Miracle-dz/UniFAR-FTUs huggingface dataset:
TURNING MANEUVERS IN STELLER SEA LIONS (EUMATOPIAS JUBATUS)

Steller sea lions are highly maneuverable marine mammals (expressed as minimum turning radius). Video recordings of turns (n = 195) are analyzed from kinematic measurements for three captive animals. Speed-time plots of 180 ◦ turns have a typical “V-shape. ” The sea lions decelerated during the first half of the turn, reached a minimum speed in the middle of the curved trajectory and reaccelerated by adduction of the pectoral flippers. The initial deceleration was greater than that for passive gliding due to pectoral flipper braking and/or change in body contour from a stiff, straight streamlined form. Centripetal force and thrust were determined from the body acceleration. Most thrust was produced during the power phase of the pectoral flipper stroke cycle. Contrary to previous findings on otariids, little or no thrust was generated during initial abduction of the pectoral flippers and during the final drag-based paddling phase of the stroke cycle. Peak thrust force at the center of gravity occurs halfway through the power phase and the centripetal force is maximal at the beginning of the power stroke. Performance is modulated by changes in the duration and intensity of movements without changing their sequence. Turning radius, maximum velocity, maximum acceleration and turning duration were 0.3 body lengths, 3.5 m/s, 5 m/s 2 , and 1.6 s, respectively. The relative maneuverability based on velocity and length specific minimum turning radius is comparable to other otariids, superior to cetaceans but inferior to many fish.
A Forecasting Methodology for Workload Forecasting in Cloud Systems

Cloud Computing is an essential paradigm of computing services based on the “elasticity” property, where available resources are adapted efficiently to different workloads over time. In elastic platforms, the forecasting component can be considered by far the most important element and the differentiating factor when comparing such systems, with workload forecasting one of the problems to solve if we want to achieve a truly elastic system. When properly addressed the cloud workload forecasting problem becomes a really interesting case study. As there is no general methodology in the literature that addresses this problem analytically and from a time series forecasting perspective (even less so in the cloud field), we propose a combination of these tools based on a state-of-the-art forecasting methodology which we have enhanced with some elements, such as: a specific cost function, statistical tests, visual analysis, etc. The insights obtained from this analysis are used to detect the asymmetrical nature of the forecasting problem and to find the best forecasting model from the viewpoint of the current state of the art in time series forecasting. From an operational point of view the most interesting forecast is a short-time horizon, so we focus on this. To show the feasibility of this methodology, we apply it to several realistic workload datasets from different datacenters. The results indicate that the analyzed series are non-linear in nature and that no seasonal patterns can be found. Moreover, on the analyzed datasets, the penalty cost as usually included in the SLA can be reduced to a 30 percent on average.
Detection of Capsicum chlorosis virus (CaCV), An Emerging Virus Infecting Chilli in Tamil Nadu, India

Detection of Capsicum chlorosis virus (CaCV), An Emerging Virus Infecting Chilli in Tamil Nadu, India Chilli plants in the farmers’ fields in Tamil Nadu showing symptoms of concentric chlorotic and necrotic rings on the leaves were found infected with a tospo virus through RT- PCR by using tospo virus degenerate primer (gL3637 /F and gL4435C). From the BLAST result the virus associated with the disease is identified as Capsicum chlorosis virus (CaCV). For further confirmation, infected samples collected from Coimbatore were amplified using newly designed primer pair (GKCaCVCPF1/R1) corresponding to the nucleocapsid region of the CaCV for further confirmation. In the sequence analysis, complete nucleocapsid protein region shared a nucleotide and amino acid identity of 99.1% and 100% with the PB4 and PB1 isolates of CaCV from India respectively. In the phylogenetic analysis, this isolate grouped with the CaCV isolates from Aurangabad and Tamil Nadu, India than with isolates from other countries. About fifteen hosts from different families were inoculated through sap, of which nine hosts produce local lesion and four showed systemic symptoms. The positive reaction of extract from these plants in DAS- ELISA (double antibody sandwich ELISA) against the Nucleocapsid protein of CaCV (DSMZ, Germany), confirmed the presence of CaCV in infected chilli plants in Tamil Nadu.
Content Based Image Retrieval Using Combined Color & Texture Features

In this paper, combined color and texture features for content based image retrieval is presented. Humans tend to differentiate images based on color and texture features such as regularity, directionality and smoothness. Color histogram is mostly used to represent color features but it cannot entirely characterize the image. Gabor filter, a tool for texture feature extraction has proved to be very effective in describing visual content via multi-resolution analysis. We present the use color histogram for color feature extraction, Gabor Wavelet for texture feature extraction and provide comprehensive experimental evaluation. The results clearly indicate that combined Color and Gabor texture features provide best retrieval precision.
New trends in potential migration from Albania: the migration transition postponed?

