Miracle-dz/UniFAR-FTUs huggingface dataset:
High-performance 3-D imaging algorithms for UWB pulse radars

Acknowledgments The author wishes to express his appreciation to Professor Toru Sato for his consistent guidance and insightful advice during the current work. It would have been impossible to complete this thesis without his constructive suggestions and criticism. The author deeply appreciates Assistant Professor Takuya Sakamoto for his appropriate advice and invaluable suggestions. The author thanks Profs. Takashi Matsuyama and Tetsuya Matsuda sincerely for their advice and criticism throughout the current work. for their invaluable advice about the experimental systems. The author sincerely thanks the colleagues in Prof Sato's Laboratory for meaningful discussions and their continued maintenance of the computers and network system in the laboratory. The author thanks the members of the radar research group for their profitable suggestions and discussions. The author wishes to thank the members of the COE program, for giving invaluable suggestions in seminars from the viewpoints of various fields. i Preface Non-destructive testing or measurement for precision devices such as reflector antennas, requires high-performance image reconstruction systems. There are also emergent demands from the proximity imaging systems for moving robots or vehicles aimed at Intelligent Transport System (ITS). These applications require different kinds of performance, such as real-time operation, fine resolution, robustness and others. Much research focus-ing on high-grade imaging systems with a wave has been carried out, which are based on optical, ultrasonic and radio detections. Each method however, has its own unique problems, and it is difficult to achieve all the required performance to a high degree. This thesis introduces an imaging system and algorithm with pulse radars, which has an advantage in range resolution. Formerly, pulse radar systems never dealt with proximity imaging due to the limited frequency bandwidth of signals. However, in recent years, Ultra Wideband (UWB) signal has been regulated and approved in several countries including Japan. UWB pulse radar systems show great promise in dealing with near field imaging and with considerably high range resolution. Much research has been carried out on sensing algorithms with radar such as SAR, range migration and diffraction tomography. It is well-known that proximity imaging with radars often becomes an ill-posed inverse problem. Most conventional algorithms are therefore, based on a recursive optimization or model-based approach, which requires intensive computational resources. As such, they are not applicable to real-time applications , which are needed for robotic and other visualization systems. To solve this problem, SEABED (Shape Estimation Algorithm based on BST and Extraction …
Optimal Severance Pay in a Matching Model

This paper uses an equilibrium matching framework to study jointly the optimal private provision of severance pay and the allocational and welfare consequences of government intervention in excess of private arrangements. Firms insure risk-averse workers by means of simple explicit employment contracts. Contracts can be renegotiated ex post by mutual consent. It is shown that the privately optimal severance payment is bounded below by the fall in lifetime wealth associated with job loss. Simulations show that, despite contract incompleteness, legislated dismissal costs largely in excess of such private optimum are effectively undone by renegotiation and have only a small allocational effect. Welfare falls. Yet, for deviations from laissez faire in line with those observed for most OECD countries, the welfare loss is small.
Generating exact lattices in the WFST framework

We describe a lattice generation method that is exact, i.e. it satisfies all the natural properties we would want from a lattice of alternative transcriptions of an utterance. This method does not introduce substantial overhead above one-best decoding. Our method is most directly applicable when using WFST decoders where the WFST is “fully expanded”, i.e. where the arcs correspond to HMM transitions. It outputs lattices that include HMM-state-level alignments as well as word labels. The general idea is to create a state-level lattice during decoding, and to do a special form of determinization that retains only the best-scoring path for each word sequence. This special determinization algorithm is a solution to the following problem: Given a WFST A, compute a WFST B that, for each input-symbol-sequence of A, contains just the lowest-cost path through A.
Sediment disentrainment and the concept of local versus nonlocal transport on hillslopes

A local formulation of the sediment flux on a hillslope describes the flux as a unique function of local hillslope conditions at any contour position x, whereas a nonlocal formulation must take into account nonlocal (upslope or downslope) conditions that influence the flux at x. Local formulations are reasonable when particle motions involve small length scales associated with localized bioturbation of the soil column or with proximal surface transport such as rain splash. Nonlocal formulations may be more appropriate in steeplands where patchy, intermittent motions involve large travel distances, mostly over the surface. Once sediment motions are initiated, the disentrainment process determines the distribution of particle travel distances, which, in turn, forms the basis of nonlocal formulations that involve a convolution of hillslope surface conditions, for example, the land‐surface slope. The kernel in the convolution integral, which weights the effect of land‐surface conditions (e.g., slope) at all positions upslope or downslope of x, derives from the formulation of the disentrainment rate and characterizes whether particle travel distances depend on conditions at the position where motions originate or vary as particles experience changing surface conditions during their downslope motions. If hillslope properties controlling transport (e.g., surface slope) are defined or measured at a specified resolution, then motions smaller than this resolution cannot be attributed to these properties resolved at a smaller scale. In essence, the relative importance of local and nonlocal transport depends on the scale of particle motions compared to the relevant scale of hillslope properties that drive transport.
Characterizing Users in an Online Classified Ad Network

