Luciana Carota - Academia.edu (original) (raw)

Papers by Luciana Carota

Onco-hematological studies are increasingly adopting statistical mixture models to support the ad... more Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genetically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In particular, Dirichlet processes have become the preferred method to approach the fit of mixture models. Usually, the multinomial distribution is at the core of such models. However, despite their advanced statistical formalism, these processes are not to be considered black box techniques and a better understanding of their working mechanisms enables to improve their employment and explainability. Focused on genomic data in Acute Myeloid Leukemia, this work unfolds the driving factors and rationale of the Hierarchical Dirichlet Mixture Models of multinomials on binary data. In addition, we introduce a novel approach to perform accurate...

ABSTRACT Electronic neuromorphic devices with on-chip, on-line learning should be able to modify ... more ABSTRACT Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic pla...

Arxiv preprint physics/0701067, 2007

(a) , Lab. Naz. di Frascati (b) ,Sez. di Torino (c) , Sez. di Roma1 (d) , CNAFBologna (e) , Sez. ... more (a) , Lab. Naz. di Frascati (b) ,Sez. di Torino (c) , Sez. di Roma1 (d) , CNAFBologna (e) , Sez. di Bari (f) , Sez. di Roma2 (g) , Sez. di Milano (h) , Italy

Neurocomputing, 2012

... Project MUNES. I'm grateful to the chip designers (Physics Dept. of “Tor Vergata” Univer... more ... Project MUNES. I'm grateful to the chip designers (Physics Dept. of “Tor Vergata” University, Rome) and to P. Del Giudice, M. Mattia and V. Dante for their valuable suggestions. References. [1] C. Mead, Analog VLSI and Neura.

Journal of Atmospheric and Solar-Terrestrial Physics, 2005

In the last decades, a possible influence of electromagnetic fields of seismic origin in the iono... more In the last decades, a possible influence of electromagnetic fields of seismic origin in the ionosphere–magnetosphere transition region has been reported in the literature. In recent years, a few space experiments also revealed anomalous bursts of charged particles precipitating from the lower boundary of the inner radiation belt. They were thought to be caused by low-frequency seismo-electromagnetic emissions. A recent

The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end... more The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end in 2009, was initiated with the aim of setting up an advanced bioinformatics and computational biology laboratory, focusing on basic and applied research in modern biology and biotechnologies. One of the 2 goals of this project has been the development of a Grid Problem Solving Environment, built on top of EGEE, DEISA and SPACI infrastructures, to allow the submission and monitoring of jobs mapped to complex experiments in bioinformatics. In this work we describe the architecture of this environment and describe several case studies and related results which have been obtained using it.

The four High Energy Physics (HEP) detectors at the Large Hadron Collider (LHC) at the European O... more The four High Energy Physics (HEP) detectors at the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) are among the most important experiments where the National Institute of Nuclear Physics (INFN) is being actively involved. A Grid infrastructure of the World LHC Computing Grid (WLCG) has been developed by the HEP community leveraging on broader initiatives (e.g. EGEE in Europe, OSG in northen America) as a framework to exchange and maintain data storage and provide computing infrastructure for the entire LHC community. INFN-CNAF in Bologna hosts the Italian Tier-1 site, which represents the biggest italian center in the WLCG distributed computing. In the first part of this paper we will describe on the building of the Italian Tier-1 to cope with the WLCG computing requirements focusing on some peculiarities; in the second part we will analyze the INFN-CNAF contribution for the developement of the grid middleware, stressing in particular the characteristics of the Virtual Organization Membership Service (VOMS), the de facto standard for authorization on a grid, and StoRM, an implementation of the Storage Resource Manager (SRM) specifications for POSIX file systems. In particular StoRM is used at INFN-CNAF in conjunction with General Parallel File System (GPFS) and we are also testing an integration with Tivoli Storage Manager (TSM) to realize a complete Hierarchical Storage Management (HSM).

Neurocomputing, 2004

Selective attention is a biological mechanism to process salient subregions of the sensory input ... more Selective attention is a biological mechanism to process salient subregions of the sensory input space, while suppressing non-salient inputs. We present a hardware selective attention system, implemented using a neuromorphic VLSI chip interfaced to a workstation, via a custom PCI board and based on an address event (spike based) representation of signals. The chip selects salient inputs and sequentially shifts from one salient input to the other. The PCI board acts as an interface between the chip and an algorithm that generates saliency maps. We present experimental data showing the system's response to saliency maps generated from natural scenes.

Selective attention is a biological mechanism to process salient subregions of the sensory input ... more Selective attention is a biological mechanism to process salient subregions of the sensory input space, while suppressing non-salient inputs. We present a hardware selective attention system, implemented using a neuromorphic VLSI chip interfaced to a workstation, via a custom PCI board and based on an address event (spike based) representation of signals. The chip selects salient inputs and sequentially shifts from one salient input to the other. The PCI board acts as an interface between the chip and an algorithm that generates saliency maps. We present experimental data showing the system's response to saliency maps generated from natural scenes.

