"Standard performance analysis tools like Activity Monitor and top(1) (and any third-party tools based on the same foundation) can’t tell you some key information about activity on your system, such as how much CPU consumption is caused by short-lived processes, or which processes are causing disk I/O. DTrace, however, can see (just about) everything.

In this post, I’ll cover the top ten Mac OS X DTrace scripts that I use for figuring out why laptops are slow or why applications are misbehaving. Most of these scripts are already installed, a few are from the new DTrace book."

"In evaluating prediction markets (and other crowd-prediction mechanisms), investigators have repeatedly observed a so-called "wisdom of crowds" effect, which roughly says that the average of participants performs much better than the average participant. The market price---an average or at least aggregate of traders' beliefs---offers a better estimate than most any individual trader's opinion. In this paper, we ask a stronger question: how does the market price compare to the best trader's belief, not just the average trader. We measure the market's worst-case log regret, a notion common in machine learning theory. To arrive at a meaningful answer, we need to assume something about how traders behave. We suppose that every trader optimizes according to the Kelly criteria, a strategy that provably maximizes the compound growth of wealth over an (infinite) sequence of market interactions. We show several consequences.…"

"The design of genetic networks with specific functions is one of the major goals of synthetic biology. However, constructing biological devices that work "as required" remains challenging, while the cost of uncovering flawed designs experimentally is large. To address this issue, we propose a fully automated framework that allows the correctness of synthetic gene networks to be formally verified in silico from rich, high level functional specifications.
Given a device, we automatically construct a mathematical model from experimental data characterizing the parts it is composed of. The specific model structure guarantees that all experimental observations are captured and allows us to construct finite abstractions through polyhedral operations. The correctness of the model with respect to temporal logic specifications can then be verified automatically using methods inspired by model checking.
Overall, our procedure is conservative but it can filter through a large number of potential device designs and select few that satisfy the specification to be implemented and tested further experimentally. Illustrative examples of the application of our methods to the design of simple synthetic gene networks are included."

"Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications."

"...We find that award-winning CEOs subsequently underperform, both relative to their prior performance and relative to a matched sample of non-winning CEOs. At the same time, they extract more compensation following the awards, both in absolute amounts and relative to other top executives in their firms. They also spend more time on public and private activities outside their companies, such as assuming board seats or writing books. The incidence of earnings management increases after winning awards. The effects are strongest in firms with weak corporate governance. Our results suggest that the ex post consequences of media-induced superstar status for shareholders are negative."

"It's been too long since I've sat down to benchmark CouchDB. I'm working on the High Performance CouchDB chapter in the book, so I needed some numbers."

"I want to focus on the metrics of the strategy trades. The performance statistics are below. I find them nothing less than stellar. The metrics that I found especially appealing are highlighted in green."

Clarification of oft-abused terminology