IBM Research - Tokyo

Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a "host" signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding.

Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.

The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous-synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system's communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.

In multihop packet radio networks with randomly distributed terminals, the optimal transmission radii to maximize the expected progress of packets in desired directions are determined with a variety of transmission protocols and network configurations. It is shown that the FM capture phenomenon with slotted ALOHA greatly improves the expected progress over the system without capture due to the more limited area of possibly interfering terminals around the receiver. The (mini)slotted nonpersistent carrier-sense-multiple-access (CSMA) only slightly outperforms ALOHA, unlike the single-hop case (where a large improvement is available), because of a large area of "hidden" terminals and the long vulnerable period generated by them. As an example of an inhomogeneous terminal distribution, the effect of a gap in an otherwise randomly distributed terminal population on the expected progress of packets crossing the gap is considered. In this case, the disadvantage of using a large transmission radius is demonstrated.

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yetmassively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and nonfilamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.

We review developments in transparent data embedding and watermarking for audio, image, and video. Data-embedding and watermarking algorithms embed text, binary streams, audio, image, or video in a host audio, image, or video signal. The embedded data are perceptually inaudible or invisible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal, e.g., multilingual soundtracks in a movie, or provide copyright protection. We discuss the reliability of data-embedding procedures and their ability to deliver new services such as viewing a movie in a given rated version from a single multicast stream. We also discuss the issues and problems associated with copy and copyright protection and assess the viability of current watermarking algorithms as a means for protecting copyrighted data.

This paper investigates a stochastic model for a software error detection process in which the growth curve of the number of detected software errors for the observed data is S-shaped. The software error detection model is a nonhomogeneous Poisson process where the mean-value function has an S-shaped growth curve. The model is applied to actual software error data. Statistical inference on the unknown parameters is discussed. The model fits the observed data better than other models.

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.

A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discrete-time linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds. 1 1.

Jalapeño is a virtual machine for Java™ servers written in the Java language. To be able to address the requirements of servers (performance and scalability in particular), Jalapeño was designed “from scratch“ to be as self-sufficient as possible. Jalapeño's unique object model and memory layout allows a hardware null-pointer check as well as fast access to array elements, fields, and methods. Run-time services conventionally provided in native code are implemented primarily in Java. Java threads are multiplexed by virtual processors (implemented as operating system threads). A family of concurrent object allocators and parallel type-accurate garbage collectors is supported. Jalapeño's interoperable compilers enable quasi-preemptive thread switching and precise location of object references. Jalapeño's dynamic optimizing compiler is designed to obtain high quality code for methods that are observed to be frequently executed or computationally intensive.

This paper discusses improvements to conventional software reliability analysis models by making the assumptions on which they are based more realistic. In an actual project environment, sometimes no more information is available than reliability data obtained from a test report. The models described here are designed to resolve the problems caused by this constraint on the availability of reliability data. By utilizing the technical knowledge about a program, a test, and test data, we can select an appropriate software reliability analysis model for accurate quality assessment. The delayed S-shaped growth model, the inflection S-shaped model, and the hyperexponential model are proposed.

In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information. The same surveys show, however, that users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results (e.g., noise and broken links) are commonly cited problems. We discuss the development of new techniques targeted to resolve some of the problems associated with Web-based information retrieval and speculate on future trends.

Three-dimensional (3D) silicon integration of active devices with through-silicon vias (TSVs), thinned silicon, and silicon-to-silicon fine-pitch interconnections offers many product benefits. Advantages of these emerging 3D silicon integration technologies can include the following: power efficiency, performance enhancements, significant product miniaturization, cost reduction, and modular design for improved time to market. IBM research activities are aimed at providing design rules, structures, and processes that make 3D technology manufacturable for chips used in actual products on the basis of data from test-vehicle (i.e., prototype) design, fabrication, and characterization demonstrations. Three-dimensional integration can be applied to a wide range of interconnection densities (<10/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), requiring new architectures for product optimization and multiple options for fabrication. Demonstration test structures, which are designed, fabricated, and characterized, are used to generate experimental data, establish models and design guidelines, and help define processes for future product consideration. This paper 1) reviews technology integration from a historical perspective, 2) describes industry-wide progress in 3D technology with examples of TSV and silicon-silicon interconnection advancement over the last 10 years, 3) highlights 3D technology from IBM, including demonstration test vehicles used to develop ground rules, collect data, and evaluate reliability, and 4) provides examples of 3D emerging industry product applications that could create marketable systems.

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.

We improve the quality of quantum circuits on superconducting quantum computing systems, as measured by the quantum volume, with a combination of dynamical decoupling, compiler optimizations, shorter two-qubit gates, and excited state promoted readout. This result shows that the path to larger quantum volume systems requires the simultaneous increase of coherence, control gate fidelities, measurement fidelities, and smarter software which takes into account hardware details, thereby demonstrating the need to continue to co-design the software and hardware stack for the foreseeable future.

This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.

Abstract New tools enable new ways of working, and materials science is no exception. In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows.

Increasing numbers of physical sensors are used for various purposes. Those physical sensors are usually used by their own applications. Because each application manages both of physical sensors and their sensor data exclusively, other applications cannot use the physical sensors in the different party easily. We propose a new infrastructure called Sensor-Cloud infrastructure which can manage physical sensors on IT infrastructure. The Sensor-Cloud Infrastructure virtualizes a physical sensor as a virtual sensor on the cloud computing. Dynamic grouped virtual sensors on cloud computing can be automatic provisioned when the users need them. The approach to enable the sensor management capability on cloud computing. Since the resource and capability of physical sensor devices is limited, the cloud computing on the IT infrastructure can be behalf of the sensor management such as availability and performance of physical sensors. This paper describes the design of Sensor-Cloud Infrastructure, the system architecture and the implementation.

This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.