Miracle-dz/UniFAR-FTUs huggingface dataset:
Looking on the bright side: the impact of ambivalent images on emotion regulation choice

ABSTRACT Previous research has found that people choose to reappraise low intensity images more often than high intensity images. However, this research does not account for image ambivalence, which is presence of both positive and negative cues in a stimulus. The purpose of this research was to determine differences in ambivalence in high intensity and low intensity images used in previous research (experiments 1–2), and if ambivalence played a role in emotion regulation choice in addition to intensity (experiments 3–4). Experiments 1 and 2 found that the low intensity images were more ambivalent than the high intensity images. Experiment 2 further found a positive relationship between ambivalence of an image and reappraisal affordances. Experiments 3 and 4 found that people chose to reappraise ambivalent images more often than non-ambivalent images, and they also chose to reappraise low intensity images more often than high intensity images. These experiments support the idea that ambivalence is a factor in emotion regulation choice. Future research should consider the impact ambivalent stimuli have on emotion regulation, including the potential for leveraging ambivalent stimuli to improve one’s emotion regulation ability.
Fatigue assessment for deck plates in orthotropic bridge decks

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Situational Graphs for Robot Navigation in Structured Indoor Environments

Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph (S-Graph), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment.
SynopSys - large graph analytics in the SAP HANA database through

Graph-structured data is ubiquitous and with the advent of social networking platforms has recently seen a signiﬁcant increase in popularity amongst researchers. However, also many business applications deal with this kind of data and can therefore beneﬁt greatly from graph processing functionality offered directly by the underlying database. This paper summarizes the current state of graph data processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research project SynopSys. With powerful graph pattern matching support at the core, we envision OLAP -like evaluation functionality exposed to the user in the form of easy-to-apply graph summarization templates. By combining them, the user is able to produce concise summaries of large graph-structured datasets. We also point out open questions and challenges that we plan to tackle in the future developments on our way towards large graph analytics.
Uniform sampling of rotations for discrete and continuous learning of 2D shape models

Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.
Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization

based on contourlet transform and multistage vector quantization. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently take advantage of the fact that the edges usually found in fingerprints are smooth curves. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. The computation and storage requirements are the major difficulty in implementing a vector quantizer. In the full-search algorithm, the computation and storage complexity is an exponential function of the number of bits used in quantizing each frame of spectral information. The storage requirement in multistage vector quantization is less when compared to full search vector quantization. The coefficients of contourlet transform are quantized by multistage vector quantization. The quantized coefficients are encoded by Huffman coding. The results obtained are tabulated and compared with the existing wavelet based ones.
QCRI’s Machine Translation Systems for IWSLT’16

This paper describes QCRI’s machine translation systems for the IWSLT 2016 evaluation campaign. We participated in the Arabic→English and English→Arabic tracks. We built both Phrase-based and Neural machine translation models, in an effort to probe whether the newly emerged NMT framework surpasses the traditional phrase-based systems in Arabic-English language pairs. We trained a very strong phrase-based system including, a big language model, the Operation Sequence Model, Neural Network Joint Model and Class-based models along with different domain adaptation techniques such as MML filtering, mixture modeling and using fine tuning over NNJM model. However, a Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, beat our very strong phrase-based system by a significant 2 BLEU points margin in Arabic→English direction. We did not obtain similar gains in the other direction but were still able to outperform the phrase-based system. We also applied system combination on phrase-based and NMT outputs.
Transmission of synthetic seed bacterial communities to radish seedlings: 2 impact on microbiota assembly and plant phenotype 3