Unlike online social networking sites (e.g. Twitter and Facebook) which are heavily used for disseminating content and sharing information between users and shopping sites (e.g. Ebay) where buyers and sellers are reviewed, the flow of information between users such as buyers and sellers in a classified ad network is very limited. Characterizing users or assigning them to some classes in one such network is challenging due to the sparsity of the data about users, the vague separation of user classes and sometimes the tendency of users to hide or misrepresent their profile information. In this paper, we study the information revealed in the ads posted to an online classified ads site and analyze the behaviour of users posting those ads; our study is conducted using data collected from Kijiji over a year. We study the problem in the context of one specific task where we seek to detect if a user posting an ad belongs to one of the two classes business and non-business, based on the ads the user has posted. We study an approach based on user profiling, where given statistics on how an ad mentions terms and features from a class profile, the affinity of an ad (and subsequently a user) to a particular class is determined. We report the effectiveness of this approach in detecting user classes solely based on the information revealed in their ads and study the impact of the profile size on the accuracy. In the absence of labeled training data, we show that a simple bootstrapping technique with only a few n-grams as a seed set can give nearly good results in terms of F-measure. We further report our experiments on characterizing the collective behavior of users in posting ads and some of the distinctive usage patterns that emerge.
Data reduction in urban traffic sensor network

In a large scale sensor network for traffic surveillance, the data to be transmitted is huge, which leads to high cost of communication. When sensor nodes are connected wirelessly, the situation will be worse. So it's necessary to reduce the data amount before data packets are transmitted. In this paper, we propose a distributed algorithm base on FCM (Fuzzy C-Means Clustering). Parameters in the algorithm are also discussed. With a set of relative optimal parameters, we perform an experiment. In our experiment based on a traffic flow volume data set, the algorithm shows high-performance with a really high reconstruction precise. The data is compressed evidently before it's transmitted, saving 46% approximately transmitted.
Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect

A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.
Performance evaluation of two home-based lazy release consistency protocols for shared virtual memory systems

This paper investigates the performance of shared virtual memory protocols on large-scale multicomputers. Using experiments on a 64-node Paragon, we show that the traditional Lazy Release Consistency (LRC) protocol does not scale well, because of the large number of messages it requires, the large amount of memory it consumes for protocol overhead data, and because of the diÆculty of garbage collecting that data. To achieve more scalable performance, we introduce and evaluate two new protocols. The rst, Home-based LRC (HLRC), is based on the Automatic Update Release Consistency (AURC) protocol. Like AURC, HLRC maintains a home for each page to which all updates are propagated and from which all copies are derived. Unlike AURC, HLRC requires no specialized hardware support. We nd that the use of homes provides substantial improvements in performance and scalability over LRC. Our second protocol, called Overlapped Home-based LRC (OHLRC), takes advantage of the communication processor found on each node of the Paragon to o oad some of the protocol overhead of HLRC from the critical path followed by the compute processor. We nd that OHLRC provides modest improvements over HLRC. We also apply overlapping to the base LRC protocol, with similar results. Our experiments were done using ve of the Splash-2 benchmarks. We report overall execution times, as well as detailed breakdowns of elapsed time, message traÆc, and memory use for each of the protocols.
MinEDec : a Decision-Support Model That Combines Text-Mining Technologies with Two Competitive Intelligence Analysis Methods

With the development of information technologies, enterprises are working in a complex and dynamic environment. Monitoring the competitive environment and effectively transforming data into knowledge for decision makers are vital competitive powers. In order to monitor and analyse the competitive environment of businesses, we integrate two well-known competitive intelligence analysis methods, the Five Forces Analysis (FFA) and a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis with various text-mining technologies in a decision support model Mining Environment for Decisions (MinEDec). We also outline the architecture of a decision support system that is based on the proposed model.
Janus : Automatic Ontology Construction Tool

The construction of an ontology for a large domain st ll remains an hard human task. The process is sometimes assisted by software tools that facilitate some parts of the construction and evolu ti n life-cycle. But they do not propose a methodology that considers the automa ti n of the entire process. In this paper we present a method for deriving auto matically an ontology. Then, we introduce Janus, an implementation of this appro ach, for deriving automatically a skeleton of an ontology from XML sc hema files in a given domain. Janus also provides different useful views that can be used for a final revision by an expert.
Low Power Probabilistic Floating Point Multiplier Design

