Miracle-dz/UniFAR-FTUs huggingface dataset:
When Does the Onset of Multiple Stellar Populations in Star Clusters Occur. II. No Evidence of Multiple Stellar Populations in Lindsay 113

The presence of multiple populations (MPs) in almost all globular clusters (GCs) older than ∼10 Gyr has drawn lots of attention. Recently, cumulative evidence has indicated that extragalactic GCs that are older than 2 Gyr seem to also harbor MPs; however, those that are younger than 2 Gyr do not. These observations seem to imply that age is a primary property that controls the presence of MPs in star clusters. However, because of the lack of studies of intermediate-age (∼2–6 Gyr old), low-mass clusters, it is unclear if the cluster mass, in addition to age, also plays a role in the occurrence of MPs. In this work, we studied a ∼4 Gyr old, low-mass (∼23,000 M⊙) cluster, Lindsay-113, in the Small Magellanic Cloud. Using Hubble Space Telescope photometry, we found that the width of the red-giant branch in this cluster, when measured in a specific color index that is sensitive to star-to-star chemical variations, can be adequately explained by a “simple” stellar population model with some possible noises contributed from measurement uncertainty, photometric artifact, and differential reddening. The comparison of observations with predictions from synthetic spectra indicates that the internal chemical spread in nitrogen abundance, which is a signature of MPs, would not exceed 0.2 dex. Since Lindsay 113 is significantly older than other GCs with MPs, we suggest that the onset of MPs is likely determined by the combination of cluster age and mass.
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation

Dialogue systems dealing with multi-domain tasks are highly required. How to record the state remains a key problem in a task-oriented dialogue system. Normally we use human-defined features as dialogue states and apply a state tracker to extract these features. However, the performance of such a system is limited by the error propagation of a state tracker. In this paper, we propose a dialogue generation model that needs no external state trackers and still benefits from human-labeled semantic data. By using a teacher-student framework, several teacher models are firstly trained in their individual domains, learn dialogue policies from labeled states. And then the learned knowledge and experience are merged and transferred to a universal student model, which takes raw utterance as its input. Experiments show that the dialogue system trained under our framework outperforms the one uses a belief tracker.
Parameterized and Consistency Tests of Gravity with GravitationalWaves: Current and Future

Gravitational wave observations offer unique opportunities to probe gravity in the strong and dynamical regime, which was difficult to access previously. We here review two theory-agnostic ways to carry out tests of general relativity with gravitational waves, namely (i) parameterized waveform tests and (ii) consistency tests between the inspiral and merger-ringdown portions. For each method, we explain the formalism, followed by results from existing events, and finally we discuss future prospects with upgraded detectors, including the possibility of using multi-band gravitational-wave observations with ground-based and space-borne interferometers. We show that such future observations have the potential to improve upon current bounds on theories beyond general relativity by many orders of magnitude. We conclude by listing several open questions that remain to be addressed.
Potential kick velocity distribution of black hole X-ray binaries and implications for natal kicks

We use very long baseline interferometry to measure the proper motions of three black hole X-ray binaries (BHXBs). Using these results together with data from the literature and Gaia DR2 to collate the best available constraints on proper motion, parallax, distance, and systemic radial velocity of 16 BHXBs, we determined their three-dimensional Galactocentric orbits. We extended this analysis to estimate the probability distribution for the potential kick velocity (PKV) a BHXB system could have received on formation. Constraining the kicks imparted to BHXBs provides insight into the birth mechanism of black holes (BHs). Kicks also have a significant effect on BH–BH merger rates, merger sites, and binary evolution, and can be responsible for spin–orbit misalignment in BH binary systems. 75 per cent of our systems have potential kicks $\gt 70\, \rm {km\,s^{-1}}$. This suggests that strong kicks and hence spin–orbit misalignment might be common among BHXBs, in agreement with the observed quasi-periodic X-ray variability in their power density spectra. We used a Bayesian hierarchical methodology to analyse the PKV distribution of the BHXB population, and suggest that a unimodal Gaussian model with a mean of 107 $\pm \,\,16\, \rm {km\,s^{-1}}$ is a statistically favourable fit. Such relatively high PKVs would also reduce the number of BHs likely to be retained in globular clusters. We found no significant correlation between the BH mass and PKV, suggesting a lack of correlation between BH mass and the BH birth mechanism. Our python code allows the estimation of the PKV for any system with sufficient observational constraints.
Enhancing Engagement during Robot-Assisted Rehabilitation Integrated with Motor Imagery Task

