Miracle-dz/UniFAR-FTUs huggingface dataset:
Decadal Variations in the Pacific Meridional Mode

This study examines decadal variations in the Pacific Meridional Mode (MM), a phenomenon known to arise from the coupling between surface winds and sea surface temperatures in the subtropical Pacific. The MM coupling strength has increased during the past four decades in a step-wise manner, with the first increase around 1976 and the second around 1993. The 1976-enhancement is accompanied by an intensification of the Aleutian Low and the 1993-enhancement is accompanied by an intensification of the Pacific Subtropical High. Evidence is presented to show that the 1976-enhancement is related to a phase change in the Pacific Decadal Oscillation (PDO), while the 1993-enhancement is related to a phase change in the Atlantic Multi-decadal Oscillation (AMO). The recent emergence of the Central Pacific El Niño, which is often preceded by the MM, is a consequence of the joint influences of the PDO and AMO. Key word: Pacific Meridional Mode, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation
University of Birmingham The Sync/de-Sync Model: How a synchronized hippocampus and a de-synchronized neocortex code memories

Neuraloscillationsareimportantformemoryformationinthebrain.Thedesynchronizationofalpha(10Hz)oscillationsintheneocortex hasbeenshowntopredictsuccessfulmemoryencodingandretrieval.However,whenengaginginlearning,ithasbeenfoundthatthe hippocampussynchronizesintheta(4Hz)oscillations,andthatlearningisdependentonthephaseoftheta.Thisinconsistencyasto whethersynchronizationis“good”formemoryformationleadstoconfusionoverwhichoscillationsweshouldexpecttoseeandwhere duringlearningparadigmexperiments.Thispaperseekstorespondtothisinconsistencybypresentinganeuralnetworkmodelofhow awellfunctioninglearningsystemcouldexhibitbothofthesephenomena,i.e.,desynchronizationofalphaandsynchronizationoftheta duringsuccessfulmemoryencoding. Wepresentaspikingneuralnetwork(theSync/deSyncmodel)oftheneocorticalandhippocampalsystem.Thesimulatedhippocam-puslearnsthroughanadaptedspike-timedependentplasticityrule,inwhichweightchangeismodulatedbythephaseofanextrinsically generatedthetaoscillation.Additionally,aglobalpassiveweightdecayisincorporated,whichisalsomodulatedbythetaphase.Inthis way,theSync/deSyncmodelexhibitsthetaphase-dependentlong-termpotentiationandlong-termdepression.Wesimulatedalearning paradigmexperimentandcomparedtheoscillatorydynamicsofourmodelwiththoseobservedinsingle-cellandscalp-EEGstudiesofthe medialtemporallobe.OurSync/deSyncmodelsuggeststhatboththedesynchronizationofneocorticalalphaandthesynchronizationof hippocampalthetaarenecessaryforsuccessfulmemoryencodingandretrieval
Development of English Language Camp Module: Reception and Perception of Asnaf Pupils in Malaysia

The Asnaf-community in Malaysia, the financially underprivileged group of the Muslim society and thus the recipients of the zakat, typically represents the poor and the needy. Many of them are uneducated and therefore have limited knowledge to teach their children. Being poor, they could not afford to provide tuition classes for their children. This often resulted in their children having low academic achievement in school, particularly in the study of English as a second language.Some of these parents resort to sending their children to charity homes so that they could be looked after by more capable adults, and also be taught in the learning of English as a second language. A language camp module was developed to facilitate the children of a charity home in Selangor in building their confidence and motivation to learn English. It is important however that the module be evaluated. The aim of this study is to understand the reception and perception of the Asnaf-children towards the English Language Camp Module. This qualitative case study was conducted on 12 Asnaf-pupils. Focus group interviews were conducted to obtain their perception. The main results indicate that the language camp module has enhanced the students’ motivation in learning English and developed their confidence in speaking the language. Such a module therefore seems suitable not just for students who wish to improve on their language proficiency, but also for schools in the development of their school curriculum. The main implication of this study is that the language camp module could provide a step-by-step guidance to any English language camp facilitator in carrying out language activities in other similar contexts aimed at increasing language proficiency level.
The Effect of Social Media Marketing on Luxury Brand Customer Equity among Young Adults

Previous studies have shown how young adults give high priority to brand consumption. The current study examines the influence of social media marketing by a luxury brand on young adults’ customer equity and purchase intention. Based on an online survey of 225 young adult respondents, social media marketing was found to have a positive effect on value equity, brand equity, and relationship equity. Contrary to previous research, the study also found that young adult luxury brand customers place higher importance on value equity and relationship equity than brand equity as both significantly and directly influence customer equity while brand equity only indirectly influences customer equity.
Spectral Clustering Based Classification Algorithm for Text Classification

