Miracle-dz/UniFAR-FTUs huggingface dataset:
Capital Flows and Asset Values: A Review of the Literature and Exploratory Investigation in a Real Estate Context

This paper reviews the existing general finance literature on capital/investor flows and asset pricing with specific emphasis on the implications of the findings for real estate. The paper reviews the key elements of important papers within a framework that carefully synthesizes and ties together the main findings and real estate implications for both private and public market valuations. The papers surveyed here examine the linkages between flows & returns as well as measures of sentiment and deviations of price from fundamental value and differ in their asset market focus (domestic equities, international equities, closed-end fund, foreign exchange), frequency of data (monthly and daily) and time periods covered. Based on the review and synthesis of results the conclusion reached here is that at most times there is not a direct causal link between flows and returns (or asset values); they simply respond to the same fundamental economic news and provide a barometer of market liquidity. However, in certain “extreme” environments capital flows are related to mispricing of assets that is related to the interaction of uninformed traders and limited arbitrage. This is quite consistent across different asset markets.
Effect of ECRH on toroidal rotation in ASDEX Upgrade H-mode discharges

Core toroidal rotation behavior and momentum transport have been examined in neutral beam injection (NBI) heated plasmas with and without electron cyclotron resonance heating (ECRH) in ASDEX Upgrade (AUG). The impurity ion temperature and rotation are measured by means of charge exchange recombination spectroscopy (CXRS) and the main ion rotation is calculated neo-classically based on the measured impurity ion temperature and rotation profiles. In purely NBI heated discharges the plasma spins up in the co-current direction and forms peaked rotation profiles. However, when ECRH power is added to these discharges the rotation decreases significantly leading to flat and occasionally slightly hollow profiles. The rotation at the edge of the plasma (ρ > 0.65) is largely unaffected by the application of the ECRH. During ECRH phases the electron temperature in the core increases dramatically concomitant with a flattening of the ion temperature profile. In these phases peaking of the impurity ion and electron density profiles is also observed. The change in toroidal rotation is sensitive to both the amount of ECRH power deposited as well as to the deposition location. The measured rotation changes can not be explained by a modification to the NBI torque deposition profile, a preferential loss of fast ions from the plasma core, or by a simple increase in momentum diffusivity. Additionally, the inward directed Coriolis momentum pinch is predicted to increase, not decrease, during the ECRH phases. Altogether, the data suggest the presence of either an outward convection of momentum or an intrinsic, counter-current directed torque. Although the physics behind these possibilities remains unclear, they are presumably related to either a convective or residual stress driven momentum flux, which responds to the ECRH-induced changes in the plasma profiles and turbulence.
Space–time variability in soil moisture droughts in the Himalayan region

Abstract. Soil water is a major requirement for biomass production and, therefore, one of the most important factors for agriculture productivity. As agricultural droughts are related to declining soil moisture, this paper examines soil moisture drought in the transboundary Koshi River basin (KRB) in the central Himalayan region. By applying the J2000 hydrological model, daily spatially distributed soil moisture is derived for the entire basin over a 28-year period (1980–2007). A multi-site and multi-variable approach – streamflow data at one station and evapotranspiration data at three stations – was used for the calibration and validation of the J2000 model. In order to identify drought conditions based on the simulated soil moisture, the soil moisture deficit index (SMDI) was then calculated, considering the derivation of actual soil moisture from long-term soil moisture on a weekly timescale. To spatially subdivide the variations in soil moisture, the river basin is partitioned into three distinct geographical regions, namely trans-Himalaya, the mountains, and the plains. Further, the SMDI is aggregated temporally to four seasons – winter, pre-monsoon, monsoon, and post-monsoon – based on wetness and dryness patterns observed in the study area. This has enabled us to look at the magnitude, extent, and duration of soil moisture drought. The results indicated that the J2000 model can simulate the hydrological processes of the basin with good accuracy. Considerable variation in soil moisture was observed in the three physiographic regions and across the four seasons due to high variation in precipitation and temperature conditions. The year 1992 was the driest year and 1998 was the wettest at the basin scale in both magnitude and duration. Similarly, the year 1992 also has the highest number of weeks under drought. Comparing the SMDI with the standardised precipitation index (SPI) suggested that SMDI can reflect a higher variation in drought conditions than SPI. Our results suggested that both the occurrence and severity of droughts have increased in the Koshi River basin over the last 3 decades, especially in the winter and pre-monsoon seasons. The insights provided into the frequency, spatial coverage, and severity of drought conditions can provide valuable contributions towards an improved management of water resources and greater agricultural productivity in the region.
Stochastic Gradient Ascent in maxLik

maxLik is a package, primarily intended for Maximum Likelihood and related estimations. It includes several optimizers and associated tools for a typical Maximum Likelihood workflow. However, as predictive modeling and complex (deep) models have gained popularity in the recend decade, maxLik also includes a few popular algorithms for stochastic gradient ascent, the mirror image for the more widely known stochastic gradient descent. This vignette gives a brief overview of these methods, and their usage in maxLik.
The Social Dynamics of People Cooperation: A Study on On-line Knowledge Construction Networks

