On the limits of measuring the bulge and disk properties of local and high-redshift massive galaxies

A considerable fraction of the massive quiescent galaxies at \emph{z} $\approx$ 2, which are known to be much more compact than galaxies of comparable mass today, appear to have a disk. How well can we measure the bulge and disk properties of these systems? We simulate two-component model galaxies in order to systematically quantify the effects of non-homology in structures and the methods employed. We employ empirical scaling relations to produce realistic-looking local galaxies with a uniform and wide range of bulge-to-total ratios ($B/T$), and then rescale them to mimic the signal-to-noise ratios and sizes of observed galaxies at \emph{z} $\approx$ 2. This provides the most complete set of simulations to date for which we can examine the robustness of two-component decomposition of compact disk galaxies at different $B/T$. We confirm that the size of these massive, compact galaxies can be measured robustly using a single S\'{e}rsic fit. We can measure $B/T$ accurately without imposing any constraints on the light profile shape of the bulge, but, due to the small angular sizes of bulges at high redshift, their detailed properties can only be recovered for galaxies with $B/T$ \gax\ 0.2. The disk component, by contrast, can be measured with little difficulty

Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

Green accounting: Developing versus developed economies

Abstract. Businesses and corporates have started to formulate strategies to opt for environment-friendly and green operations. The same has also conceived the idea of green accounting that emphasizes taking environmental factors into account of corporate financial consideration and reports. Less to date, have made a comparative review for those progresses of green accounting in the developed versus developing economies, and to offer insights for academics and practitioners. This article offered a compact discussion of this issue and provides suggestions to theory and practices.Keywords. Green accounting, Developing economies, Developed economies.JEL. C23, F62, N17

Low-Cost Learning via Active Data Procurement

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with $T$ arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the same order.Comment: Full version of EC 2015 paper. Color recommended for figures but nonessential. 36 pages, of which 12 appendi

Paradoxical Language in Chan Buddhism

Chinese Chan or Zen Buddhism is renowned for its improvisational, atypical, and perplexing use of words. In particular, the tradition’s encounter dialogues, which took place between Chan masters and their interlocutors, abound in puzzling, astonishing, and paradoxical ways of speaking. In this chapter, we are concerned with Chan’s use of paradoxical language. In philosophical parlance, a linguistic paradox comprises the confluence of opposite or incongruent concepts in a way that runs counter to our common sense and ordinary rational thinking. One naturally wonders about Chan masters’ rationales for their use of paradox. There are also concerns about whether the use violates the logical principle of noncontradiction to the effect that nothing can be both P and not-P all over in the same way at the same time. Chan became a viable Chinese Buddhist tradition during the Tang dynasty (618−907) and continued to develop for several centuries. The tradition had produced a huge literature; consequently, our investigation of its use of paradox cannot but be limited and selective. In the second section, I first sketch key ideas of Chan that are pertinent to our investigation and then examine the use of paradox in the sermons associated with certain Tang masters of the southern Chan. In the third section, I analyze the presence of paradoxical language in post-Tang encounter dialogues. The fourth section concludes

Meaning, Understanding, and Knowing-what: An Indian Grammarian Notion of Intuition (pratibha)

For Bhartrhari, a fifth-century Indian grammarian-philosopher, all conscious beings—beasts, birds and humans—are capable of what he called pratibha, a flash of indescribable intuitive understanding such that one knows what the present object “means” and what to do with it. Such an understanding, if correct, amounts to a mode of knowing that may best be termed knowing-what, to distinguish it from both knowing-that and knowing-how. This paper attempts to expound Bhartrhari’s conception of pratibha in relation to the notions of meaning, understanding, and knowing. First, I touch briefly on Bhartrhari’s views of consciousness and language, and examine at some length his indescribability thesis concerning the intuitive meaning of a sentence. Then, I delineate the general features of pratibha as intuitive understanding and discuss its probable range in relation to expert intuition and sense perception. Thereafter, I relate pratibha to the notion of knowing-what and show why these two notions are to be differentiated from knowing-that and knowing-how. The paper concludes with some remarks on the contemporary relevance of Bhartrhari’s conception of pratibha