In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic pieces, the focus here is on intricate music featuring a diverse array of instruments, voices, and effects, rich in harmonics and timbre. This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data, employing an end-to-end training approach directly on raw data without the need for manual pre-processing and channel selection. We train our models on the public NMED- T dataset and perform quantitative evaluation proposing neural embedding-based metrics. Our work contributes to the ongoing research in neural decoding and brain-computer interfaces, offering insights into the feasibility of using EEG data for complex auditory information reconstruction.

BACKGROUND: Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. RESULTS: This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations. CONCLUSIONS: eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.

Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries. We then consider possible risks from those technologies, including intentional abuse, autonomous weapons, cognitive enhancement by brain-computer interfaces, insidious effects of social media, inequity, and enfeeblement. We also discuss practical ways to bring ethical principles into practice. One proposal is to stop giving explicit goals to AI agents and to enable them to keep learning human preferences. Another is to learn from democratic mechanisms that evolved in human society to avoid over-consolidation of power. Finally, we emphasize the importance of open discussions not only by experts, but also including a diverse array of lay opinions.

BACKGROUND: Existing de novo software platforms have largely overlooked a valuable resource, the expertise of the intended biologist users. Typical data representations such as long gene lists, or highly dense and overlapping transcription factor networks often hinder biologists from relating these results to their expertise. RESULTS: VISIONET, a streamlined visualisation tool built from experimental needs, enables biologists to transform large and dense overlapping transcription factor networks into sparse human-readable graphs via numerically filtering. The VISIONET interface allows users without a computing background to interactively explore and filter their data, and empowers them to apply their specialist knowledge on far more complex and substantial data sets than is currently possible. Applying VISIONET to the Tbx20-Gata4 transcription factor network led to the discovery and validation of Aldh1a2, an essential developmental gene associated with various important cardiac disorders, as a healthy adult cardiac fibroblast gene co-regulated by cardiogenic transcription factors Gata4 and Tbx20. CONCLUSIONS: We demonstrate with experimental validations the utility of VISIONET for expertise-driven gene discovery that opens new experimental directions that would not otherwise have been identified.

Flint is a simulator that numerically integrates heterogeneous biophysical models described by a large set of ordinary differential equations. It uses an internal bytecode representation of simulation-related expressions to handle various biophysical models built for general purposes. We propose two acceleration methods for Flint using a graphics processing unit (GPU). The first method interprets multiple bytecodes in parallel on the GPU. It automatically parallelizes the simulation using a level scheduling algorithm. We implement an interpreter of the Flint bytecode that is suited for running on the GPU, which reduces both the number of memory accesses and divergent branches to achieve higher performance. The second method translates a model into a source code for both the CPU and the GPU through the internal bytecode, which speeds up the compilation of the generated source codes, because the code size is diminished because of bytecode unification. With large models such that tens of thousands or more expressions can be evaluated simultaneously, the translated code running on the GPU achieves computational performance of up to 2.7 higher than that running on a CPU. Otherwise, with small models, the CPU is faster than the GPU. Therefore, the translated code dynamically determines on which to run either the CPU or the GPU by profiling initial few iterations of the simulation.

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

MOTIVATION: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. AVAILABILITY AND IMPLEMENTATION: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. CONTACT: [email protected].

MOTIVATION: Protein-protein interaction (PPI) databases are widely used tools to study cellular pathways and networks; however, there are several databases available that still do not account for cell type-specific differences. Here, we evaluated the characteristics of six interaction databases, incorporated tissue-specific gene expression information and finally, investigated if the most popular proteins of scientific literature are involved in good quality interactions. RESULTS: We found that the evaluated databases are comparable in terms of node connectivity (i.e. proteins with few interaction partners also have few interaction partners in other databases), but may differ in the identity of interaction partners. We also observed that the incorporation of tissue-specific expression information significantly altered the interaction landscape and finally, we demonstrated that many of the most intensively studied proteins are engaged in interactions associated with low confidence scores. In summary, interaction databases are valuable research tools but may lead to different predictions on interactions or pathways. The accuracy of predictions can be improved by incorporating datasets on organ- and cell type-specific gene expression, and by obtaining additional interaction evidence for the most 'popular' proteins. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: BioPAX is a standard language for representing and exchanging models of biological processes at the molecular and cellular levels. It is widely used by different pathway databases and genomics data analysis software. Currently, the primary source of BioPAX data is direct exports from the curated pathway databases. It is still uncommon for wet-lab biologists to share and exchange pathway knowledge using BioPAX. Instead, pathways are usually represented as informal diagrams in the literature. In order to encourage formal representation of pathways, we describe a software package that allows users to create pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing tool and save the pathway data in BioPAX Level 3 format. AVAILABILITY: The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations.

