Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, we elaborate over the well-known interpretability issue in echo-state networks (ESNs). The idea is to investigate the dynamics of reservoir neurons with time-series analysis techniques developed in complex systems research. Notably, we analyze time series of neuron activations with recurrence plots (RPs) and recurrence quantification analysis (RQA), which permit to visualize and characterize high-dimensional dynamical systems. We show that this approach is useful in a number of ways. First, the 2-D representation offered by RPs provides a visualization of the high-dimensional reservoir dynamics. Our results suggest that, if the network is stable, reservoir and input generate similar line patterns in the respective RPs. Conversely, as the ESN becomes unstable, the patterns in the RP of the reservoir change. As a second result, we show that an RQA measure, called Lmax, is highly correlated with the well-established maximal local Lyapunov exponent. This suggests that complexity measures based on RP diagonal lines distribution can quantify network stability. Finally, our analysis shows that all RQA measures fluctuate on the proximity of the so-called edge of stability, where an ESN typically achieves maximum computational capability. We leverage on this property to determine the edge of stability and show that our criterion is more accurate than two well-known counterparts, both based on the Jacobian matrix of the reservoir. Therefore, we claim that RPs and RQA-based analyses are valuable tools to design an ESN, given a specific problem

The deep kernelized autoencoder

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAutoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing kernel Hilbert space, hence capturing topological properties of input data. In this paper, we enhance the autoencoder's ability to learn effective data representations by aligning inner products between codes with respect to a kernel matrix. By doing so, the proposed kernelized autoencoder allows learning similarity-preserving embeddings of input data, where the notion of similarity is explicitly controlled by the user and encoded in a positive semi-definite kernel matrix. Experiments are performed for evaluating both reconstruction and kernel alignment performance in classification tasks and visualization of high-dimensional data. Additionally, we show that our method is capable to emulate kernel principal component analysis on a denoising task, obtaining competitive results at a much lower computational cost.Norwegian Research Council FRIPR

Learning representations of multivariate time series with missing data

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordLearning compressed representations of multivariate time series (MTS) facilitates data analysis in the presence of noise and redundant information, and for a large number of variates and time steps. However, classical dimensionality reduction approaches are designed for vectorial data and cannot deal explicitly with missing values. In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS. The proposed model can process inputs characterized by variable lengths and it is specifically designed to handle missing data. Our autoencoder learns fixed-length vectorial representations, whose pairwise similarities are aligned to a kernel function that operates in input space and that handles missing values. This allows to learn good representations, even in the presence of a significant amount of missing data. To show the effectiveness of the proposed approach, we evaluate the quality of the learned representations in several classification tasks, including those involving medical data, and we compare to other methods for dimensionality reduction. Successively, we design two frameworks based on the proposed architecture: one for imputing missing data and another for one-class classification. Finally, we analyze under what circumstances an autoencoder with recurrent layers can learn better compressed representations of MTS than feed-forward architectures.Norwegian Research Counci

Unary probabilistic and quantum automata on promise problems

We continue the systematic investigation of probabilistic and quantum finite automata (PFAs and QFAs) on promise problems by focusing on unary languages. We show that bounded-error QFAs are more powerful than PFAs. But, in contrary to the binary problems, the computational powers of Las-Vegas QFAs and bounded-error PFAs are equivalent to deterministic finite automata (DFAs). Lastly, we present a new family of unary promise problems with two parameters such that when fixing one parameter QFAs can be exponentially more succinct than PFAs and when fixing the other parameter PFAs can be exponentially more succinct than DFAs.Comment: Minor correction

Novel large-range mitochondrial dna deletions and fatal multisystemic disorder with prominent hepatopathy

Hepatic involvement in mitochondrial cytopathies rarely manifests in adulthood, but is a common feature in children. Multiple OXPHOS enzyme defects in children with liver involvement are often associated with dramatically reduced amounts of mtDNA. We investigated two novel large scale deletions in two infants with a multisystem disorder and prominent hepatopathy. Amount of mtDNA deletions and protein content were measured in different post-mortem tissues. The highest levels of deleted mtDNA were in liver, kidney, pancreas of both patients. Moreover, mtDNA deletions were detected in cultured skin fibroblasts in both patients and in blood of one during life. Biochemical analysis showed impairment of mainly complex I enzyme activity. Patients manifesting multisystem disorders in childhood may harbour rare mtDNA deletions in multiple tissues. For these patients, less invasive blood specimens or cultured fibroblasts can be used for molecular diagnosis. Our data further expand the array of deletions in the mitochondrial genomes in association with liver failure. Thus analysis of mtDNA should be considered in the diagnosis of childhood-onset hepatopathies

