Magnetic phase diagram of doped CMR manganites

The magnetic phase diagram of the colossal magnetoresistance (CMR) manganites is determined based on the Hamiltonian incorporating the double-exchange (DE) interaction between degenerate Mn $e_g$ orbitals and the antiferromagnetic (AF) superexchange interaction between Mn $t_{2g}$ spins. We have employed the rigorous quantum mechanical formalism and obtained the finite temperature phase diagram which describes well the commonly observed features in CMR manganites. We have also shown that the CE-type AF structure cannot be stabilized at $x$=0.5 in this model.Comment: 2 pages, 1 figure; Transport and Thermal Properties of Advanced Materials(Aug. 2002; Hiroshima, Japan

High-Precision Extraction of Emerging Concepts from Scientific Literature

Identification of new concepts in scientific literature can help power faceted search, scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification cannot keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for scientific literature that achieves much higher precision than previous work. Our approach relies on a simple but novel intuition: each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15,000 extractions. To stimulate research in this area, we release our code and data (https://github.com/allenai/ForeCite).Comment: Accepted to SIGIR 202

On the Use of Sequences, Phase Changes, and HoneyBees For Designing Adaptive Distributed Systems

The invention of distributed protocols is an extremely challenging activity. Today however, few design paradigms are available for the creative task of designing scalable and reliable protocols for the Grid, peer to peer systems, etc. This paper first presents a *design methodology* to translate sequence equations, that are extensions of the form x{m+1}=f(xm,x{m-1}, ..., x{m-k}), into distributed protocols that are provably equivalent, i.e., exhibit the same equilibrium points and trajectories. These novel *sequence protocols* are decentralized, simple, scalable, and highly fault-tolerant. We then demonstrate how phase changes in sequence protocols can be used to detect certain global predicates in a decentralized manner. Two such new protocols called the *Multiplicative protocol* and the *Logistic protocol* are presented, rigorously analyzed, and experimentally studied. Finally, we present *HoneySort*, a novel sequence protocol for adaptive Grid computing. HoneySort is derived from sequence equations representing coordination among honeybees in nature. Through rigorous analysis and experiments with a real deployment on a 30-node PC cluster, we show that HoneySort outperforms well-known distributed sorting algorithms such as Quicksort and Insertion sort

Using Graphs For Topic Discovery

As large-scale digital text collections become abundant, the necessity of automatically summarizing text data by discovering topics and the evolution of topics in them is well-justified and there is surge of research interest in the task. We use graphs for topic discovery and topic evolution discovery by mining the statistical properties of graphs associated with the text data. Considering that an increasing number of text collections have some kind of networks associated with the data (text data in social network service, research paper collections, digital text with user browsing history), there is a great potential in using graphs for the task of text mining. Our work on topic and topic evolution discovery shows qualitatively different results from the existing approaches in that the discovered topics exhibit concreteness with a variety of size and time dynamics and in that the rich topology of topic evolution is captured in the result. We discover topics by mining the correlation between topic terms and the citation graph. This is done by developing a statistical measure, associated with terms, for the connectivity of a document graph. In topic evolution discovery, we capture the inherent topology of topic evolution in a corpus by discovering quantized units of evolutionary change in content and connecting them by summarizing the underlying document network. We note that topic words and nontopic words differ in their distributional properties and use this observation to discover topics via making a document network. We use the same observation to enhance the quality of topics obtained by Latent Dirichlet Allocation

Detecting Research Topics via the Correlation between Graphs and Texts

In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its utility for information navigation, trend analysis, and high-level description of data. We present a unique approach that uses the correlation between the distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term. This tight coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models. We develop a topic score measure for each term, using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity. Our approach is based on the intuition that if a term is relevant to a topic, the documents containing the term have denser connectivity than a random selection of documents. We extend our algorithm to detect a topic represented by a set of terms, using the intuition that if the co-occurrence of terms represents a new topic, the citation pattern should exhibit the synergistic effect. We test our algorithm on two electronic research literature collections,arXiv and Citeseer.Our evaluation shows that the approach is effective and reveals some novel aspects of topic detection

Dose–Response Effects of Bamboo Leaves on Rumen Methane Production, Fermentation Characteristics, and Microbial Abundance In Vitro

Ruminants produce large amounts of methane as part of their normal digestive processes. Recently, feed additives were shown to inhibit the microorganisms that produce methane in the rumen, consequently reducing methane emissions. The objective of this study was to evaluate the dose–response effect of Phyllostachys nigra var. henonis (PHN) and Sasa borealis supplementation on in vitro rumen fermentation, methane, and carbon dioxide production, and the microbial population. An in vitro batch culture system was used, incubated without bamboo leaves (control) or with bamboo leaves (0.3, 0.6, and 0.9 g/L). After 48 h, total gas, methane, and carbon dioxide production decreased linearly with an increasing dose of bamboo leaves supplementation. The total volatile fatty acid, acetate, and acetate-to-propionate ratio were affected quadratically with increasing doses of bamboo leaves supplementation. In addition, propionate decreased linearly. Butyrate was increased linearly with increasing doses of PHN supplementation. The absolute values of total bacteria and methanogenic archaea decreased linearly and quadratically with an increasing dose of PHN treatment after 48 h. These results show that bamboo leaves supplementation can reduce methane production by directly affecting methanogenic archaea, depressing the metabolism of methanogenic microbes, or transforming the composition of the methanogenic community. These results need to be validated using in vivo feeding trials before implementation

Effects of Olive (Olea europaea L.) Leaves with Antioxidant and Antimicrobial Activities on In Vitro Ruminal Fermentation and Methane Emission

We evaluated whether olive leaves (OLs) are effective as feed additives and supplements for ruminants and the potential methane reduction effects during in vitro fermentation. Two Hanwoo cows (460 ± 20 kg) equipped with cannula were fed Timothy hay and corn-based feed 3% of the body weight at a ratio of 6:4 (8:30 a.m. and 5:00 p.m.). Ruminal fluid from the cows was collected and mixed before morning feeding. In vitro batch fermentation was monitored after 12 and 24 h of incubation at 39 °C, and OLs were used as supplements to achieve the concentration of 5% in the basal diet. At 12 h of fermentation, methane production decreased in the 5% OLs group compared to that in the control group, but not at 24 h. The proportion of cellulose-degrading bacteria, Fibrobacter succinogenes, Ruminococcus albus, and Ruminococcus flavefaciens, tended to increase in the 5% OLs group at 12 h. The amount of ammonia produced was the same as the polymerase chain reaction result for Prevotella ruminicola. At 12 h, the proportion of Prevotella ruminicola was significantly higher in the 5% OLs group. OLs may be used incorporated with protein byproducts or other methane-reducing agents in animal feed