ABSTRACT Since the end of communist rule in 1991, Albania has seen, proportionately, one of the largest outflows of its people of any country in the world – an emigrant stock in 2013 equivalent to 44% of its resident population. This article addresses two issues related to the future trend of Albanian migration. First, a migration module of 23 questions designed by the authors and attached to the 2018 round of the European Values Survey asked about future migration intentions. These were found to be high and increasing: 52% of 18 to 40-year-olds expressed their intention to migrate compared to 44% in a comparable survey in 2006/7. The top two desired destination countries have switched from Italy and Greece in the past to Germany and the USA in the latest survey, which also shows potential migration higher amongst the more-skilled and educated population compared to past trends. A subsample of returned migrants captured by the survey shows even higher intentions to migrate – 71%. Second, Albania’s anticipated migration transition, from a country of emigration to one of net immigration, is deferred if migration intentions correlate with migration outcomes, with obvious reflections on the country’s overall development trajectory.
Laser–visual–inertial odometry and mapping with high robustness and low drift

We present a data processing pipeline to online estimate ego‐motion and build a map of the traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). Different from traditional methods that use a Kalman filter or factor‐graph optimization, the proposed method employs a sequential, multilayer processing pipeline, solving for motion from coarse to fine. Starting with IMU mechanization for motion prediction, a visual–inertial coupled method estimates motion; then, a scan matching method further refines the motion estimates and registers maps. The resulting system enables high‐frequency, low‐latency ego‐motion estimation, along with dense, accurate 3D map registration. Further, the method is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in the dark, texture‐less, and structure‐less environments. During experiments, the method demonstrates 0.22% of relative position drift over 9.3 km of navigation and robustness w.r. t. running, jumping, and even highway speed driving (up to 33 m/s).
Adaptive Robust Methodology for Parameter Estimation and Variable Selection

The dissertation consists of three distinct but related projects. We consider regression model fitting, variable selection in regression, and autocorrelation estimation in time series. In each procedure we formulate the problem in terms of minimizing an objective function which adapts to the given data. First we propose a robust M-estimation procedure for regression. The main purpose of the proposed methodology is to develop a procedure that adapts to light/heavy tailed, symmetric/asymmetric distributions with/without outliers. We focus on studying the properties of the maximum likelihood estimator of the asymmetric exponential power distribution, a broad distribution class that holds both Normal and asymmetric Laplace distributions as special cases. The proposed methodology unifies least squares and quantile regression in a data driven manner to capture both tail decay and asymmetry of the underlying distributions. Finite sample performance of the method is exhibited via extensive Monte Carlo simulation and real data applications. Second, we capitalize on the success of the proposed method and extend it to a variable selection procedure that selects the important predictors under a sparse setting. Quantile regression Lasso, i.e., quantile regession with L1 norm on the regression coefficients for regularization is a robust technique to perform variable selection. However which quantile should be adopted is unclear. The proposed methodology introduces a way to choose the most “informative” quantile of interest that is used in the adaptive quantile regression Lasso. A modified BIC criterion is used to select the optimal tuning parameter. The proposed procedure selects the quantile based on the log-likelihood of the asymmetric Laplace distribution, and aims to perform the best quantile regression Lasso which is confirmed in both simulation study and a real data analysis. Third, we focus on alleviating the underestimation issue of the sample autocorrelation in linear stationary time series. We first formulate autocorrelation estimation into a least squares prob-
Molecular identification of shrimp species, Penaeus semisulcatus, Metapenaeus dobsoni, Metapenaeus brevicornis, Fenneropenaeus indicus, Parapenaeopsis stylifera and Solenocera crassicornis inhabiting in the coromandel coast (Tamil Nadu, India) using MT-COI gene

