Are drug companies living up to their human rights responsibilities? The perspective of the former United Nations Special Rapporteur (2002-2008).

BACKGROUND TO THE DEBATE: The human rights responsibilities of drug companies have been considered for years by nongovernmental organizations, but were most sharply defined in a report by the UN Special Rapporteur on the right to health, submitted to the United Nations General Assembly in August 2008. The "Human Rights Guidelines for Pharmaceutical Companies in relation to Access to Medicines" include responsibilities for transparency, management, monitoring and accountability, pricing, and ethical marketing, and against lobbying for more protection in intellectual property laws, applying for patents for trivial modifications of existing medicines, inappropriate drug promotion, and excessive pricing. Two years after the release of the Guidelines, the PLoS Medicine Debate asks whether drug companies are living up to their human rights responsibilities. Sofia Gruskin and Zyde Raad from the Harvard School of Public Health say more assessment is needed of such responsibilities; Geralyn Ritter, Vice President of Global Public Policy and Corporate Responsibility at Merck & Co. argues that multiple stakeholders could do more to help States deliver the right to health; and Paul Hunt and Rajat Khosla introduce Mr. Hunt's work as the UN Special Rapporteur on the right to the highest attainable standard of health, regarding the human rights responsibilities of pharmaceutical companies and access to medicines

The human right to medicines

This article considers the component of the right to the highest standard of health that relates to medicines, including essential medicines. Using the right-to-health analytical framework that has been developed in recent years, the first section focuses on the responsibilities of States. The second section provides a brief introduction to the responsibilities of pharmaceutical companies

Effect of control sampling rates on model-based manipulator control schemes

The effect of changing the control sampling period on the performance of the computed-torque and independent joint control schemes is discussed. While the former utilizes the complete dynamics model of the manipulator, the latter assumes a decoupled and linear model of the manipulator dynamics. Researchers discuss the design of controller gains for both the computed-torque and the independent joint control schemes and establish a framework for comparing their trajectory tracking performance. Experiments show that within each scheme the trajectory tracking accuracy varies slightly with the change of the sampling rate. However, at low sampling rates the computed-torque scheme outperforms the independent joint control scheme. Based on experimental results, researchers also conclusively establish the importance of high sampling rates as they result in an increased stiffness of the system

Growth and Prospects of Agro-Processing Industries in Punjab

Present study attempts to examine the growth and prospects of agro-processing industries in Punjab in the post liberalization period. Punjab that used to perform reasonably well in terms of industrialization, few years back, is today facing the industrial crunch owing to obvious reasons. Keeping present trends in mind, the state needs to emulate the growth path adopted by China, explicitly, wherein thrust to development had been on the manufacturing sector. With availability of food grain production round the year, it seems viable also in the state to develop the agro processing industries. In fact, researchers have pointed out that Punjab’s agriculture has reached a stage where its sustainability is in doubt. If such apprehensions turn out to be true, then future of masses will also land in darkness. Development of agro-processing industries at this juncture, are perceived to be the stimulator that can revamp the vanished glory of the state. The study is an attempt made through empirical framework to find out the conditions for the development of agro-processing industries in the state.Keywords. Prospects, Agro-processing, Development.JEL. L52, L66, L67

The Sensing Capacity of Sensor Networks

This paper demonstrates fundamental limits of sensor networks for detection problems where the number of hypotheses is exponentially large. Such problems characterize many important applications including detection and classification of targets in a geographical area using a network of sensors, and detecting complex substances with a chemical sensor array. We refer to such applications as largescale detection problems. Using the insight that these problems share fundamental similarities with the problem of communicating over a noisy channel, we define a quantity called the sensing capacity and lower bound it for a number of sensor network models. The sensing capacity expression differs significantly from the channel capacity due to the fact that a fixed sensor configuration encodes all states of the environment. As a result, codewords are dependent and non-identically distributed. The sensing capacity provides a bound on the minimal number of sensors required to detect the state of an environment to within a desired accuracy. The results differ significantly from classical detection theory, and provide an ntriguing connection between sensor networks and communications. In addition, we discuss the insight that sensing capacity provides for the problem of sensor selection.Comment: Submitted to IEEE Transactions on Information Theory, November 200

Boilerplate Removal using a Neural Sequence Labeling Model

The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.Comment: WWW20 Demo pape