SUMMARY: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. AVAILABILITY AND IMPLEMENTATION: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. CONTACT: [email protected]

Protein-protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers.

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.

Stochastic simulation of biochemical reaction networks are widely focused by life scientists to represent stochastic behaviors in cellular processes. Stochastic algorithm has loop-and thread-level parallelism, and it is suitable for running on application specific hardware to achieve high performance with low cost. We have implemented and evaluated the FPGA-based stochastic simulator according to theoretical research of the algorithm. This paper introduces an improved architecture for accelerating a stochastic simulation algorithm called the Next Reaction Method. This new architecture has scalability to various size of FPGA. As the result with a middle-range FPGA, 5.38 times higher throughput was obtained compared to software running on a Core 2 Quad Q6600 2.40GHz.

Understanding of the logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. To facilitate this research topic, we have developed a modeling/simulating tool called CellDesigner. CellDesigner primarily has capabilities to visualize, model, and simulate gene-regulatory and biochemical networks. Two major characteristics embedded in CellDesigner boost its usability to create/import/export models: 1) solidly defined and comprehensive graphical representation (systems biology graphical notation) of network models and 2) systems biology markup language (SBML) as a model-describing basis, which function as intertool media to import/export SBML-based models. In addition, since its initial release in 2004, we have extended various capabilities of CellDesigner. For example, we integrated other systems biology workbench enabled simulation/analysis software packages. CellDesigner also supports simulation and parameter search, supported by integration with SBML ODE Solver, enabling users to simulate through our sophisticated graphical user interface. Users can also browse and modify existing models by referring to existing databases directly through CellDesigner. Those extended functions empower CellDesigner as not only a modeling/simulating tool but also an integrated analysis suite. CellDesigner is implemented in Java and thus supports various platforms (i.e., Windows, Linux, and MacOS X). CellDesigner is freely available via our Web site.

In order to simulate large scale biological models with a reconfigurable FPGA-based biochemical simulator system, reduction of required resources are essential. This paper proposes a method which combines common terms in rate law functions appeared in biochemical models and generates a shared hardware module used for numerical integration. In this approach, two functions are combined in a tree structure level, followed by pipeline scheduling and arithmetic module binding. The evaluation result reveals that this approach reduces hardware resources by 31.4% on average at the cost of 14.4% throughput degradation.

This paper introduces a scalable FPGA implementation of a stochastic simulation algorithm (SSA) called the Next Reaction Method. There are some hardware approaches of SSAs that obtained high-throughput on reconfigurable devices such as FPGAs, but these works lacked in scalability. The design of this work can accommodate to the increasing size of target biochemical models, or to make use of increasing capacity of FPGAs. Interconnection network between arithmetic circuits and multiple simulation circuits aims to perform a data-driven multi-threading simulation. Approximately 8 times speedup was obtained compared to an excution on Xeon 2.80GHz.

This paper studies several designs of network-based FPGA implementation of a stochastic simulation algorithm called the next reaction method, known for its large number of calculation involved. The procedure is divided into several subdivisions which will be implemented as independent modules, and they are connected with configurable interconnection networks so as to provide high throughput. By performing a multi-threading simulation, 3.6 times speedup was obtained compared with an execution on general purpose processors.

