12,465 research outputs found
Frustrated Heisenberg antiferromagnets: fluctuation induced first order vs deconfined quantum criticality
Recently it was argued that quantum phase transitions can be radically
different from classical phase transitions with as a highlight the 'deconfined
critical points' exhibiting fractionalization of quantum numbers due to Berry
phase effects. Such transitions are supposed to occur in frustrated
('-') quantum magnets. We have developed a novel renormalization
approach for such systems which is fully respecting the underlying lattice
structure. According to our findings, another profound phenomenon is around the
corner: a fluctuation induced (order-out-of-disorder) first order transition.
This has to occur for large spin and we conjecture that it is responsible for
the weakly first order behavior recently observed in numerical simulations for
frustrated systems.Comment: 7 pages, 3 Figures, submitted to EP
Nonlinearities and cyclical behavior: the role of chartists and fundamentalists
We develop a behavioral exchange rate model with chartists and fundamentalists to study cyclical behavior in foreign exchange markets. Within our model, the market impact of fundamentalists depends on the strength of their belief in fundamental analysis. Estimation of a STAR GARCH model shows that the more the exchange rate deviates from its fundamental value, the more fundamentalists leave the market. In contrast to previous findings, our paper indicates that due to the nonlinear presence of fundamentalists, market stability decreases with increasing misalignments. A stabilization policy such as central bank interventions may help to deflate bubbles
Learning semantic sentence representations from visually grounded language without lexical knowledge
Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matching text captions to produce visually grounded sentence
embeddings. Deep Neural Networks are trained to map the two modalities to a
common embedding space such that for an image the corresponding caption can be
retrieved and vice versa. We show that our model achieves results comparable to
the current state-of-the-art on two popular image-caption retrieval benchmark
data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the
resulting sentence embeddings using the data from the Semantic Textual
Similarity benchmark task and show that the multimodal embeddings correlate
well with human semantic similarity judgements. The system achieves
state-of-the-art results on several of these benchmarks, which shows that a
system trained solely on multimodal data, without assuming any word
representations, is able to capture sentence level semantics. Importantly, this
result shows that we do not need prior knowledge of lexical level semantics in
order to model sentence level semantics. These findings demonstrate the
importance of visual information in semantics
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