12 Seed-borne microorganisms can be pioneer taxa during germination and seedling emergence. 13 Still, the identity and phenotypic effects of these taxa that constitute a primary inoculum of 14 plant microbiota is mostly unknown. Here, we studied the transmission of bacteria from radish 15 seeds to seedlings using the inoculation of individual seed-borne strains and synthetic 16 communities (SynComs) under in vitro conditions. The SynComs were composed of highly 17 abundant and prevalent, sub-dominant or rare bacterial seed taxa. We monitored the 18 transmission of each strain alone or in communities using gyrB gene amplicon sequencing 19 and assessed their impacts on germination and seedling phenotype. 20 All strains and SynComs successfully colonized seedlings and we were able to reconstruct a 21 richness gradient (6, 8 and 12 strains) on both seeds and seedlings. Stenotrophomonas 22 rhizophila became dominant on seedlings of the three SynComs but most strains had variable 23 transmission success (i.e increasing, stable or decreasing during seed to seedling transition) 24 that also depended on the SynCom richness. 25 Some seed-borne strains ( Pseudomonas viridiflava, Paenibacillus sp) had detrimental effects 26 on germination and seedling development. Abnormal seedling morphologies were also 27 observed with SynComs but their proportions decreased at the highest richness level. 28 Interestingly, the three bacterial strains identified as core taxa of radish seeds ( Pseudomonas 29 viridiflava , Pantoea agglomerans , Erwinia persicina) were associated with detrimental effects 30 on seedling phenotypes either in isolation or in SynComs . These results confirm that the plant 31 core microbiome includes pathogenic and not only commensal or mutualistic taxa. 32 Altogether, these results show that SynCom inoculation can effectively manipulate seed and 33 seedling microbiota diversity and highlight strong fitness differences between native seed- 34 borne taxa in the colonization of these habitats. 35
TRECVID 2012 GENIE: Multimedia Event Detection and Recounting

Our MED 12 system is an extension of our MED 11 system [11], and consists of a collection of lowlevel and high-level features, feature-specific classifiers built upon those features, and a fusion system that combines features both through mid-level kernel fusion and score fusion. We have incorporated large number of audio-visual features in our new system and incorporated diverse types of standard and newly developed event agents which learn the salient audio-visual characteristics of event classes. The combination of additional features and newly developed powerful event agents improve our MED performance substantially beyond our MED 11 results. In addition, our MER 12 submissions reported recounting of specified clips for all five MER events and additionally provided MER results for all the clips detected by MED system. Our MER system generated recounting of detections based on CDR features and synopsis provided as part of the EventKits and DEVT datasets. The MER evaluation results are promising for event-level discrimination, and indicated further improvement to be made for clip-level discrimination.
2-day planetary waves in the stratosphere, mesosphere and lower thermosphere

This thesis presents observations of the 2-day planetary wave in the stratosphere, mesosphere and lower thermosphere. These observations were made using two ground-based meteor radars at polar latitudes and the satellite-borne microwave limb sounder (MLS) on the NASA Aura satellite. There have been relatively few observations of the 2-day wave at polar latitudes made using ground-based radars. This is particularly so in the Antarctic. Measurements of summertime and wintertime polar 2-day waves in the mesosphere and lower thermosphere (MLT) region were made using identical meteor radars at the conjugate geographical latitudes of Rothera (68! S, 68! W) in the Antarctic and Esrange (68! N, 21! E) in Arctic Sweden. This allows accurate quantification of the differences in the nature and seasonal variability of the 2day wave between the two polar regions. A clear seasonal variability is evident with the maximum amplitudes occurring during the summer months in both hemispheres. However, significant differences are found in the behaviour of the summertime wave between the two polar regions. In particular, wave activity is shorter lived but of larger amplitude in the Antarctic. These differences are suggested to be partly due to the different background winds of the two polar regions and possible differences in the component zonal wavenumbers in the northern and southern hemispheres. These radar studies have excellent spatial, height and time resolution but cannot resolve the component zonal wavenumbers of the 2-day wave. Therefore, Earth Observing System (EOS) Aura Microwave Limb Sounder (MLS) measurements of atmospheric temperature were used to investigate the climatology and interhemispheric differences of the different zonal wavenumbers (westward propagating zonal wavenumbers 2, 3 and 4) that compose the 2-day wave “complex”. This study demonstrates that the wave is dominated by different wavenumbers in the northern and southern hemisphere and that some of the interhemispheric differences observed in ground-based studies can be explained by the seasonal variability of these different zonal wavenumbers. These satellite studies led to participation in a multi-technique international collaboration to study the short-term variability of the summertime 2-day wave. Observations made in the northern hemisphere at mid-latitudes revealed that there are typically three peaks of enhanced 2-day wave amplitude during the summer, probably originating from a combination of baroclinic instability and critical wind speed.
Automated Methods for Estimating Baseflow from Streamflow Records in a Semi Arid Watershed

