14,597 research outputs found
Aspects of Warped AdS/CFT Correspondence
In this paper we apply the thermodynamics method to investigate the
holographic pictures for the BTZ black hole, the spacelike and the null warped
black holes in three-dimensional topologically massive gravity (TMG) and new
massive gravity (NMG). Even though there are higher derivative terms in these
theories, the thermodynamics method is still effective. It gives consistent
results with the ones obtained by using asymptotical symmetry group (ASG)
analysis. In doing the ASG analysis we develop a brute-force realization of the
Barnich-Brandt-Compere formalism with Mathematica code, which also allows us to
calculate the masses and the angular momenta of the black holes. In particular,
we propose the warped AdS/CFT correspondence in the new massive
gravity, which states that quantum gravity in the warped spacetime could
holographically dual to a two-dimensional CFT with
c_R=c_L=\f{24}{Gm\b^2\sr{2(21-4\b^2)}}.Comment: 22 pages, references added, published version, link of Mathematica
code changed to https://s.yunio.com/Mtus0z or http://pan.baidu.com/s/1mToF
Time–Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition
The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time-frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexity. The BDHLDA method is finally extended to the GMM domain, using the simpler TFC features during re-estimation to provide significantly improved computation speed. Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches
Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling
Urban Building Energy Modeling (UBEM) is a critical tool to provide
quantitative analysis on building decarbonization, sustainability,
building-to-grid integration, and renewable energy applications on city,
regional, and national scales. Researchers usually use open data as inputs to
build and calibrate UBEM. However, open data are from thousands of sources
covering various perspectives of weather, building characteristics, etc.
Besides, a lack of semantic features of open data further increases the
engineering effort to process information to be directly used for UBEM as
inputs. In this paper, we first reviewed open data types used for UBEM and
developed a taxonomy to categorize open data. Based on that, we further
developed a semantic data schema for each open data category to maintain data
consistency and improve model automation for UBEM. In a case study, we use
three popular open data to show how they can be automatically processed based
on the proposed schematic data structure using large language models. The
accurate results generated by large language models indicate the
machine-readability and human-interpretability of the developed semantic data
schema
- …