Fast computation of minimal elementary decompositions of metabolic flux vectors

The concept of elementary flux vector is valuable in a number of applications of metabolic engineering. For instance, in metabolic flux analysis, each admissible flux vector can be expressed as a non-negative linear combination of a small number of elementary flux vectors. However a critical issue concerns the total number of elementary flux vectors which may be huge because it combinatorially increases with the size of the metabolic network. In this paper we present a fast algorithm that randomly computes a decomposition of admissible flux vectors in a minimal number of elementary flux vectors without explicitly enumerating all of them.     

The State of the Art in Topology‐Based Visualization of Unsteady Flow

Vector fields are a common concept for the representation of many different kinds of flow phenomena in science and engineering. Methods based on vector field topology are known for their convenience for visualizing and analysing steady flows, but a counterpart for unsteady flows is still missing. However, a lot of good and relevant work aiming at such a solution is available. We give an overview of previous research leading towards topology‐based and topology‐inspired visualization of unsteady flow, pointing out the different approaches and methodologies involved as well as their relation to each other, taking classical (i.e. steady) vector field topology as our starting point. Particularly, we focus on Lagrangian methods, space–time domain approaches, local methods and stochastic and multifield approaches. Furthermore, we illustrate our review with practical examples for the different approaches.     

Distributed data clustering in sensor networks

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we address the distributed clustering problem, in which numerous interconnected nodes compute a clustering of their data, i.e., partition these values into multiple clusters, and describe each cluster concisely. We present a generic algorithm that solves the distributed clustering problem and may be implemented in various topologies, using different clustering types. For example, the generic algorithm can be instantiated to cluster values according to distance, targeting the same problem as the famous k-means clustering algorithm. However, the distance criterion is often not sufficient to provide good clustering results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for clustering decisions. Simulations show the robustness, speed and scalability of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, clustering criterion and cluster representation, in fully asynchronous settings.     

Good programming in transactional memory : Game theory meets multicore architecture

In a multicore transactional memory (TM) system, concurrent execution threads interact and interfere with each other through shared memory. The less interference a thread provokes the better for the system. However, as a programmer is primarily interested in optimizing her individual code’s performance rather than the system’s overall performance, she does not have a natural incentive to provoke as little interference as possible. Hence, a TM system must be designed compatible with good programming incentives (GPI), i.e., writing efficient code for the overall system should coincide with writing code that optimizes an individual thread’s performance. We show that with most contention managers (CM) proposed in the literature so far, TM systems are not GPI compatible. We provide a generic framework for CMs that base their decisions on priorities and explain how to modify Timestamp-like CMs so as to feature GPI compatibility. In general, however, priority-based conflict resolution policies are prone to be exploited by selfish programmers. In contrast, a simple non-priority-based manager that resolves conflicts at random is GPI compatible.     

How Internet software companies negotiate quality

In Internet speed development, innovation and time-to-market work against software quality. Browser giants like Microsoft Internet Explorer and Netscape Navigator are openly dealing with quality issues. The practices of application and smaller niche firms are less clear, but there are important trends     

Pancreatic xanthomatous neuropathy associated with hyperlipidemia: a cause of abdominal pain mimicking chronic pancreatitis

"Hyperlipidemic crisis" is a term used to describe episodic abdominal pain in patients with hyperlipidemia. The morphologic correlates of this phenomenon have not been investigated and the etiology of the disorder is uncertain. We report a unique histologic finding in the pancreas of a 34-year-old woman with a 17-year history of episodic abdominal pain, sometimes accompanied by hyperamylasemia. At the age of 18 years, grossly elevated cholesterol and triglyceride levels were documented and type V hyperlipidemia was diagnosed. At the age of 34 years, subtotal pancreatectomy was performed for intractable abdominal pain. Histologic examination identified an increased number of enlarged pancreatic nerves that were infiltrated by foamy macrophages and encircled by fibrous tissue; endoneurial infiltration with macrophages occasionally split nerves into individual fascicles. Otherwise, the pancreas had only minimal fibrosis, nesidioblastosis, mucinous metaplasia of some pancreatic ducts, and scattered small collections of chronic inflammatory cells, subtle features suggesting very mild chronic pancreatitis. We propose that this novel xanthomatous neuropathy mimicked pancreatitis and was one of the underlying pathophysiologic mechanisms of abdominal pain in this patient. Further studies are necessary to document the prevalence of this new entity in patients with hyperlipidemia and to correlate its occurrence with "hyperlipidemic crisis" in those individuals.     