IEEE Transactions on Neural Networks, 2003

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly t... more Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre-and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network. The proposed implementation requires only 69 83 m 2 for the neuron and 68 47 m 2 for the synapse (using a 0.6 m, three metals, CMOS technology) and, hence, it is particularly suitable for the integration of a large number of plastic synapses on a single chip.

This work handles the optimization of the grid computing performances for a data-intensive and hi... more This work handles the optimization of the grid computing performances for a data-intensive and high ”throughput” comparison of protein sequences. We use the word ”throughput” from the telecommunication science to mean the amount of concurrent independent jobs in grid. All the proteins of 355 completely sequenced prokaryotic organisms were compared to find common traits of prokaryotic life, producing in parallel tens of Gigabytes of information to store, duplicate, check and analyze. For supporting a large amount of concurrent runs with data access on shared storage devices and a manageable data format, the output information was stored in many flat files according to a semantic logical/physical directory structure. As many concurrent runs could cause reading bottleneck on the same storage device, we propose methods to optimize the grid computing based on the balance between wide data access and emergence of reading bottlenecks. The proposed analytical approach has the following advantages: not only it optimizes the duration of the overall task, but also checks if the estimated duration is compliant with the scientific requirements and if the related grid computing is really advantageous compared to an execution on a local farm.

Journal of Proteome Research, 2009

Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availabili... more Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availability of complete genomes and proteomes, protein annotation has recently taken invaluable advantage from cross-genome comparisons. In this work, we describe a new non hierarchical clustering procedure characterized by a stringent metric which ensures a reliable transfer of function between related proteins even in the case of multidomain and distantly related proteins. The method takes advantage of the comparative analysis of 599 completely sequenced genomes, both from prokaryotes and eukaryotes, and of a GO and PDB/SCOP mapping over the clusters. A statistical validation of our method demonstrates that our clustering technique captures the essential information shared between homologous and distantly related protein sequences. By this, uncharacterized proteins can be safely annotated by inheriting the annotation of the cluster. We validate our method by blindly annotating other 201 genomes and finally we develop BAR (the Bologna Annotation Resource), a prediction server for protein functional annotation based on a total of 800 genomes (publicly available at http://microserf.biocomp.unibo.it/bar/).

Onco-hematological studies are increasingly adopting statistical mixture models to support the ad... more Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genetically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In particular, Dirichlet processes have become the preferred method to approach the fit of mixture models. Usually, the multinomial distribution is at the core of such models. However, despite their advanced statistical formalism, these processes are not to be considered black box techniques and a better understanding of their working mechanisms enables to improve their employment and explainability. Focused on genomic data in Acute Myeloid Leukemia, this work unfolds the driving factors and rationale of the Hierarchical Dirichlet Mixture Models of multinomials on binary data. In addition, we introduce a novel approach to perform accurate...

ABSTRACT Electronic neuromorphic devices with on-chip, on-line learning should be able to modify ... more ABSTRACT Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic pla...

Arxiv preprint physics/0701067, 2007

(a) , Lab. Naz. di Frascati (b) ,Sez. di Torino (c) , Sez. di Roma1 (d) , CNAFBologna (e) , Sez. ... more (a) , Lab. Naz. di Frascati (b) ,Sez. di Torino (c) , Sez. di Roma1 (d) , CNAFBologna (e) , Sez. di Bari (f) , Sez. di Roma2 (g) , Sez. di Milano (h) , Italy

Neurocomputing, 2012

... Project MUNES. I'm grateful to the chip designers (Physics Dept. of “Tor Vergata” Univer... more ... Project MUNES. I'm grateful to the chip designers (Physics Dept. of “Tor Vergata” University, Rome) and to P. Del Giudice, M. Mattia and V. Dante for their valuable suggestions. References. [1] C. Mead, Analog VLSI and Neura.

Journal of Atmospheric and Solar-Terrestrial Physics, 2005

In the last decades, a possible influence of electromagnetic fields of seismic origin in the iono... more In the last decades, a possible influence of electromagnetic fields of seismic origin in the ionosphere–magnetosphere transition region has been reported in the literature. In recent years, a few space experiments also revealed anomalous bursts of charged particles precipitating from the lower boundary of the inner radiation belt. They were thought to be caused by low-frequency seismo-electromagnetic emissions. A recent

The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end... more The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end in 2009, was initiated with the aim of setting up an advanced bioinformatics and computational biology laboratory, focusing on basic and applied research in modern biology and biotechnologies. One of the 2 goals of this project has been the development of a Grid Problem Solving Environment, built on top of EGEE, DEISA and SPACI infrastructures, to allow the submission and monitoring of jobs mapped to complex experiments in bioinformatics. In this work we describe the architecture of this environment and describe several case studies and related results which have been obtained using it.