DNA barcoding is a vital tool for assessing non described and cryptic morphological species. Literature revealed that a number of marine shrimp species have morphologically been described. However, it has lots of misleading due to cryptic characters, such as dimorphism, larval and adult variation etc. In the present study, fourteen marine shrimp species ( Penaeus semisulcatus , Penaeus monodon , Metapenaeus affinis, Metapenaeus lysianasa, Metapenaeus dobsoni, Metapenaeus brevicornis, Metapenaeopsis stridulans, Fenneropenaeus indicus , Fenneropenaeus merguiensis , Parapenaeopsis stylifera , Parapenaeopsis maxillipedo , Marsupenaeus japonicas, Solenocera crassicornis and Alpheus paludicola ) were collected from Coromandel Coast of Tamil Nadu, India, and they were identified morphologically. Among these, six species ( P. semisulcatus, M. dobsoni, M. brevicornis, F. indicus, P. stylifera and S. crassicornis ) were taken for molecular identification by adopting DNA barcoding of mitochondrial cytochrome c oxidase subunit I (COI) gene. 650 bp sequences were obtained when universal primers (LCO1490 and HCO2198) were used. Their similarity was checked with BLAST-NCBI, and each and every species showed > 80% identity. Maximum intra specific divergence was calculated as 0.8% in F. indicus of India, Thailand and China. Inter specific divergence was found to be maximum, 3.95% between S. crassicornis and S. koelbeli of India and China. Minimum divergence (0.00%) was observed between two Indian haplotypes, M. dobsoni and M. brevicornis , which overlaps each other. Further studies at population levels of these two species are required to rule out this ambiguity. On the whole, the phylogenetic tree revealed the polyphyletic clade.
Metagenomic analysis of soil microbial communities

Ramonda serbica and Ramonda nathaliae, rare resurrection plants growing in the Balkan Peninsula, produce a high amount of phenolic compounds as a response to stress. The composition and size of bacterial communities in two rhizosphere soil samples of these plants were analyzed using a metagenomic approach. Fluorescent in situ hybridization (FISH) experiments together with DAPI staining showed that the metabolically active bacteria represent only a small fraction, approximately 5%, of total soil bacteria. Using universal bacteria - specific primers 16S rDNA genes were amplified directly from metagenomic DNAs and two libraries were constructed. The Restriction Fragment Length Polymorphism (RLFP) method was used in library screening. Amongst 192 clones, 35 unique operational taxonomic units (OTUs) were determined from the rhizosphere of R. nathaliae, and 13 OTUs out of 80 clones in total from the library of R. serbica. Representative clones from each OTU were sequenced. The majority of sequences from metagenomes showed very little similarity to any cultured bacteria. In conclusion, the bacterial communities in the studied soil samples showed quite poor diversity.
Distinct Subgroups of Former Foster Youth during Young Adulthood: Implications for Policy and Practice

Social policy concerning foster youth making the transition to adulthood has evolved significantly since the late 1980s (Courtney, 2009). The Independent Living Initiative of 1986 provided states funds for soft services intended to help prepare older adolescents in foster care to live independently by age 18. The Foster Care Independence Act of 1999 increased funding for such services, but recognized the need for continuing support past age 18 by encouraging states to provide the services up to age 21. That law, and its later expansion through the Education and Training Voucher (ETV) program, also expanded the kinds of support states could provide beyond soft services to include housing, health insurance, and direct support for postsecondary education. The Fostering Connections to Success and Increasing Adoptions Act of 2008 (Fostering Connections Act) fundamentally changed the nature of federal support for young people in state care by extending entitlement funding March 2010
Seasonal variation in the body fat of Japanese macaques Macaca fuscata

Japanese macaques Macaca fuscata inhabit the Japanese archipelago where they experience markedly seasonal warm and cool temperate climates. Their reproduction and their nutritional status follow clearly seasonal patterns. Their status can be effectively evaluated based on their total body fat, but making such measurements has been difficult. In this study, the body fat of Japanese macaques was studied using Double Energy X-ray Absorptiometry (DXA). Adult males were found to have a median fat mass of 7% and adult females 9%. Using the 90th percentile as the criterion, we determined the levels of obesity in Japanese macaques as 17% in males and 27% in females. Fatness indicators (physique index and skinfold thickness) were found to correlate well with total body fat and fat mass percentage. Fat mass is estimated fairly well from these indicators. Fatness indicators change seasonally in artificially reared Japanese macaques.
FATTY ACID COMPOSITION AND OXIDATIVE STABILITY OF MUSCLES IN LAMBS FED COCONUT OIL SUPPLEMENTED DIET

POPOVA, T. , 2011. Fatty acid composition and oxidative stability of muscles in lambs fed coconut oil supplemented diet. Bulg. J. Agric. Sci., 17: 402-409 A study with 10 male lambs of Bulgarian dairy synthetic population was carried out to investigate the effect of dietary coconut oil supplementation on the lipid components, fatty acid composition and oxidative stability during storage in m. Longissimus dorsi and m. Semimembranosus. Dietary coconut oil supplementation did not induce significant changes in the amount of lipid components in both muscles. Triacylglycerols of m. Longissimus dorsi and m. Semimembranosus in the lambs from the group fed coconut oil supplemented diet had significantly higher content of C14:0 and C16:0 (P<0.01; P<0.05) and lower amount of C17:0 (P<0.01). The differences in the phospholipid fatty acid composition of the studied muscles were due to both diet and muscle type. Coconut oil supplementation increased significantly the amount of C16:1 (P<0.05) and lowered that of C17:0 and C22:5 (P<0.05; P<0.01). The content of C20:4 (P<0.05) and the total amount of PUFA was higher (P<0.05) in m. Semimebranosus in the control and coconut oil supplemented group of lambs, compared to m. Longissimus dorsi. The oil oxidative stability of both m. Longissimus dorsi and m. Semimebranosus was influenced by the time of storage whereas no significant effect of the dietary coconut oil supplementation was observed.
ModelE2-TOMAS development and evaluation using aerosol optical depths, mass and number concentrations