Definition of inseparably fused ventricular myocardium in thoracopagus: fetal echocardiographic utility and pathologic refinement

Correlative echocardiographic and pathological findings in a thoracopagus with conjoined hearts are reported. One twin had tricuspid atresia with discordant atrioventricular connections and concordant ventriculoarterial connections. The morphologic right ventricle was hypoplastic and there was a large muscular ventricular septal defect. The other twin had hypoplasia of the mitral valve anulus and left ventricle with double-outlet right ventricle and pulmonary valve atresia. The tricuspid valve was severely insufficient in part because of a large orifice and redundant, elongated leaflets with abnormal chordal attachments. The left ventricles of these two twins shared a perforated common "free wall" with at least two large defects allowing mixing of the circulations at that level. Not all anatomic details were established conclusively by fetal echocardiography; however, sufficient diagnostic information was obtained to support a decision not to aggressively resuscitate these twins after elective cesarean delivery at 31 weeks' gestation.     

A modular computer vision system for picture segmentation and interpretation

The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long-term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task, can communicate with both of these memories. The information in the LTM remains unchanged during the analysis, while the STM is being continually updated and revised by the appropriate processors. The latter may be conceived of as being activated by certain data conditions in the STM, and using the information in both the LTM and STM to alter the status of the STM.     

LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE‐IMPOVERISHED SYSTEM

We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), infers safety constraints from a single expert demonstration trace and minimal background knowledge, and applies these constraints to the solutions proposed by multiple planning agents in an integrated and heterogeneous ensemble. The SCLC calculates how much to blame plan fragments (partial solutions) generated by the individual planning agents. This information is used when composing these fragments into a final overall plan. In particular, fragments whose safety violations exceed a threshold are rejected. This facilitates the generation of safe plans. We have integrated the SCLC within the Generalized Integrated Learning Architecture, which was designed for Defense Advanced Research Projects Agency (DARPA)’s Integrated Learning (IL) program. The main goal of the IL program is to promote the development and success of sophisticated systems that learn to solve challenging real‐world problems based on a simple demonstration by a human expert and exiguous domain knowledge. We present experimental results showing the advantages of the SCLC on two multiagent problem‐solving tasks that were benchmark applications in DARPA’s IL program.     

Constraint Lingo: towards high‐level constraint programming

Logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error prone. If the problem domain is structured enough, we can let the programmer express the problem in terms of more abstract, higher‐level constraints. A compiler can then convert the higher‐level program into a logic‐programming formalism. The compiler writer can experiment with alternative low‐level representations of the higher‐level constraints in order to achieve a high‐quality translation. The programmer can then take advantage of both a reduction in complexity and an improvement in runtime speed for all problems within the domain. We apply this analysis to the domain of tabular constraint‐satisfaction problems. Examples of such problems include logic puzzles solvable on a hatch grid and combinatorial problems such as graph coloring and independent sets. The proper abstractions for these problems are rows, columns, entries, and their interactions. We present a higher‐level language, Constraint Lingo, dedicated to problems in this domain. We also describe how we translate programs from Constraint Lingo into lower‐level logic formalisms such as the logic of propositional schemata. These translations require that we choose among competing lower‐level representations in order to produce efficient results. The overall effectiveness of our approach depends on the appropriateness of Constraint Lingo, our ability to translate Constraint Lingo programs into high‐quality representations in logic formalisms, and the efficiency with which logic engines can compute answer sets. We comment on our computational experience with these tools in solving both graph problems and logic puzzles. Copyright © 2004 John Wiley & Sons, Ltd.