ReCSiP is an FPGA-based biochemical simulator to accelerate kinetic simulations of biochemical pathways. Biochemical models are described as a set of ordinary differential equations (ODEs). Each equation in the model is called "rate law function", which represents the velocity of corresponding biochemical reaction mechanism. ReCSiP achieves high-throughput simulation with statically pipelined rate law function modules and numerical integration modules on an FPGA. This paper shows the basic structure of ReCSiP, and results of evaluation in 2 aspects: area and throughput. As the summary of evaluation, 1) about 64% of the total circuit area is occupied by floating-point arithmetic units, and 2) with an XC2VP70, ReCSiP at 90MHz can achieve 20times or more speedup compared to Intel's Pentium4 microprocessor at 3.2GHz

Stochastic simulation of biochemical systems has become one of major approaches to study life processes as system, yet is a computational challenge to run the simulation due to its vast calculation cost. This paper shows the implementation and evaluation of a stochastic simulation algorithm (SSA) called "first reaction method" on an FPGA-based biochemical simulator. It achieves high throughput by (1) consecutively throwing data into deeply-pipelined floating point arithmetic units, and (2) by distributing multiple simulators for parallel execution. As the result of evaluation on an FPGA-based simulation platform called ReC-SiP2, the simulator outperforms execution on Xeon 2.80 GHz by approximately 80 times, even with large-scale biochemical systems

A reconfigurable biochemical simulator by solving ordinary differential equations has received attention as a personal high speed environment for biochemical researchers. For efficient use of the reconfigurable hardware, static scheduling of high-throughput arithmetic pipeline structures is essential. This paper shows and compares some scheduling alternatives, and analyzes the tradeoffs between performance and hardware amount. Through the evaluation, it is shown that the sharing first scheduling reduces the hardware cost by 33.8% in average, with the up to 11.5% throughput degradation. Effects of sharing of rate law functions are also analyzed.

Today, mathematical modeling and simulation of biochemical pathways take a major role in biological researches. However, modern microprocessors cannot provide enough throughputs to explore the large parameter space of target pathways. To address this problem, ReCSiP (a reconfigurable cell simulation platform), an FPGA-based biochemical simulator is proposed. It's an ODE-based simulator, which solves the rate-law functions. The framework proposed in this paper, enables to simulate pathways consisting many different types of chemical reactions by connecting the rate-law modules (solvers) on an FPGA. It provides the solver-to-solver communication mechanism on an FPGA and automatic configuration software to generate the circuit.

BACKGROUND: The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) Nomarski differential interference contrast (DIC) microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages. RESULTS: We developed a system that automates the detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. Local image entropy is used to produce regions of the images that have the image texture of the nucleus. From these regions, those that actually detect nuclei are manually selected at the first and last time points of the image set, and an object-tracking algorithm then selects regions that detect nuclei in between the first and last time points. The use of local image entropy makes the system applicable to multiple image sets without the need to change its parameter values. The use of an object-tracking algorithm enables the system to detect nuclei in the process of cell division. The system detected nuclei with high sensitivity and specificity from the one- to 24-cell stages. CONCLUSION: A combination of local image entropy and an object-tracking algorithm enabled highly objective and productive detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. The system will facilitate genomic and computational analyses of C. elegans embryos.

MOTIVATION: Large-scale gene expression profiles measured in gene deletion mutants are invaluable sources for identifying gene regulatory networks. Signed directed graph (SDG) is the most common representation of gene networks in genetics and cell biology. However, no practical procedure that deduces SDGs consistent with such profiles has been developed. RESULTS: We developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method in which an algorithm deduces the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Positive (or negative) directed edges representing positive (or negative) gene regulations are deduced by comparing the gene expression level between the wild-type and mutant. The most parsimonious SDGs are deduced using graph theoretical procedures. Compensation for excess removal of edges by restoring a minimum number of edges makes the method applicable to cyclic gene networks. Use of independent groups of edges greatly reduces the computational cost, thus making the method applicable to large-scale expression profiles. We confirmed the applicability of our method by applying it to the gene expression profiles of 265 Saccharomyces cerevisiae deletion mutants, and we confirmed our method's validity by comparing the pheromone response pathway, general amino acid control system, and copper and iron homeostasis system deduced by our method with those reported in the literature. Interpretation of the gene network deduced from the S. cerevisiae expression profiles by using our method led to the prediction of 132 transcriptional targets and modulators of transcriptional activity of 18 transcriptional regulators. AVAILABILITY: The software is available on request.