Stroke remains the most common cause of motor deficits for adults. Enhancing engagement has become the focus of recent research with the aim of improving the efficiency of robot-assisted rehabilitation. Since motor imagery (MI) has the potential to engage the subject, the objective of this study is to explore the influence of complementing robot-assisted rehabilitation with MI during training exercises. An experiment was designed and conducted in which 10 healthy subjects were recruited to participate in two separate sessions. An acoustic-cue-based experimental paradigm was applied in both sessions. In the first session, each patient was required to imagine moving arm after the cue, then the robot device drove the arm during the MI process; while in the second session, the robotic device drove the user to move without requiring the MI tasks. Each session consisted of 20 trails, in which electroencephalogram (EEG) was recorded to analyze the activated brain regions. Analyses showed that the activation of sensorimotor cortex (SM1) was the strongest during passive movement (PM) integrated with MI than either PM or MI alone. The results indicated that robot-assisted training integrated with MI task can enhance the subject's engagement as shown by a stronger event related desynchronization (ERD), which can lead to a stronger stimulation on SM1. This indication can explain why only passive movement driven by robotic device has a low rehabilitation efficiency during clinical practice. The result can also contribute to the understanding of the mechanism underlying the brain computer interface (BCI) supported rehabilitation therapy, which can improve rehabilitation efficiency by closing the loop between the motor intention and sensorimotor feedback.
Enclosing Chebyshev Expansions in Linear Time

We consider the problem of computing rigorous enclosures for polynomials represented in the Chebyshev basis. Our aim is to compare and develop algorithms with a linear complexity in terms of the polynomial degree. A first category of methods relies on a direct interval evaluation of the given Chebyshev expansion in which Chebyshev polynomials are bounded, e.g., with a divide-and-conquer strategy. Our main category of methods that are based on the Clenshaw recurrence includes interval Clenshaw with defect correction (ICDC), and the spectral transformation of Clenshaw recurrence rewritten as a discrete dynamical system. An extension of the barycentric representation to interval arithmetic is also considered that has a log-linear complexity as it takes advantage of a verified discrete cosine transform. We compare different methods and provide illustrative numerical experiments. In particular, our eigenvalue-based methods are interesting for bounding the range of high-degree interval polynomials. Some of the methods rigorously compute narrow enclosures for high-degree Chebyshev expansions at thousands of points in a few seconds on an average computer. We also illustrate how to employ our methods as an automatic a posteriori forward error analysis tool to monitor the accuracy of the Chebfun feval command.
Evaluation of Morphological Embeddings for the Russian Language

A number of morphology-based word embedding models were introduced in recent years. However, their evaluation was mostly limited to English, which is known to be a morphologically simple language. In this paper, we explore whether and to what extent incorporating morphology into word embeddings improves performance on downstream NLP tasks, in the case of morphologically rich Russian language. NLP tasks of our choice are POS tagging, Chunking, and NER -- for Russian language, all can be mostly solved using only morphology without understanding the semantics of words. Our experiments show that morphology-based embeddings trained with Skipgram objective do not outperform existing embedding model -- FastText. Moreover, a more complex, but morphology unaware model, BERT, allows to achieve significantly greater performance on the tasks that presumably require understanding of a word's morphology.
Constraining the detectability of water ice in debris disks

Context. Water ice is important for the evolution and preservation of life. Identifying the distribution of water ice in debris disks is therefore of great interest in the field of astrobiology. Furthermore, icy dust grains are expected to play important roles throughout the entire planet formation process. However, currently available observations only allow deriving weak conclusions about the existence of water ice in debris disks. Aims. We investigate whether it is feasible to detect water ice in typical debris disk systems. We take the following ice destruction mechanisms into account: sublimation of ice, dust production through planetesimal collisions, and photosputtering by UV-bright central stars. We consider icy dust mixture particles with various shapes consisting of amorphous ice, crystalline ice, astrosilicate, and vacuum inclusions (i.e., porous ice grains). Methods. We calculated optical properties of inhomogeneous icy dust mixtures using effective medium theories, that is, Maxwell-Garnett rules. Subsequently, we generated synthetic debris disk observables, such as spectral energy distributions and spatially resolved thermal reemission and scattered light intensity and polarization maps with our code DMS. Results. We find that the prominent ~3 and 44 μm water ice features can be potentially detected in future observations of debris disks with the James Webb Space Telescope (JWST) and the Space Infrared telescope for Cosmology and Astrophysics (SPICA). We show that the sublimation of ice, collisions between planetesimals, and photosputtering caused by UV sources clearly affect the observational appearance of debris disk systems. In addition, highly porous ice (or ice-rich aggregates) tends to produce highly polarized radiation at around 3 μm. Finally, the location of the ice survival line is determined by various dust properties such as a fractional ratio of ice versus dust, physical states of ice (amorphous or crystalline), and the porosity of icy grains.
A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI

With the growth of location-based services, indoor localization is attracting great interests as it facilitates further ubiquitous environments. Specifically, device free localization using wireless signals is getting increased attention as human location is estimated using its impact on the surrounding wireless signals without any active device tagged with subject. In this paper, we propose MuDLoc, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple APs. Multi-view learning is an emerging technique in machine learning which improve performance by utilizing diversity from different view data. In MuDLoc, the localization is modeled as a pattern matching problem, where the target location is predicted based on similarity measure of CSI features of an unknown location with those of the training locations. MuDLoc implements Generalized Inter-view and Intra-view Discriminant Correlation Analysis (GI$^{2}$DCA), a discriminative feature extraction approach using multi-view CSIs. It incorporates inter-view and intra-view class associations while maximizing pairwise correlations across multi-view data sets. A similarity measure is performed to find the best match to localize a subject. Experimental results from two cluttered environments show that MuDLoc can estimate location with high accuracy which outperforms other benchmark approaches.
CatE: Category-Name GuidedWord Embedding

Unsupervised word embedding has benefited a wide spectrum of NLP tasks due to its effectiveness of encoding word semantics in distributed word representations. However, unsupervised word embedding is a generic representation, not optimized for specific tasks. In this work, we propose a weakly-supervised word embedding framework, CatE. It uses category names to guide word embedding and effectively selects category representative words to regularize the embedding space where the categories are well separated. Experiments show that our model outperforms unsupervised word embedding models significantly on both document classification and category representative words retrieval tasks.
PrivFT: Private and Fast Text Classification With Homomorphic Encryption

We present an efficient and non-interactive method for Text Classification while preserving the privacy of the content using Fully Homomorphic Encryption (FHE). Our solution (named Private Fast Text (PrivFT)) provides two services: 1) making inference of encrypted user inputs using a plaintext model and 2) training an effective model using an encrypted dataset. For inference, we use a pre-trained plaintext model and outline a system for homomorphic inference on encrypted user inputs with zero loss to prediction accuracy compared to the non-encrypted version. In the second part, we show how to train a supervised model using fully encrypted data to generate an encrypted model. For improved performance, we provide a GPU implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme that shows 1 to 2 orders of magnitude speedup against existing implementations. We build PrivFT on top of our FHE engine in GPUs to achieve a run time per inference of 0.17 seconds for various Natural Language Processing (NLP) public datasets. Training on a relatively large encrypted dataset is more computationally intensive requiring 5.04 days.
USERS’ SOCIOCULTURAL ORIENTATION AND SMART SYSTEMS ACCEPTANCE LINK: DO DEMOGRAPHICS MATTER?

ABSTRACT This research investigates the impact of sociocultural determinants on people’s perceptions and usage of smart systems in the United Arab Emirates (UAE). This research used the Theory of reasoned action, Unified theory of acceptance and use of technology (UTAUT) and Frambach and Schillewaert’s framework. These theories which focus on the determinants of technology acceptance behavior of users have been widely used in contemporary technology acceptance research. Data were collected from the UAE Ministries of Foreign Affairs, Health, and Economy using structured questionnaires. A total of 357 responses were analyzed using multivariate statistical techniques such as regression analysis and factor analysis. Multiple regression analysis helped to determine the direction of relationships between the constructs representing users’ sociocultural orientation, attitude toward smart systems and acceptable behavior. Factor analysis served to identify the underlying dimensions of the above constructs. The major finding of this research is that cultural values and peers’ influence significantly affect users’ perceptions and applications of technological innovations. Furthermore, the study reveals that users’ age and gender wield critical moderating effects on the relationship between cultural values and attitude toward smart systems. These findings have important implications for acceptance and implementation of smart systems in the UAE which are highlighted in this paper. This paper also identifies the limits of this research and explores the venues of further research in this field.
Factors Affecting the Adoption of an Electronic Word of Mouth Message: A Meta-Analysis

