He is our master : Jesus in the Thought of Swami Prabhupada

Now that steam, electricity, and the printing press have brought into closer communication the different races that inhabit the earth, and have expanded the minds of men, tending to dispel the illusion that God Almighty especially favours any particular people, it is time to proclaim to the world, that if a messenger of God appeared in Judea about nineteen hundred years ago, it is no less true that a messenger from the same God appeared in the quiet town of Navadweep (popularly known as Nadia) in Bengal, some fifteen centuries later. The former is known by the name of Jesus Christ; the latter is known in India by the name of Sree Gauranga, Sree Krishna Chaitanya, and several other names. If wonders attended Jesus, so also they attended Sree Gauranga of Nadia. The Christians have conferred an inestimable obligation upon those Hindus whose faith has been affected by Western materialism, by presenting Christ to them; and they, as a grateful return, are anxious to present Sree Krishna and Sree Gauranga to the people of the West. So begins Shishir Kumar Ghose\u27s lengthy biography of Caitanya, published at the turn of the twentieth century

VIEWPOINT: Hinduism and the Academy: Towards a Dialogue Between Scholar and Practitioner

Gupta articlulates a rationale as to why the position of both the academician and the practitioner are necessary for meaningful religious dialog

Robust and MaxMin Optimization under Matroid and Knapsack Uncertainty Sets

Consider the following problem: given a set system (U,I) and an edge-weighted graph G = (U, E) on the same universe U, find the set A in I such that the Steiner tree cost with terminals A is as large as possible: "which set in I is the most difficult to connect up?" This is an example of a max-min problem: find the set A in I such that the value of some minimization (covering) problem is as large as possible. In this paper, we show that for certain covering problems which admit good deterministic online algorithms, we can give good algorithms for max-min optimization when the set system I is given by a p-system or q-knapsacks or both. This result is similar to results for constrained maximization of submodular functions. Although many natural covering problems are not even approximately submodular, we show that one can use properties of the online algorithm as a surrogate for submodularity. Moreover, we give stronger connections between max-min optimization and two-stage robust optimization, and hence give improved algorithms for robust versions of various covering problems, for cases where the uncertainty sets are given by p-systems and q-knapsacks.Comment: 17 pages. Preliminary version combining this paper and http://arxiv.org/abs/0912.1045 appeared in ICALP 201

Generic Drug Policy In The U.s. - Impact On Drug Prices And Shortages

Generic medicines offer a significantly cheaper alternative to brand-name drugs and have become an indispensable means of maintaining patient access and adherence to treatments. In recent years, as a result of monopolistic and oligopolistic conditions, generic drugs have begun to increase in price, sometimes exorbitantly. The competitiveness of drug markets with respect to the number of generic manufacturers and the implications for drug prices and shortages have not been systematically studied. Two main analyses are presented in this study. First, using publicly available information, the timing of generic drug approvals and the total number of generic manufacturers for all small-molecule drugs approved between 1984 and 2015 were characterized. Second, this study investigated the impact on drug prices and shortages of a specific FDA regulation, called the Unapproved Drugs Initiative. The first analysis demonstrates that among 417 FDA-approved drugs, 210 were eligible for generic competition, and 77 (37%) had three or fewer generic drugs approved: 16 had three generic approvals, 9 had two, 16 had one, and 36 had zero. Among the 174 drugs with at least one generic approval, the median number of generic approvals was 7 (IQR, 4-12). Generic approvals were fewer among orphan-designated drugs when compared with non-orphan-designated drugs (18 of 33 [55%] vs. 156 of 177 [88%]; p\u3c0.001). The second analysis found that since 2006, 34 unapproved prescription drugs had been addressed by the Unapproved Drugs Initiative (UDI). Nearly 90% of those that went on to receive FDA approval were supported by literature reviews or bioequivalence studies, not new clinical trials. In addition, once targeted by the UDI, drugs experienced price and shortage increases of nearly 40% and 74%, respectively. Overall, more than one-third of drugs approved after 1984 and without protection from patents have three or fewer generic competitors, making them vulnerable to price increases. By unintentionally reducing the number of manufacturers for specific drugs, the FDA’s Unapproved Drugs Initiative led to higher prices and more frequent and longer shortages, highlighting the importance of robust generic competition. In conclusion, insufficient pharmaceutical competition has created an environment enabling price increases of old, off-patent generic drugs, such as Daraprim and Epipen. This study highlights that a substantial number of additional, similar drugs is vulnerable to such price increases for a variety of reasons. Future efforts to reform generic drug policy should seek to boost generic competition, more carefully regulate drug prices, and address brand-name pharmaceutical companies’ strategies to obstruct the ability of generic manufacturers to compete. In addition, physicians and patients should be bettered educated on the fact that a lack of generic competitors may mean that simply prescribing generic drugs will not make medications affordable for patients; alternative options may have to be explored. Such efforts are essential in ensuring continued patient access to affordable drugs