: The main aim of text categorization is the classification of documents into a fixed number of predefined categories. In text categorization, the dimensionality of the feature vector is usually high. Various approaches have been proposed to reduce the dimensionality of the feature vector while performing automatic text categorization. We propose a Spectral Clustering-based Classification algorithm that reduces the dimensionality of a feature vector. In order to reduce the calculation cost, an incremental method is added in the present algorithm. The algorithm is applied to several text classification problems. The results show it is more effective and more accurate than the traditional active learning algorithm .
Using a Genetic Algorithm with Options Data to Forecast Stocks

The possibility of profit coming from financial markets motivates a lot of efforts in financial forecasting. Many researches try to find information from big amounts of data, because access to data are became increasingly easier. This paper presents an innovate solution to make market forecasting. The system built applies technical indicators to options data in order to predict future market movements in the stocks market. We use a two steps optimization to find the best trading rules that leads to higher returns. Each of this two steps has a Genetic Algorithm optimizing trading rules during a training period. After that, the rules are tested and compared to index performance and funds benchmarks. Our solution has better performances than all of them. In a year when index has 0.70 % of loss, our system achieves 3.52 % of return.
ONLINE APPENDIX A Generalized Model of Advertised Sales

would i) strictly increase firm i’s profits per-consumer as π′ i(u) < 0 for ui > u , but ii) never reduce the number of consumers that it trades with, since non-advertised offers are unobserved to consumers and consumers can only visit one firm. Third, for any tie-break probability, xi(T ) ∈ [0, 1], setting ui = u and ηi = 1 with positive probability is strictly dominated by setting ui = u m and ηi = 0 . Given ui = u , moving from advertising to not advertising would i) strictly reduce firm i’s advertising costs, Ai > 0, and ii) never reduce the number of consumers that it trades with since xi(T ) is independent of advertising decisions via Assumption X.
Triple Diagnostic Accuracy on Early Stage Breast Cancer at dr. Cipto Mangunkusumo and Persahabatan General Hospital

Introduction. Breast cancer is the most common cancer in Indonesia with incidence rate 40.3 per 100.000 women and mortality rate 16.6 per 100.000 women. On early stage, the decision for operative procedure (i.e. mastectomy) requires intraoperative frozen section to assess malignancy; which is mostly unavailable in secondary hospitals. The triple diagnostic (TD) test consists of physical examination, breast ultrasonography and fine needle aspiration biopsy is an accurate and simple preoperative diagnostic method that may solve the problem. The study aimed to find out conformance of the triple diagnostic to histopathology findings in those with breast lump where the malignancy was suspected. Method. A study of diagnostic accuracy conducted enrolling subjects with suspected malignant breast lump managed in dr Cipto Mangunkusumo General Hospital (RSCM) and Persahabatan Hospital (RSP) in period of February 2016 to August 2017 who met the criteria: those underwent preoperative triple diagnostic, intraoperative frozen section and histopathology examination. The conformance of TD and frozen section were compared to histopathology findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were the focuses of the study. Results. There were 33 subjects enrolled (prevalence of 4.3%), mean age of 49.6 years ± 10.9, were above 40 years (78.8%). Tumor size of 2–5 cm found in 63.6% subjects, and the most histopathology finding was invasive carcinoma (84.8%). Frozen section showed sensitivity of 96.8%, specificity of 100%, PPV of 100%, NPV of 66.7% and accuracy of 97.0%. TD showed sensitivity of 77.4%, specificity of 100%, PPV of 100%), NPV of 22.2% and accuracy of 78.8% (p = 0.016). Conclusion. Triple diagnostic reaches up to 78% accuracy on early stage breast cancer may be used secondary hospital in Indonesia whenever frozen section is unavailable.
Profiles of cloud condensation nuclei, dust mass concentration, and INP-relevant aerosol properties in the Saharan Air Layer over Barbados from polarization lidar and airborne in situ measurements