The four High Energy Physics (HEP) detectors at the Large Hadron Collider (LHC) at the European O... more The four High Energy Physics (HEP) detectors at the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) are among the most important experiments where the National Institute of Nuclear Physics (INFN) is being actively involved. A Grid infrastructure of the World LHC Computing Grid (WLCG) has been developed by the HEP community leveraging on broader initiatives (e.g. EGEE in Europe, OSG in northen America) as a framework to exchange and maintain data storage and provide computing infrastructure for the entire LHC community. INFN-CNAF in Bologna hosts the Italian Tier-1 site, which represents the biggest italian center in the WLCG distributed computing. In the first part of this paper we will describe on the building of the Italian Tier-1 to cope with the WLCG computing requirements focusing on some peculiarities; in the second part we will analyze the INFN-CNAF contribution for the developement of the grid middleware, stressing in particular the characteristics of the Virtual Organization Membership Service (VOMS), the de facto standard for authorization on a grid, and StoRM, an implementation of the Storage Resource Manager (SRM) specifications for POSIX file systems. In particular StoRM is used at INFN-CNAF in conjunction with General Parallel File System (GPFS) and we are also testing an integration with Tivoli Storage Manager (TSM) to realize a complete Hierarchical Storage Management (HSM).

Neurocomputing, 2004

Selective attention is a biological mechanism to process salient subregions of the sensory input ... more Selective attention is a biological mechanism to process salient subregions of the sensory input space, while suppressing non-salient inputs. We present a hardware selective attention system, implemented using a neuromorphic VLSI chip interfaced to a workstation, via a custom PCI board and based on an address event (spike based) representation of signals. The chip selects salient inputs and sequentially shifts from one salient input to the other. The PCI board acts as an interface between the chip and an algorithm that generates saliency maps. We present experimental data showing the system's response to saliency maps generated from natural scenes.

Selective attention is a biological mechanism to process salient subregions of the sensory input ... more Selective attention is a biological mechanism to process salient subregions of the sensory input space, while suppressing non-salient inputs. We present a hardware selective attention system, implemented using a neuromorphic VLSI chip interfaced to a workstation, via a custom PCI board and based on an address event (spike based) representation of signals. The chip selects salient inputs and sequentially shifts from one salient input to the other. The PCI board acts as an interface between the chip and an algorithm that generates saliency maps. We present experimental data showing the system's response to saliency maps generated from natural scenes.

IEEE Transactions on Neural Networks, 2003

Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly t... more Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre-and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in the electronic network. The proposed implementation requires only 69 83 m 2 for the neuron and 68 47 m 2 for the synapse (using a 0.6 m, three metals, CMOS technology) and, hence, it is particularly suitable for the integration of a large number of plastic synapses on a single chip.

This work handles the optimization of the grid computing performances for a data-intensive and hi... more This work handles the optimization of the grid computing performances for a data-intensive and high ”throughput” comparison of protein sequences. We use the word ”throughput” from the telecommunication science to mean the amount of concurrent independent jobs in grid. All the proteins of 355 completely sequenced prokaryotic organisms were compared to find common traits of prokaryotic life, producing in parallel tens of Gigabytes of information to store, duplicate, check and analyze. For supporting a large amount of concurrent runs with data access on shared storage devices and a manageable data format, the output information was stored in many flat files according to a semantic logical/physical directory structure. As many concurrent runs could cause reading bottleneck on the same storage device, we propose methods to optimize the grid computing based on the balance between wide data access and emergence of reading bottlenecks. The proposed analytical approach has the following advantages: not only it optimizes the duration of the overall task, but also checks if the estimated duration is compliant with the scientific requirements and if the related grid computing is really advantageous compared to an execution on a local farm.

Journal of Proteome Research, 2009

Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availabili... more Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availability of complete genomes and proteomes, protein annotation has recently taken invaluable advantage from cross-genome comparisons. In this work, we describe a new non hierarchical clustering procedure characterized by a stringent metric which ensures a reliable transfer of function between related proteins even in the case of multidomain and distantly related proteins. The method takes advantage of the comparative analysis of 599 completely sequenced genomes, both from prokaryotes and eukaryotes, and of a GO and PDB/SCOP mapping over the clusters. A statistical validation of our method demonstrates that our clustering technique captures the essential information shared between homologous and distantly related protein sequences. By this, uncharacterized proteins can be safely annotated by inheriting the annotation of the cluster. We validate our method by blindly annotating other 201 genomes and finally we develop BAR (the Bologna Annotation Resource), a prediction server for protein functional annotation based on a total of 800 genomes (publicly available at http://microserf.biocomp.unibo.it/bar/).