In this paper, a method for co-evolving morphology and controller of a real biped walking robot is proposed using evolutionary computation. Although certain control theories exist, such as zero moment point (ZMP) compensation, these theories assume humanoid robot morphology is given in advance. Thus, engineers have to design a control program for a priori designed morphology. If morphology and locomotion are considered simultaneously, we do not have to spend time with trial-and-error. The detailed model of morphology is used for morphology in which the characteristics of the servo module are considered. As the controller, pattern generators are used. The biped walking robot which can walk the longest distance are designed with a two step genetic algorithm. As a result, the detailed model of the robot which can walk the longest distance is generated from limited size of chromosomes.

Robots should listen to and recognize speeches with their own ears under noisy environments and simultaneous speeches to attain smooth communications with people in a real world. This paper presents three simultaneous speech recognition based on active audition which integrates audition with motion. Our robot audition system consists of three modules - a real-time human tracking system, an active direction-pass filter (ADPF) and a speech recognition system using multiple acoustic models. The real-time human tracking realizes robust and accurate sound source localization and tracking by audio-visual integration. The performance of localization shows that the resolution of the center of the robot is much higher than that of the peripheral. We call this phenomenon "auditory fovea" because it is similar to visual fovea (high resolution in the center of the human eye). Active motions such as being directed at the sound source improve localization because of making the best use if the auditory fovea. The ADPF realizes accurate and fast sound separation by using a pair of microphones. The ADPF separates sounds originating from the specified direction obtained by the real-time human tracking system. Because the performance of separation depends on the accuracy of localization, the extraction of sound from the front direction is more accurate than that of sound from the periphery. This means that the pass range of ADPF should be narrower in the front direction than in periphery. In other words, such active pass range control improves sound separation. The separated speech is recognized by the speech recognition using multiple acoustic models that integrates multiple results to output the result with the maximum likelihood. Active motions such as being directed at a sound source improve speech recognition because it realizes not only improvement of sound extraction but also easier integration of the results using face ID by face recognition. The robot audition system improved by active audition is implemented on an upper-torso humanoid. The system attains localization, separation and recognition of three simultaneous speeches and the results proves the efficiency of active audition.

Controlling robot behaviors becomes more important recently as active perception for robot, in particular active audition in addition to active vision, has made remarkable progress. We are studying how to create social humanoids that perform actions empowered by real-time audio-visual tracking of multiple talkers. In this paper, we present personality as means of controlling on-verbal behaviors. It consists of two dimensions, dominance vs. submissiveness and friendliness vs. hostility, based on the interpersonal theory in psychology. The upper-torso humanoid SIG equipped with real-time audio-visual multiple-talker tracking system is used as a testbed for social interaction. As a companion robot, with friendly personality, it turns toward new sound source in order to show its attention, while with hostile personality, it turns away a new sound source. As a receptionist robot with dominant personality, it focuses its attention on the current customer, while with submissive personality, its attention to the current customer is interrupted by a new one.

In this paper, evolutionary designing method of a biped walking robot is proposed and evaluated in comparison with traditional manner. Usually, the structure and control system of robot have been designed separately. Therefore, the structure is given in advance when the control system is developed. This results in a great deal of trial-and-errors. On the other hand, evolutionary designing method carries out these operations simultaneously. Two calculations are conducted of which morphologies are changed (evolutionary designing method) and not changed (traditional manner) during the evolution. As a result, almost the same number of pareto optimal solutions are emerged. However, solutions generated with evolutionary designing method obtain higher fitnesses than the others. This means that evolutionary designing method could be powerful and efficient method for the real robot.

Robot audition by its own ears (microphones) is essential for natural human-robot communication and interface. Since a microphone is embedded in the head of a robot, the head-related transfer function (HRTF) plays an important role in sound source localization and extraction. Usually, from binaural input, the interaural phase difference (IPD) and interaural intensity difference (IID) are calculated, and then the direction is determined by using IPD and IID with HRTF. The problem of HRTF-based sound source localization is that a HRTF should be measured for each robot in an anechoic chamber, because it depends on the shape of robot's head; HRTF should be interpolated to manipulate a moving talker, because it is available only for discrete azimuth and elevation. To cope with these problems of HRTF, we proposed the auditory epipolar geometry as a continuous function of IPD and IID to dispense with HRTF and have developed a real-time multiple-talker tracking system. This auditory epipolar geometry, however, does not give a good approximation to IID of all range and IPD of peripheral areas. In this paper, the scattering theory in physics is employed to take into consideration the diffraction of sounds around robot's head for better approximation of IID and IPD. The resulting system shows that it is efficient for localization and extraction of sound at higher frequency and from side directions.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