Abstract Electronic word of mouth (eWoM) messages are increasingly consequential for consumers’ decisions regarding products/services. This has led to a large body of scholarly research on factors affecting eWoM message adoption. Adoption of an eWoM message refers to accepting the information and recommendations contained in an eWoM message, which consequently influence consumers’ cognitive and behavioral tendencies toward pertinent products/services. Notwithstanding the contributions of prior eWoM studies, we observe inconsistent findings across these studies that make a consensus difficult to reach. Lack of consistency is also evident among eWoM service providers in the selection and presentation order of the pertinent factors on their sites. To address this gap, we draw on the heuristic-systematic model in a meta-analytic structural equation modeling study to test a nomological eWoM adoption model that assesses the factors affecting the adoption of an eWoM message. Our meta-analysis of 87 eWoM studies, comprising 105,318 observations, sheds light on eight factors toward eWoM message adoption. Our findings unravel the multitude of ways in which these factors can influence eWoM message adoption and show the relative importance of these factors based on their total effects on eWoM message adoption. This enables the eWoM service providers to enhance the inconsistencies in the selection and presentation order of important factors toward eWoM message adoption on their sites.
Shopping in Virtual Reality Stores: The Influence of Immersion on System Adoption

Abstract Companies have the opportunity to better engage potential customers by presenting products to them in a highly immersive virtual reality (VR) shopping environment. However, a minimal amount is known about why and whether customers will adopt such fully immersive shopping environments. We therefore develop and experimentally validate a theoretical model, which explains how immersion affects adoption. The participants experienced the environment by using a head-mounted display (high immersion) or by viewing product models in 3D on a desktop (low immersion). We find that immersion does not affect the users’ intention to reuse the shopping environment, because two paths cancel each other out: Highly immersive shopping environments positively influence a hedonic path through telepresence, but surprisingly, they negatively influence a utilitarian path through product diagnosticity. We can explain this effect via low readability of product information in the VR environment and expect VR’s full potential to develop when the technology is further advanced. Our study contributes to literature on immersive systems and IS adoption research by introducing a research model for the adoption of VR shopping environments. A key practical implication of our study is that system designers need to pay special attention to the current state of technology when designing VR applications.
Factors Affecting the Offshore Deployment of Interorganizational Systems in China: A Case Study Analysis

ABSTRACT Because of the significant market potential in China, many international enterprises are moving their production line to China. Accompanying production offshoring is the shifting of information systems to ensure that the production processes remain efficient and effective both at home and abroad. However, many enterprises encounter problems while deploying offshore information systems in China, especially when the systems are interorganizational systems (IOS) that span organizational boundaries and involve the management of relationships among participants. Therefore, this paper aims to identify critical success factors of offshore interorganizational systems in China through a case study of a supply chain relationship based on an e-procurement system between a Taiwanese personal computer manufacturer and its Chinese partners. Although this system had been well used by Taiwanese users, the performance of this system in China was relatively unsatisfactory in terms of grant complaints from Chinese users. Based on the relational view of the firm, four IOS-specific relational factors are proposed and examined: (1) IOS-specific assets, (2) IOS knowledge-sharing routines, (3) system complementarity, and (4) IOS governance. The case analysis reveals that effective IOS governance most strongly affects the results of offshoring in China, whereas system complementarity has the least influence.
The Impact of Unconventional Monetary Policy of ECB to Central and Eastern European Countries : A Panel VAR Analysis

In this thesis we examine the macroeconomic interactions of unconventional monetary policy introduced by European Central bank during crisis by estimating a panel vector autoregression. We study impact of such policies using monthly data from 13 Central and Eastern European countries within sevenyear period from 2008 to 2014. We find a positive reactions of output and prices to expansionary unconventional monetary policy shock. Our results provide evidence that decrease in shadow policy rate of ECB leads to rise in output as well as temporary rise in inflation, however, the effect on inflation is weaker and less persistent. We also find that unconventional monetary policy positively influences market uncertainty, but we do not find any significant effect on exchange rates. Individual country estimates suggest that the reaction of exchange rates to non-standard monetary policy shock significantly vary across countries.
7 The Pattern of Consumption for Food Away From Home ( FAFH ) of Modern Filipino Households

Food has traditionally been the top priority item in any household’s consumption basket. Whether consumed at home or outside the home, households usually apportion about half of their total budget on food. In the modern times however, because of the shifting consumer preferences and dramatic growth in income, especially in the cities, there has been a remarkable change in household’s food consumption patterns. In the Philippines, the proliferation of vast arrays of food service facilities such as conventional full-service and fast-food restaurants, coffee shops, food courts, roadside stalls, canteens, delicatessens, etc., together with improved purchasing power, growing time constraints among household members and incessant bombardment of promotional ads across various media collectively create a strong impetus among Filipinos to “eat out. ” The result may be a steady convergence of the proportion of food eaten at home (FAH) and the proportion of food eaten away from home (FAFH). The issue of the increasing importance of FAFH has not been a priority area among local researchers and policy makers as there is an obvious dearth of research studies, executive and legislative concerns related to it.