393 research outputs found
On the thermodynamics of classical micro-canonical systems
We study the configurational probability distribution of a mono-atomic gas
with a finite number of particles N in the micro-canonical ensemble. We give
two arguments why the thermodynamic entropy of the configurational subsystem
involves Renyi's entropy function rather than that of Tsallis. The first
argument is that the temperature of the configurational subsystem is equal to
that of the kinetic subsystem. The second argument is that the instability of
the pendulum, which occurs for energies close to the rotation threshold, is
correctly reproduced
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
Cross-modality retrieval encompasses retrieval tasks where the fetched items
are of a different type than the search query, e.g., retrieving pictures
relevant to a given text query. The state-of-the-art approach to cross-modality
retrieval relies on learning a joint embedding space of the two modalities,
where items from either modality are retrieved using nearest-neighbor search.
In this work, we introduce a neural network layer based on Canonical
Correlation Analysis (CCA) that learns better embedding spaces by analytically
computing projections that maximize correlation. In contrast to previous
approaches, the CCA Layer (CCAL) allows us to combine existing objectives for
embedding space learning, such as pairwise ranking losses, with the optimal
projections of CCA. We show the effectiveness of our approach for
cross-modality retrieval on three different scenarios (text-to-image,
audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a
multi-view network using freely learned projections optimized by a pairwise
ranking loss, especially when little training data is available (the code for
all three methods is released at: https://github.com/CPJKU/cca_layer).Comment: Preliminary version of a paper published in the International Journal
of Multimedia Information Retrieva
EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use
In audio classification, differentiable auditory filterbanks with few
parameters cover the middle ground between hard-coded spectrograms and raw
audio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with
Per-Channel Energy Normalization (PCEN), has shown promising results, but is
computationally expensive. With inhomogeneous convolution kernel sizes and
strides, and by replacing PCEN with better parallelizable operations, we can
reach similar results more efficiently. In experiments on six audio
classification tasks, our frontend matches the accuracy of LEAF at 3% of the
cost, but both fail to consistently outperform a fixed mel filterbank. The
quest for learnable audio frontends is not solved.Comment: Accepted at EUSIPCO 2022. Code at
https://github.com/CPJKU/EfficientLEA
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Prediction of Sinorhizobium meliloti sRNA genes and experimental detection in strain 2011
Valverde C, Livny J, SchlĂĽter J-P, Reinkensmeier J, Becker A, Parisi G. Prediction of Sinorhizobium meliloti sRNA genes and experimental detection in strain 2011. BMC Genomics. 2008;9(1): 416.Background: Small non-coding RNAs (sRNAs) have emerged as ubiquitous regulatory elements in bacteria and other life domains. However, few sRNAs have been identified outside several well-studied species of gamma-proteobacteria and thus relatively little is known about the role of RNA-mediated regulation in most other bacterial genera. Here we have conducted a computational prediction of putative sRNA genes in intergenic regions (IgRs) of the symbiotic alpha-proteobacterium S. meliloti 1021 and experimentally confirmed the expression of dozens of these candidate loci in the closely related strain S. meliloti 2011. Results: Our first sRNA candidate compilation was based mainly on the output of the sRNAPredictHT algorithm. A thorough manual sequence analysis of the curated list rendered an initial set of 18 IgRs of interest, from which 14 candidates were detected in strain 2011 by Northern blot and/or microarray analysis. Interestingly, the intracellular transcript levels varied in response to various stress conditions. We developed an alternative computational method to more sensitively predict sRNA-encoding genes and score these predicted genes based on several features to allow identification of the strongest candidates. With this novel strategy, we predicted 60 chromosomal independent transcriptional units that, according to our annotation, represent strong candidates for sRNA-encoding genes, including most of the sRNAs experimentally verified in this work and in two other contemporary studies. Additionally, we predicted numerous candidate sRNA genes encoded in megaplasmids pSymA and pSymB. A significant proportion of the chromosomal- and megaplasmid- borne putative sRNA genes were validated by microarray analysis in strain 2011. Conclusion: Our data extend the number of experimentally detected S. meliloti sRNAs and significantly expand the list of putative sRNA-encoding IgRs in this and closely related alpha-proteobacteria. In addition, we have developed a computational method that proved useful to predict sRNA-encoding genes in S. meliloti. We anticipate that this predictive approach can be flexibly implemented in many other bacterial species
A non-standard finite-difference-method for a non-autonomous epidemiological model: analysis, parameter identification and applications
In this work, we propose a new non-standard finite-difference-method for the numerical solution of the time-continuous non-autonomous susceptible-infected-recovered model. For our time-discrete numerical solution algorithm, we prove preservation of non-negativity and show that the unique time-discrete solution converges linearly towards the time-continuous unique solution. In addition to that, we introduce a parameter identification algorithm for the susceptible-infected-recovered model. Finally, we provide two numerical examples to stress our theoretical findings
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