Washington Photometry of the Globular Clusters in the Virgo Giant Elliptical Galaxy M86

We present a photometric study of the globular clusters (GCs) in the Virgo giant elliptical galaxy M86 based on Washington CT1 images. The colors of the GCs in M86 show a bimodal distribution with a blue peak at (C -T1) = 1.30 and a red peak at (C -T1) = 1.72. The spatial distribution of the red GCs is elongated similarly to that of the stellar halo, while that of the blue GCs is roughly circular. The radial number density profile of the blue GCs is more extended than that of the red GCs. The radial number density profile of the red GCs is consistent with the surface brightness profile of the M86 stellar halo. The GC system has a negative radial color gradient, which is mainly due to the number ratio of the blue GCs to the red GCs increasing as galactocentric radius increase. The bright blue GCs in the outer region of M86 show a blue tilt: the brighter they are, the redder their mean colors get. These results are discussed in comparison with other Virgo giant elliptical galaxies.Comment: 15 pages, 13 figures, Accepted by Journal of the Korean Astronomical Societ

Curb Your Enthusiasm: The Rise of Hedge Fund Activist Shareholders and the Duty Of Loyalty

Shareholder activism has been a growing problem in the corporate world, creating numerous dilemmas for the board of directors of companies. Activist shareholders can unsettle a company, pressuring the directors to make decisions according to the course of business the activists would prefer, and thus interfering with the traditional role of directors as the decision-makers of a company. With this new development in the business world, legal scholars have been debating if this activism needs to be controlled and, if so, what measures can be taken to reach a balance. This Note examines the traditional corporate principles such as the shareholder primacy theory and the principle of “one share, one vote,” evaluating the benefits and the costs of adhering to these theories amidst the changing landscape in the business and legal world. This Note then proposes that the traditional concept of the duty of loyalty can be applied to activist shareholders, much like it has been applied to the directors and majority shareholders in the past, based on a fact-by-fact analysis

Characterizing the Vickrey Combinatorial Auction by Induction

This note studies the allocation of heterogeneous commodities to agents whose private values for combinations of these commodities are monotonic by inclusion. This setting can accommodate the presence of complementarity and substitutability among the heterogeneous commodities. By using induction logic, we provide an elementary proof of Holmstrom's (1919) characterization of the Vickrey combinatorial auction as the unique efficient, strategy-proof, and individually rational allocation rule. Our proof method can also be applied to domains to which his proof cannot be.

Identification of Outlying Observations with Quantile Regression for Censored Data

Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results. While there are many statistical outlier detection algorithms and software programs for uncensored data, few are available for censored data. In this article, we propose three outlier detection algorithms based on censored quantile regression, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches. The performance of the three algorithms was investigated in simulation studies. In addition, real data from SEER database, which contains a variety of data sets related to various cancers, is illustrated to show the usefulness of our methodology. The algorithms are implemented into an R package OutlierDC which can be conveniently employed in the \proglang{R} environment and freely obtained from CRAN

Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision

The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervised learning have shown that effective representations can be learnt from natural cross-modal synchrony. We build on earlier work to train embeddings that are more discriminative for uni-modal downstream tasks. To this end, we propose a novel training strategy that not only optimises metrics across modalities, but also enforces intra-class feature separation within each of the modalities. The effectiveness of the method is demonstrated on two downstream tasks: lip reading using the features trained on audio-visual synchronisation, and speaker recognition using the features trained for cross-modal biometric matching. The proposed method outperforms state-of-the-art self-supervised baselines by a signficant margin.Comment: Under submission as a conference pape

Factors Influencing Consumer Behavioural Intention To Choose Functional Foods

Kajian ini menyelidik faktor-faktor yang mempengaruhi niat konsumer di Malaysia dalam memilih makanan berfungsi. This research examined the factors that influence Malaysian consumers’ behavioural intention towards choosing functional foods

Perfect match: Improved cross-modal embeddings for audio-visual synchronisation

This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization. Here, we set up the problem as one of cross-modal retrieval, where the objective is to find the most relevant audio segment given a short video clip. The method builds on the recent advances in learning representations from cross-modal self-supervision. The main contributions of this paper are as follows: (1) we propose a new learning strategy where the embeddings are learnt via a multi-way matching problem, as opposed to a binary classification (matching or non-matching) problem as proposed by recent papers; (2) we demonstrate that performance of this method far exceeds the existing baselines on the synchronization task; (3) we use the learnt embeddings for visual speech recognition in self-supervision, and show that the performance matches the representations learnt end-to-end in a fully-supervised manner.Comment: Preprint. Work in progres