Doxycycline induced oesophageal ulcers in a navy ship crewmember

A healthy 25-year-old crewmember of a navy ship was diagnosed with suspected pneumonia and prescribed 100 mg twice a day of doxycycline for 10 days. During the 7th day of treatment the patient joined his navy ship to sail aboard and 2 days later, immediately after taking the doxycycline capsule, he felt a forceful pain in the median chest which was followed with odynophagia of both solid foods and liquids. The patient adhered to the administration guidelines of the doxycycline, except drinking 330 mL of beer, 3 h before taking the capsule. A working diagnosis of atypical chest pain, possibly due to oesophagitis, was made. The patient was advised to fast and rest and treatment with intravenously (IV) H2-receptor antagonist, clear fluids and analgesics was started. Later on, due to lack of improvement in the patient’s status and the potential risk of future deterioration, a decision was made to evacuate the patient to a hospital. Gastroscopy, revealed 3 ulcers in the mid-oesophagus and the patient was hospitalised for treated of IV antacids and fluids with gradual improvement. This case emphasizes the limitation of diagnosing and treating a common side effect in the middle of the sea and the potential risk in taking medications with alcohol

Suboptimal Choices and the Need for Experienced Individual Well-Being in Economic Analysis

Standard economic analysis assumes that people make choices that maximize their utility. Yet both popular discourse and other fields assume that people sometimes fail to make optimal choices and thus adversely affect their own happiness. Most social sciences thus frequently describe some patterns of decision as suboptimal. We review evidence of suboptimal choices that arise for two reasons. First, people err in predicting the utility they may accrue from available choice options due to the evaluation mode. Second, people choose on the basis of salient rules that are unlikely to maximize utility. Our review is meant to highlight the possibility of a research program that combines economic analysis with measures of experienced individual well-being to improve people's happiness.suboptimal choice, individual well-being, experienced utility, evaluation mode, salient rule, utility misprediction

Clustering in Hypergraphs to Minimize Average Edge Service Time

We study the problem of clustering the vertices of a weighted hypergraph such that on average the vertices of each edge can be covered by a small number of clusters. This problem has many applications such as for designing medical tests, clustering files on disk servers, and placing network services on servers. The edges of the hypergraph model groups of items that are likely to be needed together, and the optimization criteria which we use can be interpreted as the average delay (or cost) to serve the items of a typical edge. We describe and analyze algorithms for this problem for the case in which the clusters have to be disjoint and for the case where clusters can overlap. The analysis is often subtle and reveals interesting structure and invariants that one can utilize

Codes for Load Balancing in TCAMs: Size Analysis

Traffic splitting is a required functionality in networks, for example for load balancing over paths or servers, or by the source's access restrictions. The capacities of the servers (or the number of users with particular access restrictions) determine the sizes of the parts into which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Recent works suggested algorithms to compute a smallest implementation of a given partition in the longest prefix match (LPM) model. In this paper we analyze properties of such minimal representations and prove lower and upper bounds on their size. The upper bounds hold for general TCAMs, and we also prove an additional lower-bound for general TCAMs. We also analyze the expected size of a representation, for uniformly random ordered partitions. We show that the expected representation size of a random partition is at least half the size for the worst-case partition, and is linear in the number of parts and in the logarithm of the size of the address space

Statistical approach to NoC design

Chip multiprocessors (CMPs) combine increasingly many general-purpose processor cores on a single chip. These cores run several tasks with unpredictable communication needs, resulting in uncertain and often-changing traffic patterns. This unpredictability leads network-on-chip (NoC) designers to plan for the worst-case traffic patterns, and significantly over-provision link capacities. In this paper, we provide NoC designers with an alternative statistical approach. We first present the traffic-load distribution plots (T-Plots), illustrating how much capacity over-provisioning is needed to service 90%, 99%, or 100% of all traffic patterns. We prove that in the general case, plotting T-Plots is #P-complete, and therefore extremely complex. We then show how to determine the exact mean and variance of the traffic load on any edge, and use these to provide Gaussian-based models for the T-Plots, as well as guaranteed performance bounds. Finally, we use T-Plots to reduce the network power consumption by providing an efficient capacity allocation algorithm with predictable performance guarantees. © 2008 IEEE

Statistical approach to networks-on-chip

Chip multiprocessors (CMPs) combine increasingly many general-purpose processor cores on a single chip. These cores run several tasks with unpredictable communication needs, resulting in uncertain and often-changing traffic patterns. This unpredictability leads network-on-chip (NoC) designers to plan for the worst case traffic patterns, and significantly overprovision link capacities. In this paper, we provide NoC designers with an alternative statistical approach. We first present the traffic-load distribution plots (T-Plots), illustrating how much capacity overprovisioning is needed to service 90, 99, or 100 percent of all traffic patterns. We prove that in the general case, plotting T-Plots is #P-complete, and therefore extremely complex. We then show how to determine the exact mean and variance of the traffic load on any edge, and use these to provide Gaussian-based models for the T-Plots, as well as guaranteed performance bounds. We also explain how to practically approximate T-Plots using random-walk-based methods. Finally, we use T-Plots to reduce the network power consumption by providing an efficient capacity allocation algorithm with predictable performance guarantees. © 2006 IEEE

Sketches for Blockchains

Blockchains suffer from a critical scalability problem where traditionally each network node maintains all network state, including records since the establishment of the blockchain. Sketches are popular hash-based data structures used to represent a large amount of data while supporting particular queries such as on set membership, cardinality estimation and identification of large elements. Often, to achieve time and memory savings, sketches allow potential inaccuracies in answers to the queries. The design of popular blockchain networks such as Bitcoin and Ethereum makes use of sketches for various tasks such as summarization of transaction blocks or declaring the interests of light nodes. Similarly, they seem natural to deal with the size of the state in blockchains. In this paper, we study existing and potential future applications of sketches in blockchains. We first summarize current blockchain use cases of sketches. Likewise, we explore how this coupling can be generalized to a wider range of sketches and additional functionalities. In particular, we explain how sketches can detect anomalies based on efficient an summary of the state or traffic

Efficient Measurement on Programmable Switches Using Probabilistic Recirculation

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the RMT programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses \emph{Probabilistic Recirculation} to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also analyze the effect of each architectural constraint on the measurement accuracy and provide insights for measurement algorithm designers.Comment: To appear in IEEE ICNP 201