We present a method for co-evolving structures and a controller of biped walking robots. Currently, biped walking humanoid robots are designed manually on trial-and-error basis. Although a certain control theory exists, such as zero moment point compensation, these theories assume humanoid robot morphology is given in advance. Thus, engineers have to design a control program for a priori designed morphology. It is very time consuming if both morphology and locomotion are considered simultaneously. Therefore, a method useful for designing the robot is proposed. First, simple models for both morphology and controller are used for the dynamic simulation, which are multi-link models as morphology and two kinds of controllers. One is a layered neural network and the other is a neural oscillator. Robots with optimal energy efficiency of walking are designed with the genetic algorithm. As a result, various combinations of morphologies and gaits are generated, and the relationship between the length of each link and moving distance is obtained, which gives an optimal energy efficiency. Moreover, the robots are encoded from the limited size of chromosomes.

Robot audition in real-world should cope with motor and other noises caused by the robot's own movements in addition to environmental noises and reverberation. This paper reports how auditory processing is improved by audio-visual integration with active movements. The key idea resides in hierarchical integration of auditory and visual streams to disambiguate auditory or visual processing. The system runs in real-time by using distributed processing on 4 PCs connected by a Gigabit Ethernet. The system implemented in a upper-torso humanoid tracks multiple talkers and extracts speech from a mixture of sounds. The performance of epipolar geometry based sound source localization and sound source separation by active and adaptive direction-pass filtering is also reported.

This paper presents a method for humanoid walking acquisition with less energy consumption based on a two-stage genetic algorithm. In the first phase of genetic algorithm, in order to acquire the continuous walking motion, the fitness function consists of a walking distance (longer is better). In the second phase, the fitness function consists of a walking distance (longer) and energy consumption (less) for acquisition of highly energy-efficient walking. Further, we restrain the relationship among some joints and keep knee joint straight on supporting leg in order to ensure the less energy consumption. We apply the method to our platform PINO which has low-torque actuators owing to the servo modules. In order to realize a genetic process, we encode the scaling parameter of the joint movements and the phase difference between the joints into the computational simulation which considers the characteristics of the servo module used in our platform, PINO. The evolved results are applied to a real PINO and its smooth and stable walking with less energy consumption is verified.

Robot audition in the real world should cope with environment noises and reverberation and motor noises caused by the robot's own movements. This paper presents the active direction-pass filter (ADPF) to separate sounds originating from the specified direction with a pair of microphones. The ADPF is implemented by hierarchical integration of visual and auditory processing with hypothetical reasoning on interaural phase difference (IPD) and interaural intensity difference (IID) for each subband. In creating hypotheses, the reference data of IPD and IID is calculated by the auditory epipolar geometry on demand. Since the performance of the ADPF depends on the direction, the ADPF controls the direction by motor movement. The human tracking and sound source separation based on the ADPF is implemented on an upper-torso humanoid and runs in real-time with 4 PCs connected over Gigabit ethernet. The signal-to-noise ratio (SNR) of each sound separated by the ADPF from a mixture of two speeches with the same loudness is improved to about 10 dB from 0 dB.

In this paper, we present a method for co-evolving morphology and controller of bi-ped humanoid robots. Currently, bi-ped walking humanoid robots are designed manually on trial-and-error basis. Thus, engineers have to design control program for apriori designed morphology, neither of them shown to be optimal within a large design space. We propose evolutionary approach that co-evolves the morphology and controller. Two steps of co-evolutions were achieved in a precision dynamics simulator. One is for obtaining the optimal lengths of links using simple multi-link model and the other is for obtaining the optimal geometry of servomotor which is often used for radio control car. Finally, unexpected optimal solutions were discovered. This indicates that a complex design task of biped humanoid can be performed automatically using evolution-based approach, thus varieties of humanoid robots can be designed in speedy manner.