The present study aims to evaluate lidar retrievals of cloud-relevant aerosol properties by using polarization lidar and coincident airborne in situ measurements in the Saharan Air Layer over the Barbados region. Vertical profiles of the number concentration of cloud condensation nuclei (CCN), large particles (diameter d>500 nm), surface area, and ice nucleating particles (INP) are derived from the lidar measurements and compared with CCN concentrations and the INP-relevant aerosol properties measured in situ with aircraft. The measurements were performed in the framework of the Saharan Aerosol Long5 range Transport and Aerosol–Cloud-interaction Experiment (SALTRACE) in summer 2013. The CCN number concentrations derived from lidar observations were up to a factor of two higher than the ones measured in situ on board the research aircraft Falcon. Possible reasons for the difference are discussed. The number concentration of particles with dry radius >250 nm and the surface area concentration obtained from the lidar observations and used as input for the INP parameterizations agreed well (<30–50% deviation) with the aircraft measurements. In a pronounced lofted dust layer 10 during summer (10 July 2013), the lidar retrieval yielded 100–300 CCN per cm at 0.2% water supersaturation and 10–200 INP per L at –25◦C. During the SALTRACE winter campaign (March 2014), the dust layer from Africa was mixed with smoke particles which dominated the CCN number concentration. This example highlights the unique lidar potential to separate smoke and dust contributions to the CCN reservoir and thus to identify the sensitive role of smoke in trade wind cumuli developments over the 15 tropical Atlantic during the winter season.
University of Birmingham Relational aggression in adolescents with conduct disorder

: As most research on conduct disorder (CD) has been conducted on male 3 participants, it has been suggested that female-specific symptoms may be underestimated 4 based on current DSM-5 criteria. In particular, relational aggression, i.e. the hurtful, often 5 indirect, manipulation of relationships with the intention of damaging the other’s social 6 position, has been proposed as a characteristic of CD that is more common in females. In 7 addition, sex-specific studies on correlates of relational aggressive behavior are lacking. 8 Relational aggression may be strongly related to the correlates of proactive aggression, 9 namely low affective empathy, and high levels of callous-unemotional (CU) traits and 10 relational victimization. Thus, the present study investigated sex differences in relational 11 aggression, and associations between relational aggression and correlates of proactive 12 aggression in 662 adolescents with CD (403 females) and 849 typically-developing controls 13 (568 females) aged 9-18 years ( M = 14.74, SD = 2.34) from the European multi-site 14 FemNAT-CD study. Females with CD showed significantly higher levels of relational 15 aggression compared to males with CD, whereas no sex differences were seen in controls. 16 Relational aggression was only partly related to correlates of proactive aggression in CD: 17 Independent of sex, CU traits showed a positive association with relational aggression. In 18 females only, cognitive, but not affective empathy, was negatively associated with relational 19 aggression. Relational victimization was more strongly associated with relational aggression 20 in males compared to females. Despite interesting sex specific correlates of relational 21 aggression, effects are small and the potential clinical implications should be investigated in 22 future studies. 23
EMITTANCE MEASUREMENTS AT THE NEW UNILAC PRE-STRIPPER USING A PEPPER-POT WITH A PC-CONTROLLED CCD-CAMERA

The software was designed to provide the interconnection between all parts of the installed measuring hardware, a user friendly interface for hardware control and a detailed graphical output for the obtained results. Figure 1 shows the general structure of the software. To control the CCD camera the manufacturer developed system drivers and DLL’s have been used. This offers the operator the possibility to change the operation mode of the camera, the exposure time, the resolution and some other parameters related to the actual parameters of the ion beam or the calibration signal. Furthermore, in case of low intensity of the observed light spots it is possible to integrate a series of single shots to improve the quality of the images. The objective of the CCD-camera is controlled by means of the Tiger-BASIC based microprocessor system, which has been connected to the PC through a conventional RS485 interface. Therefore, no additional system driver had to be installed. The collected data may be processed immediately via the “Calculation routines DLL” for evaluation of emittances or may be stored on the hard disk in binary or ASCII format for later calculations. Additionally, data may be saved and reloaded in Windows bitmap format. Information about a current measurement, including commentary and preview pictures, are stored in the local database to provide easiest and systematic access to all results. Information about the kind of accelerated ions, their charges, energy etc., is obtained from the global GSI database.
A simple microbiological assay for the stereospecific differentiation of α and β isomers of arteether

A new rapid bioassay has been developed which can precisely differentiate between stereospecific α and β isomers of the antimalarial drug arteether. This method was developed through the disc diffusion bioactivity tests wherein semisynthetically produced α arteether was able to inhibit the growth of E. coli strains which are defective in DNA gyrase enzyme. The wild type E. coli with intact DNA gyrase did not show this sensitivity to α arteether. The β isomer of arteether was, however, ineffective against both the mutant and wild type strains. Direct experimental proof of gyrase involvement was obtained through mobilization of gyr genes by transformation of E. coli gyr‐ mutant strains with wild type gyrA clone pMK90 (carried on the thermo‐inducible λ Col E1 vector). This resulted in α arteether resistant and nalidixic acid sensitive phenotype clearly demonstrating the use of gyrA mutant strains in differentiating α and β isomers of arteether by this simple bioassay. Copyright © 2000 John Wiley & Sons, Ltd.
Two-well linearization for solid-solid phase transitions

