Threatened fishes of the world: Tanichthys albonubes Lin 1932 (Cyprinidae)

The white cloud mountain minnow Tanichthys albonubes Lin is an endemic species to southern China and the genus has two species, Tanichthys albonubes Lin and Tanichthys micagemmae Freyhof et Herder. The distribution range of T. albonubes Lin and T. micagemmae is very narrow and only found in the mountain brooks of Baiyunshan Mountain (White Cloud Mountain), Huaxian Country and the vicinity of Guangzhou in Guangdong Province and Halong, Quang Ninh Province, Vietnam respectively. The wild populations of this fish had already been on the verge of extinction when Shu-Yan Lin first discovered it in 1932 at the Baiyunshan Mountain. It was believed to be extinct in the wild because there were no reports of this fish in the wild since 1980. In September 2003, a small and isolated population of the fish was discovered in a mountain puddle in the north vicinity of Guangzhou. Additional studies are needed to determine the survival and propagation of the released fish. The protection of their natural habitat should be implemented.

Superradiance: exploiting quantum phase transition in the real world

Superradiance (SR), or cooperative spontaneous emission, has been predicted by R. Dicke before the invention of the laser. During the last few years one can see a renaissance of both experimental and theoretical studies of the superradiant phase transition in a variety of media, ranging from quantum dots and Bose condensates through to black holes. Until recently, despite of many years of research, SR has been considered as a phenomenon of pure scientific interest without obvious potential applications. However, recent investigations of the femtosecond SR emission generation from semiconductors have opened up some practical opportunities for the exploitation of this quantum optics phenomenon. Here we present a brief review of some features, advantages and potential applications of the SR generation from semiconductor laser structures

Four-dimensional Newton's constant in brane world with a sloping extra dimension

We show the four-dimensional Newton's constant obtained naturally from five-dimensional brane world with a tinily sloping extra dimension, which is independent of the bulk Weyl tensor. The corresponding universe is stiff fluid dominated when the slope of extra dimension is very small. Otherwise, the universe may be undergoing a self-acceleration at present epoch and have a decelerated phase in very recent past.

Angular momentum of a brane-world model

In this paper we discuss the properties of the general covariant angular momentum of a five-dimensional brane-world model. Through calculating the total angular momentum of this model, we are able to analyze the properties of the total angular momentum in the inflationary RS model. We show that the space-like components of the total angular momentum of the inflationary RS model are all zero while the others are non-zero, which agrees with the results from ordinary RS model.

Efficiency Dynamics on Two Coupled Small-World Networks

We investigate the effect of clusters in complex networks on efficiency dynamics by studying a simple efficiency model in two coupled small-world networks. It is shown that the critical network randomness corresponding to transition from a stagnant phase to a growing one decreases to zero as the connection strength of clusters increases. It is also shown for fixed randomness that the state of clusters transits from a stagnant phase to a growing one as the connection strength of clusters increases. This work can be useful for understanding the critical transition appearing in many dynamic processes on the cluster networks.

Rapid world wide depletion of predatory fish communities

Serious concerns have been raised about the ecological effects of industrialized fishing1, 2, 3, spurring a United Nations resolution on restoring fisheries and marine ecosystems to healthy levels4. However, a prerequisite for restoration is a general understanding of the composition and abundance of unexploited fish communities, relative to contemporary ones. We constructed trajectories of community biomass and composition of large predatory fishes in four continental shelf and nine oceanic systems, using all available data from the beginning of exploitation. Industrialized fisheries typically reduced community biomass by 80% within 15 years of exploitation. Compensatory increases in fast-growing species were observed, but often reversed within a decade. Using a meta-analytic approach, we estimate that large predatory fish biomass today is only about 10% of pre-industrial levels. We conclude that declines of large predators in coastal regions5 have extended throughout the global ocean, with potentially serious consequences for ecosystems5, 6, 7. Our analysis suggests that management based on recent data alone may be misleading, and provides minimum estimates for unexploited communities, which could serve as the ‘missing baseline’8 needed for future restoration efforts.

Recognition of Surface Reflectance Properties from a Single Image under Unknown Real-World Illumination

This paper describes a machine vision system that classifies reflectance properties of surfaces such as metal, plastic, or paper, under unknown real-world illumination. We demonstrate performance of our algorithm for surfaces of arbitrary geometry. Reflectance estimation under arbitrary omnidirectional illumination proves highly underconstrained. Our reflectance estimation algorithm succeeds by learning relationships between surface reflectance and certain statistics computed from an observed image, which depend on statistical regularities in the spatial structure of real-world illumination. Although the algorithm assumes known geometry, its statistical nature makes it robust to inaccurate geometry estimates.

Learning World Models in Environments with Manifest Causal Structure

This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.

A System for Representing and Using Real-World Knowledge

This report describes a knowledge-base system in which the information is stored in a network of small parallel processing elements ??de and link units ??ich are controlled by an external serial computer. This network is similar to the semantic network system of Quillian, but is much more tightly controlled. Such a network can perform certain critical deductions and searches very quickly; it avoids many of the problems of current systems, which must use complex heuristics to limit and guided their searches. It is argued (with examples) that the key operation in a knowledge-base system is the intersection of large explicit and semi-explicit sets. The parallel network system does this in a small, essentially constant number of cycles; a serial machine takes time proportional to the size of the sets, except in special cases.