is found that the quantitative measurement of the structural complexity of the design models bears a strong relationship to qualitative evaluation of the design conducted by judges. It is also found that there is no strong correlation between design model quality evaluated by the judges and the final system performance. For embedded software development, it is particularly important to estimate and verify reliability and performance in the early stages,using the model. Based on the analysis result,we consider possible remedies with respect to the models submitted,the evaluation methods used and the contest specifications. In order to adequately measure several non-functional quality characteristics including performance on the model,it is necessary to improve the way of developing robot software (such as applying model driven development)and reexamine the evaluation methods.
Twenty Four Approaches for Conservation of Non-Orthodox Agricultural Plant Genetic Resources in Nepal

The conservation of plant genetic diversity underpins the future of agriculture, food and nutrition security and is critical to ensure the ability of future generations to cope with global environmental changes. The conservation efforts were more focused on orthodox crop species in Nepal before 2010. About 40% of agricultural plant genetic resources are non-orthodox, either recalcitrant/ intermediate type or vegetatively propagated plant species. Approaches for conservation of non-orthodox plant species differ from that of orthodox crops. Different conservation approaches have been established by National Agriculture Genetic Resources Center, Nepal for conserving non-orthodox agricultural plant species. We applied household survey, literatures survey, field survey, key informant survey and organized focus group discussion for assessing the different conservation approaches. A total of 24 approaches are being considered for conservation of non-orthodox plant species. Approaches under the ex-situ strategy are field genebank, botanical garden, city park, government farm, religious place, in-vitro culture and evolutionary plant breeding; under on-farm strategy are community field genebank, school field genebank, household field genebank, community/ public orchard, village level field genebank, geographical indication and participatory landrace enhancement, and under in-situ strategy are protected area, Ramsar site, world heritage site, community forest and legal protection for conserving non-orthodox plant species in Nepal. Field genebank is the very good approach, and it has been established in about 20 research stations. It should be extended to government farms and agriculture offices to conserve the local APGRs available in their respective command areas. Databases (passport and characterization) have been generated and will be available online to enhance the utilization in breeding, research and production.
Industrial Robot: An International Journal Emerald Article: A software framework for agricultural and forestry robots

Purpose – The purpose of this paper is to describe a generic software framework for development of agricultural and forestry robots. The primary goal is to provide generic high-level functionality and to encourage distributed and structured programming, thus leading to faster and simplified development of robots. A secondary goal is to investigate the value of several architecture views when describing different software aspects of a robotics system. Design/methodology/approach – The framework is constructed with a hybrid robot architecture, with a static state machine that implements a flow diagram describing each specific robot. Furthermore, generic modules for GUI, resource management, performance monitoring, and error handling are included. The framework is described with logical, development, process, and physical architecture views. Findings – The multiple architecture views provide complementary information that is valuable both during and after the design phase. The framework has been shown to be efficient and time saving when integrating work by several partners in several robotics projects. Although the framework is guided by the specific needs of harvesting agricultural robots, the result is believed to be of general value for development also of other types of robots. Originality/value – In this paper, the authors present a novel generic framework for development of agricultural and forestry robots. The robot architecture uses a state machine as replacement for the planner commonly found in other hybrid architectures. The framework is described with multiple architecture views.
Modeling complex networks with self-similar outerplanar unclustered graphs

This paper introduces a family of modular, self-similar, small-world graphs with clustering zero. Relevant properties of this family are comparable to those of some networks associated with technological systems with a low clustering, like the power grid or some electronic circuits. Moreover, the graphs are outerplanar and it is know that many algorithms that are NP-complete for general graphs perform polynomial in outerplanar graphs. Therefore the graphs constitute a good mathematical model for these systems.
Texture analysis of CT images

The present study has shown some promise in the use of texture for the extraction of diagnostic information from CT images. A number of features are obtained from abdominal CT scans of the liver using the spatial domain statistical texture analysis methods: SGLDM, GLRLM, and GLDM. This study investigated whether (a) the texture could be used to discriminate among the various tissue types that are inaccessible to human perception and, (b) if so, then what are the most useful feature parameters for such an application? The efficacies of the different methods were evaluated from the consistency of the computed values within a class and from their differences with other classes. The study has demonstrated the use of texture for tissue characterization of CT images. In particular, we have been successful in identifying the onset of disease in liver tissue, which can not be recognized even by trained human observers. Three useful features, namely entropy (H), local homogeneity (L) and grey level distribution (GLD), have been found effective for pattern recognition. The performance of these features has been compared on the basis of statistical significance. The results show that, except for L, (Direction 0/spl deg/) all feature parameters perform equally well and detect early malignancy with a confidence level of above 99%-a finding that will not only help in automation, but more importantly, in early detection of malignancy in the liver. >
Doppler tomography of the Little Homunculus: high‐resolution spectra of [Fe II]λ16 435 around Eta Carinae★

High-resolution spectra of [Fe II ] λ16435 around η Carinae provide powerful diagnostics of the geometry and kinematics of the 'Little Homunculus' (LH) growing inside the larger Homunculus nebula. The LH expansion is not perfectly homologous: while low latitudes are consistent with linear expansion since 1910, the polar caps imply ejection dates around 1920-1930. However, the expansion speed of the LH is much slower than the post-eruption wind, so the star's powerful wind may accelerate the LH. With an initial ejection speed of 200 km s -1 in 1890, the LH would have been accelerated to its present speed if the mass is roughly 0.1 M ○. This agrees with an independent estimate of the LH mass based on its density and volume. In any case, an ejection after 1930 is ruled out. Using the LH as a probe of the 1890 event then, it is evident that its most basic physical parameters (total mass and kinetic energy, which are 0.1 M ○. and 10 46.9 erg, respectively) are orders of magnitude less than during the giant eruption in the 1840s. Thus, the ultimate energy sources were different for these two events - yet their ejecta have the same bipolar geometry. This clue may point toward a collimation mechanism separate from the underlying causes of the outbursts.