A classic question common to social sciences revolves around how people cooperate. Indeed,under what conditions will cooperation emerge without central authority? However, as the complication ofcommunity is growing, the importance of answering this question is increasing. That’s because the spread ofmass collaboration has changed everything, such as Wikinomics. Questions posted about how networks connectindividuals through structure and process, what rules and resources situate them in larger social systems, andaffect the interaction to and from them. This paper concerns that why people are willing to cooperate withoutthe aid of a central authority and how behavior and norms are affected by social relations. But the answer forpeople developing cooperation has a fundamental effect on social, political, and economic relations with others.Because of the social phenomena is composed of action, interaction, and relationship through network structure,analysts should not discrete individual behavior and ongoing network structure. As central objectives ofnetwork analysis are to measure and represent structural and to explain both why they occur and what are theirconsequences. The approach of this study is to propose network analysis to explain how participator actions forthe cloud aid education in China. The aim of this study is to utilize network diagram to analyze networkstructure of the cloud aid education. Based on longitudinal study, we offer a structural means to understand howknowledge creation and sharing occurs within networks, and then find the key determinants of efficientknowledge management. Mapping these dimensions in social networks enable social and technicalinterventions managers can employ to improve a network’s ability to create and share knowledge. The result isillustrated by investigating conditions of cooperation and identifying social
South Kalimantan Local Wisdom-Based Biology Learning Model

The objectives of this study were to analyze the validity, practicality, and effectiveness of South Kalimantan local wisdombased biology learning and its effect on student learning outcomes. The research method used is research and development. This research was in a Develop stage of Thiagarajan’s Model. This development has produced learning models (lesson plans, student’s worksheet, learning achievement test questions, teacher activity sheets, student activity sheets, and student response sheets). The local wisdom-based learning model were designed with seven stages using Banjar language (regional language of South Kalimantan). Model that have been developed were tested for the level of validity, practicality, effectiveness, and its effect in learning. The level of validity is determined based on the assessment and review of the four validators. To find out the effectiveness and the effect of the learning model, quasi-experimental design was applied by involving two classes at SMAN 7 Banjarmasin-Indonesia. Data were collected using a variety of instruments, namely the validity assessment sheet, the student’s worksheet and lesson plan sheets, student achievement test questions, and student response sheets. Data analysis was implemented to measure the effectiveness and the effect of learning by calculating n-Gain and ANCOVA, respectively. The results, the learning tools met the "valid" criteria so that it can be implemented. Learning also concluded having good practicality criteria. Moreover, it can be seen that the application of local wisdom-based learning model and tools was quite effective in improving student learning outcomes, in contrast to learning in the control class. Furthermore, ANCOVA test concluded that there were significant differences in learning outcomes between students in the experimental and the control class.
Statistical and Geometrical Alignment using Metric Learning in Domain Adaptation

Domain adapted machine learning is driven by the possibilities of learning from source data distribution to understand different target data distributions. An assumption is made that one application (source) domain always has enough labeled information, but the other related application (target) may contain information that is partially labeled or completely unlabeled. Therefore, it is necessary to train the target domain classifier using enough labeled information of the source domain. However, contrary to primitive assumptions, the source domain and target domain data need not have the same distribution. Therefore, we can’t directly use data of source domain to train classifier for data of target domain. Existing approaches can be deprived of one or more objectives: perform geometric diffusion on the manifold, align the cross-domain distributions, preserve the discriminative information using metric learning. Here, we have proposed a novel framework that aims to meet all such objectives. In this framework, we proposed two methods, statistical and geometrical alignment using metric learning with pseudo labels (SGA-MDAP) and without pseudo labels (SGA-MDA) in visual domain adaptation. It has been demonstrated through various experiments that our framework outperforms various state-of-the-art methods over four different real-world cross-domain visual identification datasets such as PIE face, ORL face, Yale face, and Office Caltech.
Identification of IP addresses using fraudulent geolocation data