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525

In vivo tissue uptake of intravenously injected water soluble all-trans β-carotene used as a food colorant

Water soluble β-carotene (WS-BC) is a carotenoid form that has been developed as a food colorant. WS-BC is known to contain 10% of all-trans β-carotene (AT-BC). The aim of the present study was to investigate in vivo tissue uptake of AT-BC after the administration of WS-BC into rats. Seven-week-old male rats were administered 20 mg of WS-BC dissolved in saline by intravenous injection into the tail vein. At 0, 6, 24, 72, 120 and 168 hours (n = 7/time), blood was drawn and liver, lungs, adrenal glands, kidneys and testes were dissected. The levels of AT-BC in the plasma and dissected tissues were quantified with HPLC. After intravenous administration, AT-BC level in plasma first increased up to 6 h and returned to normal at 72 h. In the testes, the AT-BC level first increased up to 24 h and then did not decrease but was retained up to 168 h. In the other tissues, the level first increased up to 6 h and then decreased from 6 to 120 or 168 h but did not return to normal. The accumulation of WS-BC in testes but not in the other 5 tissues examined may suggest that AT-BC was excreted or metabolized in these tissues but not in testes. Although WS-BC is commonly used as a food colorant, its effects on body tissues are still not clarified. Results of the present study suggest that further investigations are required to elucidate effects of WS-BC on various body tissues

Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research

Assessment of the capacity to consent to treatment in patients admitted to acute medical wards

BACKGROUND: Assessment of capacity to consent to treatment is an important legal and ethical issue in daily medical practice. In this study we carefully evaluated the capacity to consent to treatment in patients admitted to an acute medical ward using an assessment by members of the medical team, the specific Silberfeld's score, the MMSE and an assessment by a senior psychiatrist. METHODS: Over a 3 month period, 195 consecutive patients of an internal medicine ward in a university hospital were included and their capacity to consent was evaluated within 72 hours of admission. RESULTS: Among the 195 patients, 38 were incapable of consenting to treatment (unconscious patients or severe cognitive impairment) and 14 were considered as incapable of consenting by the psychiatrist (prevalence of incapacity to consent of 26.7%). Agreement between the psychiatrist's evaluation and the Silberfeld questionnaire was poor (sensitivity 35.7%, specificity 91.6%). Experienced clinicians showed a higher agreement (sensitivity 57.1%, specificity 96.5%). A decision shared by residents, chief residents and nurses was the best predictor for agreement with the psychiatric assessment (sensitivity 78.6%, specificity 94.3%). CONCLUSION: Prevalence of incapacity to consent to treatment in patients admitted to an acute internal medicine ward is high. While the standardized Silberfeld questionnaire and the MMSE are not appropriate for the evaluation of the capacity to consent in this setting, an assessment by the multidisciplinary medical team concurs with the evaluation by a senior psychiatrist

CK2 Phosphorylation of Schistosoma mansoni HMGB1 Protein Regulates Its Cellular Traffic and Secretion but Not Its DNA Transactions

parasite resides in mesenteric veins where fecundated female worms lay hundred of eggs daily. Some of the egg antigens are trapped in the liver and induce a vigorous granulomatous response. High Mobility Group Box 1 (HMGB1), a nuclear factor, can also be secreted and act as a cytokine. Schistosome HMGB1 (SmHMGB1) is secreted by the eggs and stimulate the production of key cytokines involved in the pathology of schistosomiasis. Thus, understanding the mechanism of SmHMGB1 release becomes mandatory. Here, we addressed the question of how the nuclear SmHMGB1 can reach the extracellular space. eggs of infected animals and that SmHMGB1 that were localized in the periovular schistosomotic granuloma were phosphorylated.We showed that secretion of SmHMGB1 is regulated by phosphorylation. Moreover, our results suggest that egg-secreted SmHMGB1 may represent a new egg antigen. Therefore, the identification of drugs that specifically target phosphorylation of SmHMGB1 might block its secretion and interfere with the pathogenesis of schistosomiasis