ModelE2-TOMAS development and evaluation using aerosol optical depths, mass and number concentrations Y. H. Lee, P. J. Adams, and D. T. Shindell NASA Goddard Institute for Space Studies and Columbia Earth Institute, New York, NY, USA Department of Civil and Environmental Engineering and Department of Engineering Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA Earth and Ocean Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
Understanding Curbs and Intersections for Effective On-Road Driving

Last few years have seen a tremendous increase in interest for autonomous driving. As advances have been made in this field, autonomous driving has faced various challenges. In this thesis, we present vision based solutions to curb and intersection detection problems faced by self-driving cars. In the first part of the thesis, we present a discriminative approach to the problem of curb detection under diverse road conditions. Curb detection is a critical component of driver assistance and autonomous driving systems. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixel-wise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further, we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over a large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset. While it is important to detect road boundaries, another interesting problem is to know how the boundaries fork and lead to an intersection. In the second part of the thesis, we focus on the problem of road intersection detection. Long-short term memory networks(LSTM) models have shown considerable performance on a variety of problems dealing with sequential data. In this thesis, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road intersection detection as binary classification task over a sequence of frames. The model combines deep hierarchical visual feature extractor with recurrent sequence model. The model is end to end trainable with a capability of capturing the temporal dynamics of the system. We exploit this capability to identify road intersection in a sequence of temporally consistent images. The model has been rigorously trained and tested on various different datasets. We think that our findings could be useful to model behavior of autonomous agent in the real world.
Vision- and Manipulation-based Signs in Taiwan Sign Language *

An fMRI experiment was conducted to investigate how iconic elements are represented in sign languages and how such representation affects the linkage between language and brain. Iconic signs are those that clearly resemble the actions, objects, or characteristics they represent. In Taiwan Sign Language (TSL), expressions involve two kinds of signs: vision-based signs and manipulation- based signs. It is proposed that these two kinds of signs impose different processing demands on the signers. In our experiment, deaf signers viewed line drawings and were asked to covertly produce the names of the pictures either in TSL or in Chinese. Brain activities were measured with an fMRI technique. Analyses of the neural images indicated that the neural systems involved in the production of TSL and Chinese words were remarkably similar, in that the dorsal visual route and perisylvian areas were important in processing both languages for our deaf participants. On the other hand, the neural substrates mediating the representation of objects and/or action took different dynamically distributed forms, depending on which sensory/perceptual channels were required to weight the input information for recognition. In addition, results also suggested that the characteristics of signs (i.e., vision-based vs. manipulation-based) correlated with functional arrangements (i.e., visual vs. motor pathway) of the cerebral cortex, indicating that the brain not only represents the structural knowledge of the linguistic categories (syntactic and/or the semantic categories), but also involves the dynamic processes which characterize the interaction between modality and conceptual representation.
In Situ and Context-Aware Target Apps Selection for Unified Mobile Search

With the recent growth in the use of conversational systems and intelligent assistants such as Google Assistant and Microsoft Cortana, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with mobile apps and frequently search for an information need using different apps. Recent work has stated the need for a unified mobile search system that would act as meta search on users' mobile devices: it would identify the target apps for the user's query, submit the query to the apps, and present the results to the user. Moreover, mobile devices provide rich contextual information about users and their whereabouts. In this paper, we introduce the task of context-aware target apps selection as part of a unified mobile search framework. To this aim, we designed an in situ study to collect thousands of mobile queries enriched with mobile sensor data from 255 users during a three month period. With the aid of this dataset, we were able to study user behavior as they performed cross-app search. We finally study the performance of state-of-the-art retrieval models for this task and propose a simple yet effective neural model that significantly outperforms the baselines. Our neural approach is based on learning high-dimensional representations for mobile apps and contextual information. Furthermore, we show that incorporating context improves the performance by 20% in terms of nDCG@5, enabling the model to perform better for 57% of users. Our data is publicly available for research purposes.