Understanding of the runoff generation processes is important in understanding the magnitude and dynamics ofgroundwater discharge. However, these processes continue to be difficult to quantify and conceptualize. In this study,two digital filter based separation modules, the Recursive filtering method (RDF) and a generalization of therecursive digital filter (GRDF) were1991–2002 in the Hableh Roud River at the stream gauge of Bonkuh, Semnanprovince. A technique for assessing the recession constants of the sub flows based on calibration the average value ofthe inverse of the value of the inverse of the slope of the linear path in the recession periods of an Ln (q)-time graphis presented. The result show that, the GRDF method gave higher (Baseflow index) BFI values than the RDF methodwith less variability and the mean baseflow calculated on an annual basis, ranged from 3.27 m3/s to 4.04 m3/s overperiod of study by RDF and GRDF, respectively. Since the true values of the baseflow index are unknown, it cannotbe said, which one of the methods gives the best estimates.
Unpacking the functional heterogeneity of the Emotional Face Matching Task: 3 a normative modelling approach

: 24 In this study we dissect the heterogeneity that underlies traditional group-level analyses, and determine 25 how individualised patterns of predicted activation relate to age, sex, and variations in acquisition 26 parameters and task design choices. To this end we take advantage of six large open-access/shared 27 datasets and collate a large representative sample of over 7500 participants from which we build a 28 normative of task-evoked activation during a widely used emotional reactivity task, the Emotional Face 29 Matching Task. This enables us to bind heterogeneous datasets to a common reference model and enables 30 meaningful comparisons between them. We then apply this model to the naturalistic and clinically realistic 31 MIND-Set cohort, which is a heterogeneous and highly comorbid sample containing individuals with one or 32 more current diagnosis (affective and anxiety disorders, autism spectrum disorders and/or attention deficit 33 hyperactivity disorder). This enables us to determine whether, and if so how, participants with mental illness 34 and/or neurodivergence differ from the reference cohort, both at the group level and at the level of the 35 individual and in terms of cross-diagnostic symptom domains in addition to diagnosis. We show that patients 36 have, on average, a higher frequency of extreme deviations, and have unique spatial distributions 37 depending on the DSM-IV diagnosis and the number of co-occurring diagnoses when models are 38 constructed using the face>shapes task contrast. Models built using the face>baseline task contrast, have, 39 by comparison, greater predictive value for individuals’ functioning across four transdiagnostic domains. 40 We demonstrate the application of the normative modelling framework to task-based functional 41 neuroimaging data, discuss its potential to further our understanding of individual differences in brain 42 function within reference populations, and further validate the clinical relevance of these models. 43
The Economic Values of Tourism in the Australian Alps

The study's primary objective was to measure the economic impact of tourists' expenditure in the Australian Alps, on the economies of the Australian Capital Territory, New South Wales and Victoria. The project involved measuring tourism expenditure in the Alps region and the associated multiplier effects of that expenditure. The economic impact is shown in terms of Gross State Product (GSP) and employment/jobs that are attributed to tourism to each of the states' Alps national parks.
A robust active contour edge detection algorithm based on local Gaussian statistical model for oil slick remote sensing image

Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea. In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images. In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework. The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm. The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images. Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm. The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms. In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances.
A Business Cycle Indicator for Abu Dhabi