IP geolocation information is used all over the internet, but is easily faked. A number of different internet organisations do this – from bulletproof hosting providers attempting to conceal the location of their servers, to VPN providers looking to sell services in countries they don’t have a presence in. Servers using fraudulent IP geolocation in this way may also be more likely to be hosting fraudulent content, making IP geolocation fraud important to detect in the context of internet fraud prevention. In this project, a system has been developed for detecting this kind of IP geolocation fraud. The system developed in this report uses measurements from a global network of measurement servers – an array of 8 servers in 7 different countries managed by Netcraft, and over 10,000 servers in 176 countries through the RIPE Atlas API. Using this system we have analysed the prevalence of geolocation fraud in address space spanning over 4 million IPs, which is, to the best of our knowledge, the largest study of its kind conducted. Despite focusing on only a small part of the IPv4 address space, our analysis has revealed incorrect geolocation being used by over 62,000 internet hosts, targeting 225 out of the 249 possible country codes. In terms of address space, we have discovered incorrect geolocation being used by IP address blocks cumulatively spanning over 2.1 million IPs. This project has been conducted as an industrial project with Netcraft. The system described in this report has been deployed internally at Netcraft, and will continue to be used after this project’s conclusion.
The p-version of the boundary element method for mixed boundary value problems on polyhedra

We study the p-version of the boundary element method for a mixed boundary value problem. The discretising scheme is based upon a system of boundary integral operators that uniquely defines the Cauchy data of the problem, the trace on one part of the boundary in H̃ and the normal derivative on the other part in H̃−1/2. We consider polyhedral domains where singularities at vertices and edges appear. These singularities are very strong at edges where different boundary conditions meet, i.e., the trace is not necessarily in H and the normal derivative not in L2. For this situation we prove an optimal a priori error estimate.
Role of molecular chaperones in cold adaptation of bacteria

The role of HSPs in cold adaptation of bacteria is beginning to be understood. It was shown a couple of years ago that rate of survival of a strain of Escherichia coli after incubation at -80oC for 24 hours could be significantly improved by warming the solution at 42 o C for 30 minutes before cold storage. The induction of the heat shock proteins (Dna K/Dna J) in the heat treated sample was also demonstrated (Chow and Tung 1998). The Clp B protein, which is essential for thermotolerance in Cyanobacteria and Eukaryotes, was found to be induced by moderate chilling in the cyanobacterial strain Synechococcus PCC 7942. Following a shift from 37oC to 25oC, growth and photosynthesis were more adversely affected in a mutant having deletion in the clp B gene, compared to the wild type Synechococcus PCC 7942 (Porankiewicz and Clarke 1997). The role of the Htp G protein (a prokaryotic homolog of the heat shock protein Hsp 90) in both thermal adaptation and cold acclimation of the same organism was also reported some time back (Hossain and Nakamoto 2002).
A Pilot Study on Structural Responses of Normal Concrete-UHPC Composite Bridge Decks w/wo Rebars through an Experimental-Numerical Approach

Ultra-high-performance concrete (UHPC) is already gaining in-field applications in bridge deck rehabilitation. On the other aspect, an innovative NC-UHPC composite deck system was motivated to promote accelerated bridge construction (ABC) implementation of a newly built bridge, of which the precast UHPC substrate serves as the formwork for the cast-in-place NC overlay. Performance characterizations were investigated by loading five decks, of various UHPC thickness and longitudinal or transverse rebars. Their effects on the composite decks’ failure patterns and load-deflection curves were quantitatively analyzed. Besides, digital image correlation (DIC) technique was adopted to adequately capture their damage evolutions. Moreover, analytical solutions for predicting a composite deck’s flexural and shear strengths are tentatively derived. Finally, 3D finite element (FE) models are developed within ATENA framework, capable of embracing the NC-UHPC interface’s behaviors. Numerical predictions agree well with the experimental results and can benefit further researches evolving a NC-UHPC composite deck system.
Toward Designing User-centered Idle Behaviors for Social Robots in the Home

— ​ Idle behaviors give interactive agents a sense of ​ aliveness both in and outside of interactions. However, in our recent long-term study with Jibo, participants reported the robot's proactive behavior and constant motion as being disruptive and invasive to their living spaces. These findings are consistent with concerns expressed by participants in other studies conducted with a variety of agents. We propose design guidelines for social robots' idle behavior in order to promote a sense of control, privacy, and comfort for their owners. We encourage HRI researchers to include stakeholders in the design process not only for a robot's main functionalities but also for its idle behavior in order to ensure that robots successfully co-living with humans. We plan to further investigate the effects of different robot idle behaviors on long-term usability and engagement in home settings for our future work.
The Wildlife Trusts

The Wildlife Trusts (TWT) welcome this opportunity to comment on the review of consents process. TWT, with more than 800,000 members are the largest UK voluntary organisation dedicated to conserving the full range of the UK’s habitats and species, whether they be in the countryside, in cities or at sea. TWT manages 2,300 reserves covering more than 90,000 hectares of land including coastal reserves; TWT stand up for wildlife, inspire people about the natural world and foster sustainable living.
Cost Analysis of Wildfire Suppression Activities in the Black Hills of South Dakota

