Formal explanation of grammar rules
Practice of common grammatical patterns
Providing opportunities for Ss to use English in realistic situations
A grammar lesson consists of 4 parts
2. Focused Practice
3. Communicative Practice
4. Teacher feedback & correction
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: The self-organizing fractal theory as a universal discovery method: the phenomenon of life....
Advanced Computer-Assisted Techniques in Drug Discovery
edited by Han van de Waterbeemd
.Methods and Principles in Medicinal Chemistry
Edited by R. Mannhold P. Krogsgaard-Larsen H. Timmerman
Volume 1 Hugo Kubinyi, QSAR: Hansch Analysis and Related Approaches Volume 2 Han van de Waterbeemd (ed.), Chemometric Methods in Molecular Design Volume 3
Han van de Waterbeemd (ed.), Advanced Computer- Assisted Techniques in Drug Discovery
.Methods and Principles in Medicinal Chemistry
edited by R. Mannhold, P. Krogsgaard-Larsen, H.
Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. ...
Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and reason about textual input. This requires knowledge about the domain structure (such as entities, classes, and actions) in order to do inference. We present a method to infer this implicit knowledge from unlabeled text. Unlike previous approaches, we use automatically extracted classes with a probability distribution over entities to allow for context-sensitive labeling.
We present a web mining method for discovering and enhancing relationships in which a speciﬁed concept (word class) participates. We discover a whole range of relationships focused on the given concept, rather than generic known relationships as in most previous work. Our method is based on clustering patterns that contain concept words and other words related to them. We evaluate the method on three different rich concepts and ﬁnd that in each case the method generates a broad variety of relationships with good precision.
Effectively identifying events in unstructured text is a very difﬁcult task. This is largely due to the fact that an individual event can be expressed by several sentences. In this paper, we investigate the use of clustering methods for the task of grouping the text spans in a news article that refer to the same event. The key idea is to cluster the sentences, using a novel distance metric that exploits regularities in the sequential structure of events within a document.
Generative methods, topic discovery in images, application of pLSA action recognition, multiple actions,... As the main contents of the lecture "Generative learning methods for bags of features". Each of your content and references for additional lectures will serve the needs of learning and research.
To discover relation types from text, most methods cluster shallow or syntactic patterns of relation mentions, but consider only one possible sense per pattern. In practice this assumption is often violated. In this paper we overcome this issue by inducing clusters of pattern senses from feature representations of patterns.
We present a novel method for record extraction from social streams such as Twitter. Unlike typical extraction setups, these environments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual messages may not express the full relation to be uncovered, as is often assumed in extraction tasks.
We present a novel approach for discovering word categories, sets of words sharing a signiﬁcant aspect of their meaning. We utilize meta-patterns of highfrequency words and content words in order to discover pattern candidates. Symmetric patterns are then identiﬁed using graph-based measures, and word categories are created based on graph clique sets. Our method is the ﬁrst pattern-based method that requires no corpus annotation or manually provided seed patterns or words.
This paper presents a method for unsupervised discovery of semantic patterns. Semantic patterns are useful for a variety of text understanding tasks, in particular for locating events in text for information extraction. The method builds upon previously described approaches to iterative unsupervised pattern acquisition. One common characteristic of prior approaches is that the output of the algorithm is a continuous stream of patterns, with gradually degrading precision.
On the other hand, the construction of a comprehensive morphological analyzer for a language based on linguistic theory requires a considerable amount of work by experts. This is both slow and expensive and therefore not applicable to all languages. Consequently, it is important to develop methods that are able to discover and induce morphology for a language based on unsupervised analysis of large amounts of data.
MTMF combines the best parts of the Linear Spectral Mixing model and the statistical
Matched Filter model while avoiding the drawbacks of each parent method (Boardman,
1998). It is a useful Matched Filter method without knowing all the possible endmembers in
a landscape especially in case of subtle, sub-pixel occurrences. Firstly, pixel spectra and
endmember spectra require a minimum noise fraction (MNF) (Green et al., 1988, Boardman,
1993) transformation. MNF reduces and separates an image into its most dimensional and
Molecules, small structures composed of atoms, are essential substances for lives.
However, we didn’t have the clear answer to the following questions until the 1920s:
why molecules can exist in stable as rigid networks between atoms, and why
molecules can change into different types of molecules. The most important event for
solving the puzzles is the discovery of the quantum mechanics. Quantum mechanics is
the theory for small particles such as electrons and nuclei, and was applied to
hydrogen molecule by Heitler and London at 1927.
Great efﬁciencies have been achieved in the drug discovery process as a result of technological advances in target identiﬁcation, high-throughput screening, high-throughput organic synthesis, just-in-time in vitro ADME (absorption, distribution, metabolism, and excretion), and early pharmacokinetic screening of drug leads. These advances, spanning target selection all the way through to clinical candidate selection, have placed greater and greater demands on the analytical community to develop robust high-throughput methods.
Chemistry plays a key role in conquering diseases, solving energy problems, addressing environmental problems, providing the discoveries that lead to new industries, and developing new materials and technologies for national defense and homeland security. However, the field is currently facing a crucial time of change and is struggling to position itself to meet the needs of the future as it expands beyond its traditional core toward areas related to biology, materials science, and nanotechnology....
The closing years of the 19th century and the start of the 20th century
witnessed the emergence of microbiology and immunology as discrete scientific
disciplines, and in the work of Roux and Yersin, perhaps the first benefits
of their synergy—immunotherapy against bacterial infection. As we advance
into the new millennium, microbiology and immunology again offer a conceptual
leap forward as antibody phage display gains increasing acceptance as
the definitive technology for monoclonal production and unleashes new opportunities
in immunotherapy, drug discovery, and functional genomics....
In the last two decades, the discovery of signaling roles of ROS demonstrated their
universal use in biological systems. The third section of this book, entitled “Reactive
Species as Signaling Molecules” contains the chapters covering clear ROS-based signaling
in yeasts and plants. It is not strange that many authors provide readers with the
information gained from yeasts. This is a very popular classic eukaryotic model system to
disclose molecular mechanisms of cellular responses to oxidative stress (Lushchak, 2010).
In the first chapter of this book section, M. A.
Although much of its discovery process is descriptive and qualitative, chemistry is fundamentally a
quantitative science. It serves a wide range of human needs, activities, and concerns. The mathematical sciences
provide the language for quantitative science, and this language is growing in many directions as computational
science in general continues its rapid expansion. A timely opportunity now exists to strengthen and increase the
beneficial impacts of chemistry by enhancing the interaction between chemistry and the mathematical sciences.