In this paper we consider nonlinearly elastic, frame-indifferent, and singularly perturbed two-well models for materials undergoing solid-solid phase transitions in any space dimensions, and we perform a simultaneous passage to sharp-interface and small-strain limits. Sequences of deformations with equibounded energies are decomposed via suitable Caccioppoli partitions into the sum of piecewise constant rigid movements and suitably rescaled displacements. These converge to limiting partitions, deformations, and displacements, respectively. Whereas limiting deformations are simple laminates whose gradients only attain two values, the limiting displacements belong to the class of special functions with bounded variation (SBV). The latter feature elastic contributions measuring the distance to simple laminates, as well as jumps associated to two consecutive phase transitions having vanishing distance, and thus not being detected by the limiting deformations. By Gamma-convergence we identify an effective limiting model given by the sum of a quadratic linearized elastic energy in terms of displacements along with two surface terms. The first one is proportional to the total length of interfaces created by jumps in the gradient of the limiting deformation. The second one is proportional to twice the total length of interfaces created by jumps in the limiting displacement, as well as by the boundaries of limiting partitions. A main tool of our analysis is a novel two-well rigidity estimate which has been derived in [32] for a model with anisotropic second-order perturbation.
A Monte Carlo Approach to Closing the Reality Gap

We propose a novel approach to the 'reality gap' problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate paired roll-outs to identify points of divergence in behavior. These are used to generate {\it state-space kernels} that coerce the simulation into behaving more like observed reality. The method was evaluated using ROS/Gazebo for simulation and a heavily modified Traaxas platform in outdoor deployment. The results support not just that the kernel approach can force the simulation to behave more like reality, but that the modification is such that an improved control policy tested in the modified simulation also performs better in the real world.
Molecular mapping of the short awn 2 (lks2) and dense spike 1 (dsp1) genes on barley chromosome 7H

The short awn 2 (lks2) and dense spike 1 (dsp1) genes are unique to East Asian barley. These two spike-related morphological genes are important because they may be involved in stable production and local adaptation. As a first step of their positional cloning, molecular mapping was conducted in 98 F2 plants derived from a cross between Karafuto Zairai and Aizu Hadaka 3. The dsp1 gene was mapped to the proximal region of the short arm of chromosome 7H. The lks2 gene was located on the long arm of 7H and flanked by EST-based markers k04151 and k06123, with distances of 0.5 cM in the proximal side and 1.0 cM in the distal side. Both k04151 and k06123 shared homology to rice genes on chromosome 6 that were separated with the physical distance of 5.6 Mbp. In this interval, rice–barley microsynteny was exploited for marker enrichment. Of 57 rice genes attempted, 15 (26.3%) yielded polymorphic EST-based markers. Breakdown of collinearity was found in the candidate region of lks2, suggesting occurrence of structural changes in the chromosome region harboring lks2 during divergence of barley and rice from a common ancestor.
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

In this work, we develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to calculate the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively alleviate the adverse impact caused by the cluttered background and large intra-class appearance variations. To implement <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq1-3217373.gif"/></alternatives></inline-formula>-shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. Our extensive experiments validate the effectiveness of our algorithm which outperforms state-of-the-art methods by a significant margin on five widely used few-shot classification benchmarks, namely, miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), Caltech-UCSD Birds-200-2011 (CUB), and CIFAR-FewShot (CIFAR-FS). We also demonstrate the effectiveness of our method on the image retrieval task in our experiments.
Machine Learning on Graphs: A Model and Comprehensive Taxonomy

There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure. The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning. The third, graph neural networks, aims to learn differentiable functions over discrete topologies with arbitrary structure. However, despite the popularity of these areas there has been surprisingly little work on unifying the three paradigms. Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. We propose a comprehensive taxonomy of representation learning methods for graph-structured data, aiming to unify several disparate bodies of work. Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs (e.g. GraphSage, Graph Convolutional Networks, Graph Attention Networks), and unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc) into a single consistent approach. To illustrate the generality of this approach, we fit over thirty existing methods into this framework. We believe that this unifying view both provides a solid foundation for understanding the intuition behind these methods, and enables future research in the area.