Dial a Ride from k-forest

The k-forest problem is a common generalization of both the k-MST and the dense-$k$-subgraph problems. Formally, given a metric space on $n$ vertices $V$, with $m$ demand pairs $\subseteq V \times V$ and a ``target'' $k\le m$, the goal is to find a minimum cost subgraph that connects at least $k$ demand pairs. In this paper, we give an $O(\min\{\sqrt{n},\sqrt{k}\})$-approximation algorithm for $k$-forest, improving on the previous best ratio of $O(n^{2/3}\log n)$ by Segev & Segev. We then apply our algorithm for k-forest to obtain approximation algorithms for several Dial-a-Ride problems. The basic Dial-a-Ride problem is the following: given an $n$ point metric space with $m$ objects each with its own source and destination, and a vehicle capable of carrying at most $k$ objects at any time, find the minimum length tour that uses this vehicle to move each object from its source to destination. We prove that an $\alpha$-approximation algorithm for the $k$-forest problem implies an $O(\alpha\cdot\log^2n)$-approximation algorithm for Dial-a-Ride. Using our results for $k$-forest, we get an $O(\min\{\sqrt{n},\sqrt{k}\}\cdot\log^2 n)$- approximation algorithm for Dial-a-Ride. The only previous result known for Dial-a-Ride was an $O(\sqrt{k}\log n)$-approximation by Charikar & Raghavachari; our results give a different proof of a similar approximation guarantee--in fact, when the vehicle capacity $k$ is large, we give a slight improvement on their results.Comment: Preliminary version in Proc. European Symposium on Algorithms, 200

Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits

In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed? The stochastic knapsack problem is of interest in its own right, but techniques developed for it are applicable to other stochastic packing problems. Indeed, ideas for this problem have been useful for budgeted learning problems, where one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to pull the arms a total of B times to maximize the reward obtained. Much recent work on this problem focus on the case when the evolution of the arms follows a martingale, i.e., when the expected reward from the future is the same as the reward at the current state. What can we say when the rewards do not form a martingale? In this paper, we give constant-factor approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations, and also for budgeted learning problems where the martingale condition is not satisfied. Indeed, we can show that previously proposed LP relaxations have large integrality gaps. We propose new time-indexed LP relaxations, and convert the fractional solutions into distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise a randomized adaptive scheduling algorithm. We hope our LP formulation and decomposition methods may provide a new way to address other correlated bandit problems with more general contexts

Understanding Type Ia Supernovae Through Their Host Galaxies Using the SDSS-II Supernova Survey

The recent discovery of the accelerating expansion of the Universe and thus the existence of dark energy was made possible by the study of Type Ia supernovae. These thermonuclear explosions of white dwarfs are excellent standardizable candles that can be seen out to great distances and used to constrain cosmological parameters. However, in an era when modern surveys are discovering hundreds of Type Ia supernovae and upcoming surveys plan to find thousands more, we are no longer limited by statistics, but are now being limited by systematic uncertainties in supernova cosmology. Among these systematic uncertainties are the nature of the supernova progenitor and the effect of the environment on the progenitor. An excellent way to probe these systematics is through the study of the galaxies that host Type Ia supernovae. Correlations have been found between supernova properties and the physical properties of their host galaxies such as mass, metallicity, and star formation rate. In this dissertation, I use supernovae from the full three-year Sloan Digital Sky Survey II (SDSS-II) Supernova Survey and multi-wavelength photometry of their host galaxies to find evidence of a correlation between supernova luminosities and the age of their hosts, a possible proxy for progenitor age. I also detail a method of host galaxy identification, tested and applied to the many thousands of SDSS-II supernova candidates, which will be published in the upcoming final data release of the Supernova Survey. In addition, I present work in which I compute the luminosity functions for Type Ia supernovae and their host galaxies. This work and continuing work in this vein can help shed light on the nature of dark energy and improve the utility of Type Ia supernovae as cosmological distance indicators