This paper presents an active direction-pass filter (ADPF) that separates sounds originating from the specified direction by using a pair of microphones. Its application to front-end processing for speech recognition is also reported. Since the performance of sound source separation by the ADPF depends on the accuracy of sound source localization (direction), various localization modules including the interaural phase difference, interaural intensity difference for each sub-band, and other visual and auditory processing are integrated hierarchically. The resulting performance of auditory localization varies according to the relative position of the sound source. The resolution of the center of the robot is much higher than that of peripherals, indicating similar property of visual fovea. To make the best use of this property, the ADPF controls the direction of a head by motor movement. In order to recognize sound streams separated by the ADPF, a hidden Markov model based automatic speech recognition is built with multiple acoustic models trained by the output of the ADPF under different conditions. A preliminary dialog system is thus implemented on an upper-torso humanoid. The experimental results prove that it works well even when two speakers speak simultaneously.

In this paper, real-time multiple speaker tracking is addressed, because it is essential in robot perception and humanrobot social interaction. The difficulty lies in treating a mixture of sounds, occlusion (some talkers are hidden) and real-time processing. Our approach consists of three components; (1) the extraction of the direction of each speaker by using interaural phase difference and interaural intensity difference, (2) the resolution of each speaker's direction by multi-modal integration of audition, vision and motion with canceling inevitable motor noises in motion in case of an unseen or silent speaker, and (3) the distributed implementation to three PCs connected by TCP/IP network to attain real-time processing. As a result, we...

This paper addresses the problem of automatic recognition of three simultaneous speeches with two microphones, that is, that of sound source separation where the number of sound sources is greater than that of microphones. The approach used is the direction-pass filter, which is implemented by hypothetical reasoning on the interaural phase difference (IPD) and interaural intensity difference (IID). Auditory processing calculates IPD and IID for each subband, and generates hypotheses for precalculated IPD and IID for every direction including one obtained by visual processing. Then the system calculates the belief factor of hypothesis by Dempster-Shafer theory and determines the direction of each subband. Subbands of the specific direction are collected and then converted to a wave form by inverse FFT. With 200 benchmarks of three simultaneous utterances of Japanese words, the average 1-best and 10-best recognition rates of extracted speeches are 60% and 81%, respectively.

Sound localization for a robot or an embedded system is usually solved by using inter-aural phase difference (IPD) and inter-aural intensity difference (IID). These values are calculated by using head-related transfer function (HRTF). However, the HRTF depends on the shape of the head and also on changes of the environments. Therefore, sound localization without HRTF is needed for real-world applications. In this paper, we present a new sound localization method based on auditory epipolar geometry with motion control. The auditory epipolar geometry is an extension of an epipolar geometry in stereo vision to audition, and auditory and visual epipolar geometries can share the sound source direction. The key idea is to exploit additional inputs obtained by the motor control in order to compensate damages in the IPD and IID caused by reverberation of the room and the body of the robot. The proposed system can localize and extract simultaneously two sound sources in a real-world room.

Nakadai et al. (2001) have developed a real-time auditory and visual multiple-talker tracking technique. In this paper, this technique is applied to human-robot interaction including a receptionist robot and a companion robot at a party. The system includes face identification, speech recognition, focus-of-attention control, and sensorimotor task in tracking multiple talkers. The system is implemented on a upper-torso humanoid and the talker tracking is attained by distributed processing on three nodes connected by 100Base-TX network. The delay of tracking is 200 msec. Focus-of-attention is controlled by associating auditory and visual streams by using the sound source direction and talker position as a clue. Once an association is established, the humanoid keeps its face to the direction of the associated talker.

Describes the issues in robotics from a viewpoint of edutainment through a series of activities in the Robot World Cup Initiative and related events, such as the International Robot Games Festival (Robofesta) supported by the Japanese government to promote creative and imaginative education programs, RoboCup Jr. which is designed for kids and the younger generation to play RoboCup games with easily constructible platforms, development of small legged robots for pets in the house or games, and education projects in system engineering. Finally, concluding remarks for future activities are given.