The research purpose of this correlational study was to identify and analyze the cost drivers of wildfire suppression activity in the Black Hills of South Dakota. Both correlational analysis and regression modeling were used as procedures to answer the research questions of the identification of the cost factors and their interrelationships to total wildfire suppression costs. Results showed that the factors of size and the cost of aviation resources were the only significant variables showing any correlation or predictive power for the total cost of a wildfire, (r > .8, p < .0001). Recommendations are to increase oversight for aviation operations and to maintain aggressive initial attack to keep wildfires starts small in size in the fire department’s service area.
EXPLORING THE PARAMETER SPACE IN STATISTICAL MACHINE TRANSLATION VIA F-STRUCTURE TRANSFER

Machine translation can be carried out via transfer between source and target language deep syntactic structures. In this paper, we examine core parameters of such a system in the context of a statistical approach where the translation model, based on deep syntax, is automatically learned from parsed bilingual corpora. We provide a detailed empirical investigation into the effects of core parameters on translation quality for the German-English translation pair, such as methods of word alignment, limits on the size of transfer rules, transfer decoder beam size, n-best target input representations for generation, as well as deterministic versus non-deterministic generation. Results highlight just how vital employing a suitable method of word alignment is for this approach as well as the signiﬁcant trade-off between gains in Bleu score and increase in overall translation time that exists when n-best structures are generated.
Unseen Target Stance Detection with Adversarial Domain Generalization

Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show that our approach achieves new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the importance of domain difference between targets in unseen target stance detection.
Le concept de niche chez les Drosophiles

INTRODUCTION 425 1. Les Drosophiles des Ficus d'Afrique tropicale . 430 2. Les Drosophiles des cactus du désert de Sonora . 435 2.1. Besoins de croissance et de reproduction . 437 ,. 2.2. Tolérance aux alcaloïdes '. "'. 440 J. Mécanismes de la différenciation des niches . 442 3.1. Séparation des niches par les spectres de levures consommées . 442 3.2. Chevauchement des niches et compétition . 444 3.3. Relâchement de la niche et transfert de gîte larvaire . 446 4. Variation de la niche et variabilité génétique . 446 CONCLUSION . 449 RESUME . 452 SUMMARY . 452 BIBLIOGRAPHIE . 453
Integration of a Miniature Synthetic Aperture Radar ( MicroSAR ) on the Aerosonde UAV

Project RAVEN is a research project at Memorial University of Newfoundland, whose aim is to establish an Uninhabited Aerial Vehicle (UAV) presence off the Eastern coast of Canada in support of maritime surveillance operations conducted by manned aircraft. Until recently, the use of radar on the small low altitude long endurance (LALE) class of UAV being considered for this role was thought impossible due to restrictive payload size and weight limits. However, RAVEN has acquired the use of a small Synthetic Aperture Radar (MicroSAR) that will be integrated onto the Aerosonde UAV, through a cooperative research project with Brigham Young University (BYU) in Utah, and the University of Colorado at Boulder. The intent is to use the MicroSAR system on an Aerosonde to take high-resolution imagery of sea ice off the Newfoundland Coast in 2007. The end application for such technology includes monitoring of sea ice changes in the Arctic, support for the Iceberg Patrol in the North Atlantic, and target detection and characterization. This paper presents the work done to date to integrate the MicroSAR onto the Aerosonde UAV in preparation for this mission and initial test results. Index Terms — Aerosonde, Ice Patrol, MicroSAR, Maritime Surveillance, Project RAVEN, Synthetic Aperture Radar.
Automatic Image Labeling with Click Supervision on Aerial Images

Manually generating annotated bounding boxes for object detection is time consuming. Although human-annotation is the most accurate approach, machine learning models can provide additional assistance. In this paper, we propose a human in a loop automatic image labeling framework focusing on aerial images with less features for detection. The proposed model consists of two main parts, prediction model and adjustment model. The user first provides click location to prediction model to generate a bounding box of a specific object. The bounding box is then fine-tuned by the adjustment model for more accurate size and location. A feedback and retrain mechanism is implemented that allows the users to manually adjust the generated bounding box and provide feedback to incrementally train the adjustment network during runtime. This unique online learning feature enables user to generalize existing model to target classes not initially presented in the training set, and gradually improves the specificity of the model to those new targets online. We demonstrate promising results on Neovision 2 Heli dataset. Compared to the state-of-the-art method, our prediction model achieves a higher detection rate, and our adjustment model improves the IOU by up to 45%