According to the Abu Dhabi Economic Vision 2030, the main objective is to diversify its production base, and widen non-oil production. In addition to this growth strategy cyclical fluctuations are also a concern. Economic measures to increase the periods of economic prosperity and reduce the slowdown periods are implemented. To do so there is also a need for reliable tools to monitor the cyclical economic development. The composite indicator approach for economic change assessment, therefore, is seen as a proper method taken up by the Department of Economic Development (DED) in Abu Dhabi as a tool to assess actual short-term economic changes. However, there are no quarterly national accounts for Abu Dhabi at the moment and only a few short-term statistics. A suitable quarterly reference series for indicator assessment has to be constructed with temporal disaggregation methods. Since Abu Dhabi has a huge oil sector real income is used as a reference, because it reflects the economic situation better than real GDP. For the construction of the composite indicators various data sources are exploited.
eScholarship International

The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of causal learning in associative terms. A review of the development of the use of rational causal models in the psychology of learning is discussed concluding with the presentation of three areas of research related to cause-effect learning. We explain how rational context choices, a selective association effect (i.e
Ninth International Conference on Ecological Vehicles and Renewable Energies ( EVER ) Dynamic MEC Modeling of Claw Pole Alternators

The paper is devoted to the modeling of a claw pole alternator (CPA) following the introduction of a new magnetic equivalent circuit (MEC) which takes into account the variation of the rotor position, leading to the so-called "dynamic MEC". The proposed model considers both linear and saturated behaviors of the magnetic circuit. A special attention is paid to the modeling of the leakage fluxes. A dedicated numerical procedure based on the Newton-Raphson algorithm has been developed for the resolution of the proposed dynamic MEC. A comparison between the results predicted by the dynamic MEC, under no-load operation, and those of experimental tests carried out on CPA test bench, has led to a good agreement. Keywords-Claw pole alternator, dynamic magnetic equivalent circuit, leakage flux, saturation, Newton-Raphson algorithm, back-EMF, no-load characteristic.
The Capital Asset Pricing Model

The Capital Asset Pricing Model (CAPM) revolutionized modern finance. Developed in the early 1960s by William Sharpe, Jack Treynor, John Lintner and Jan Mossin, the model provided the first coherent framework for relating the required return on an investment to the risk of that investment. This paper lays out the key ideas of the model, places its development in a historical context, and discusses its applications and enduring importance to the field of finance.
Sistemi di percezione frontale per veicoli intelligenti mediante visione artificiale

The performance of many computer vision applications, especially in the automotive field, can be greatly increased if camera oscillations induced by movements of the acquisition devices are corrected by a stabilization system. An effective stabilizer should cope with different oscillation frequencies and amplitude intervals, and work in a wide range of environments (such as urban, extra-urban or even unstructured ones). In this paper we will analyze three different approaches, based on signature, feature, and correlation tracking respectively, that have been devised to face these problems.
Identification and validation of key genes associated with smoking-induced lung adenocarcinoma development through bioinformatics analysis and predictions of small-molecule drugs

Although smoking is known to be the leading risk factor for lung cancer, it is still unclear how normal cells turn cancerous in cigarette smokers. This study aimed to identify key molecular drivers that contributed to the progression and prognosis of lung adenocarcinoma (LUAD) in cigarette smokers, as well as screen, correlated small molecule therapeutic drugs by bioinformatics analysis. Gene expression profile was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between current smokers without cancer and never smokers were identified and were analyzed to identify gene ontologies, pathways, protein‑protein interaction (PPI) networks, hub genes, and prognostic potentials. Finally, effective small-molecule compounds were screened by the Connectivity Map (CMap) database. A total of nine genes were screened out as the critical among the DEGs from the PPI network. Overall survival analysis revealed that high mRNA expression of ACTR2 and ANAPC10 were significantly associated with the LUAD. Furthermore, three candidate small-molecule drugs for manipulating LUAD progression were predicted. Identification of critical genes involved in disease development and candidate drugs to combat it can lead us to better diagnosis and targeted therapy strategies. The results of the present study may provide insight into the mechanisms underlying LUAD pathogenesis development risk in cigarette smokers and may provide potential targets for prevention.