The Robot World Cup Initiative (RoboCup) is an international joint project to promote science and technology in intelligent robotics, AI and related fields with the specific goal of building a fully humanoid robot that can be the human world cup champion by 2050, and to apply technologies developed in this activity to socially significant issues such as rescue, and for next generation industries. This idea inspired the imagination of people and it has grown to be one of the largest research areas in multi-agent robotics. The paper discusses extension of RoboCup activity for education and edutainment. Specifically, RoboCup has initiated a new project called RoboCup Jr. that aims at promotion of engineering education and popularization of robotics using RoboCup. A RoboCup Jr. league will be created to meet diverse needs of the education field, and a series of organizational frameworks are proposed to develop a network of social, personal, educational, and technical infrastructures to assist those who are interested in RoboCup Jr. activity.

The Japanese Government plans to hold the International Robot Games Festival in 2001, which is called "RoboFesta 2001". The RoboFesta 2001 will be held both in the Osaka area and Kanagawa Prefecture in Japan, and will consist of several authoritative robot games, an international forum, and lots of robot-related exhibitions during the summer and autumn in 2001. The article describes the aims and organisation of the festival, then describes the various games.

This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the virtual world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-machine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces for rescue parties in real disasters. It also provides a virtual reality training function for the public. This diverse spectrum of RoboCup-Rescue contributes to the creation of the safer social system.

We propose a framework towards the integration of information sensors based on the idea that the stimulus perceived through different sensors are spatial-time correlated for a short time period. Applications in robotics need to be able to process information from multiple sensors, for instance, in the case of a visible talking person. How can we relate this kind of information in a simple way, without making use of high level representation? This is the question that we want to address. A new framework based on a correlation measure of low level data information is proposed. This low level correlation measure can be used as an integration data engine to support high level task description. In the paper a coherent approach from sensor level to task level for developing a robot which can handle a large number of sensors and actuators is developed. An example how this approach can be used for a visual-sound integration task is also presented.

In this paper, we report an initial design of humanoid head platform for RoboCup humanoid challenge. While many researches in RoboCup humanoid challenge naturally focus on walking and running behaviors, we focus on perception and high-level behavior issues using an upper-torso humanoid. We have designed a head/neck part of the humanoid with aesthetically designed appearance, and various processing for early perception and associated reflex. The goal of this paper is to illustrate issues in designing humanoid head platform for high-level cognition research, and provide some of initial insights obtained during the early stage of implementations. With regards to perception, the emphasis will be placed on how interaction between different perception channel and motor control can complement each other to improve processing.

Disaster rescue is one of the most serious social issues which involves very large numbers of heterogenous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain by creating a standard simulator and forum for researchers and practitioners. While the rescue domain is intuitively appealing as a large scale multi-agent domain, it has not yet been given thorough analysis of its domain characteristics. We present detailed analysis of the task domain, challenges, and outline benchmark evaluations.

We present a humanoid active audition system with improved noise cancellation using humanoid cover acoustics and the cover design for an industrial exterior design. For active audition, it is an important problem to distinguish between the internal sounds like motor noises and sounds which originate from the outer world. It is important that inevitable motor noise is cancelled while the humanoid is in motion. Therefore, humanoids require the exterior to cancel such internal noises because it separates humanoid inner world from the outer world. We report an active audition system focused on the sound source tracking by integrating audition, vision and motor movements using sound separation of the humanoid cover. The experiments show that the humanoid can track and localize sound sources more accurately, the noise cannot always be cancelled out optimally and further improvements are required.

RoboCup is an increasingly successful attempt to promote the full integration of robotics and AI research. The most prominent feature of RoboCup is that it provides the researchers with the opportunity to demonstrate their research results as a form of competition in a dynamically changing hostile environment, defined as the international standard game definition, in which the gamut of intelligent robotics research issues are naturally involved. The article describes what we have learned from the past RoboCup activities, and overview the future perspectives of RoboCup in the next century, mainly focusing on the real robot leagues. Finally, we introduce the new leagues, one of which will have been held at RoboCup-99 in Stockholm.

We propose a concept for reconfigurable robotics, which is to analyze a robot configuration in syntax and to give semantics to robot components. We call it "syntactic-semantic analysis of robot configuration". The motivation for this concept arises when we consider a reconfigurable robot platform, which allows us to build various styles of robots. We would like to establish a method which enables us to develop a portable application software on various styles of robots. Making an analogy to syntactic theory of natural language processing, we propose a syntactic analysis of the robot configuration, which also gives semantics to robot components. We implement the proposed method on a reconfigurable robot platform, which allow us to configure a quadruped robot and a wheel-based robot.

One of the ultimate dreams in robotics is to create life-like robotics systems, such as a humanoid robot and an animal-like legged robot. We choose to build a pet-type legged robot because we believe that dog-like and cat-like legged robots have major potential for future entertainment robotics markets for personal robots. However, a number of challenges exist before any such robot can be fielded in the real world. Robots have to be reasonably intelligent, maintain a certain level of agility, and be able to engage in some collaborative behaviors. RoboCup is an ideal challenge to foster robotics technologies for small personal and mobile robotics system. In this paper, we present Sony's legged robots system which entered RoboCup-98 Paris as a special exhibition game.

The ultimate goal of the RoboCup Initiative is to build a humanoid soccer team which beats a human World Cup Champion team. In this paper, we presents reasons why this goal should be pursued, and analyze technical issues involved in, humanoid to play soccer game. The analysis demonstrates the breadth of technologies that need to be developed through the course of the Challenge, which has major impacts to industries in general.

Sony has provided a remarkable platform for research and development in robotic agents, namely fully autonomous legged robots. In this paper, we describe our work using Sony's legged robots to participate at the RoboCup'98 legged robot demonstration and competition. Robotic soccer represents a very challenging environment for research into systems with multiple robots that need to achieve concrete objectives, particularly in the presence of an adversary. Furthermore RoboCup'98 offers an excellent opportunity for robot entertainment. We introduce the RoboCup context and briefly present Sony's legged robot. We developed a vision-based navigation and a Bayesian localization algorithm. Team strategy is achieved through pre-defined behaviors and learning by instruction.

The soil nematode Caenorhabditis Elegans (C. elegans) is the most investigated of all multicellular organisms. Since the proposal to use it as a model organism, a series of research projects have been undertaken, investigating various aspects of this organism. As a result, the complete cell lineage, neural circuitry, and various genes and their functions have been identified. The complete C. elegans DNA sequencing and gene expression mapping for each cell at different times during embryogenesis will be identified in a few years. Given the abundance of collected data, we believe that the time is ripe to introduce synthetic models of C. elegans to further enhance our understanding of the underlying principles of its development and behavior. For this reason, we have started the Perfect C. elegans Project, which aims to produce ultimately a complete synthetic model of C. elegans' cellular structure and function. This article describes the goal, the approach, and the initial results of the project.

An overview of RoboCup (The World Cup Robot Soccer), which offers opportunities for AI and robotics research by providing an attractive but formidable challenge. It also provides a range of challenge programs which is designed to evaluate specific technical issues. The challenge program will be up-dated and new challenges will be offered as technology progresses. Along with other programs such as an education program, RoboCup offers a comprehensive research program which promotes AI and robotics.

This article reports on a simple neurogenesis model that is combined with evolutionary computation. Because the integration of an evolutionary process with neural networks is such an exciting field of study, with the promise of discovering new computational models and, possibly, providing novel biological insights, much research has been conducted in this area. However, only a few studies have incorporated a development stage, and none have modeled metabolism and other chemical reactions in a consistent manner. In this article, we present a simple model of neurogenesis and cell differentiation that combines evolutionary computing, metabolism, development, and neural networks. The model represents an evolutionary large-scale chaos as a mathematical foundation. An evolutionary large-scale chaos is a large-scale chaos whose map functions change through evolutionary computing. Experiments indicate that the model is capable of evolving and growing large neural networks, and exhibits phenomena analogous to cell differentiation.

This paper presents an enhanced model of plan-based dialogue understanding. Most plan-based dialogue understanding models derived from [Litman and Allen, 1987] assume that the dialogue speakers have access to the same domain plan library, and that the active domain plans are shared by the two speakers. We call these features shared domain plan constraints. These assumptions, however, ae too strict to account for mixed-initiative dialogues where each speaker has a different set of domain plans that are housed in his or her own plan library, and where an individual speaker's domain plans may be activated at any point in the dialogue. We propose an extension to the Litman and Allen model by relaxing the shared domain plan constraints. Our extension improves (1) the ability to track the currently active plan, (2) the ability to explain the planning behind speaker utterances, and (3) the ability to track which speaker controls the conversational initiative in the dialogue.