We present a low power probabilistic floating point multiplier. Probabilistic computation has been shown to be a technique for achieving energy efficient designs. As best known to the authors, this is the first attempt to use probabilistic digital logic to attain low power in a floating point multiplier. To validate the approach, probabilistic multiplications are introduced in a ray tracing algorithm used in computer graphics applications. It is then shown that energy savings of around 31% can be achieved in a ray tracing algorithm's floating point multipliers with negligible degradation in the perceptual quality of the generated image.
Evaluating boosting algorithms to classify rare classes: comparison and improvements

Classification of rare events has many important data mining applications. Boosting is a promising meta-technique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. The authors evaluate three existing categories of boosting algorithms from the single viewpoint of how they update the example weights in each iteration, and discuss their possible effect on recall and precision of the rare class. We propose enhanced algorithms in two of the categories, and justify their choice of weight updating parameters theoretically. Using some specially designed synthetic datasets, we compare the capability of all the algorithms from the rare class perspective. The results support our qualitative analysis, and also indicate that our enhancements bring an extra capability for achieving better balance between recall and precision in mining rare classes.
The Robin Function and Its Eigenvalues

Abstract The paper deals with the Robin function and eigenvalue problems generated by the Robin operator. First we show that Green's function of an 𝑛-fold connected domain is the Robin function of an appropriate simply connected domain. The main part of the paper deals with eigenvalue problems for the Robin operator: the mixed Stekloff eigenvalue problem and the membrane problem with mixed boundary conditions. Isoperimetric inequalities are proved for the sum of reciprocal eigenvalues.
Irradiation models for ULXs and fits to optical data

We have constructed a model which describes the optical emission from ultraluminous X-ray sources (ULXs), and have used it to constrain the parameters of seven ULX systems. Our model assumes a binary nature for ULXs, and accounts for optical emission from an X-rayirradiated companion star and accretion disc. We apply our model to six different ULX optical counterparts observed with the Hubble Space Telescope, and one observed with the ESO VLT, and determine the mass, radius and age of the donor stars in these systems. In addition, we obtained constraints for the black hole (BH) mass in some cases. We use the mass accretion rate implied by the X-ray luminosity of these sources as an additional constraint on the donor star, by assuming the mass transfer is driven by the stellar nuclear evolution. We find that in general the donors are older and less massive than previously thought, and are consistent with being of spectral type B. We discuss how these results affect our understanding of the evolution and history of ULXs. Where we can constrain the BH masses, we find them to be consistent with stellar mass BHs or intermediate mass BHs of order ∼100 M� . We make predictions for future observations of optical/infrared ULX counterparts, calculating binary periods for different BH masses in each of the seven sources.
Relativistic Spinning Particle and a New Non-Commutative Spacetime

The relativistic spinning particle model, recently proposed by Staruszkiewicz, is analyzed in a Hamiltonian framework. The model is heavily constrained and constraint analysis, in the Dirac scheme, is both novel and instructive. Our major finding is an associated noncommutative spacetime the structure of which is completely new. We have shown that this non-commutative phase space algebra is essential in revealing the spin effects in the particle model through the angular momentum generator and Hamiltonian equations of motion.
Recommender Systems as a Mobile Marketing Service

Recommender systems (RSs) have become a familiar artifact in cyberspace as a vehicle for increasing revenues while deepening customer loyalty and satisfaction. Typically RS are developed in house by companies with a large product line and customer base. However, the structure of RS is often straightforward, and effective systems can be developed at relatively low cost, and thus offered as a marketing service. We discuss the anatomy of a specific recommender system designed for a telecommunication carrier which uses predictive analytics in the form of collaborative filtering techniques to recommend products to users. Collaborative filtering (CF) is based upon the premise that users who have purchased a particular product will have similar preferences to other users who also purchased the product. We discuss and compare three versions of a CF-based recommender system based upon customer purchase history, customer browsing history, and user segments respectively. The results in terms of increased sales suggest that RS offer substantial value as a mobile marketing service. We suggest ways in which RS can be generalized into RS generators for more rapid development and deployment. We then invert the RS perspective from product-centric to user-centric and suggest how this would work in customer targeting for mobile advertising campaigns. We conclude that RS in the largest sense are heavily model-feedback in nature and require increasingly sophisticated automated modeling and predictive analytics capabilities layered on a scalable big data infrastructure.
Service robots: An industrial perspective

This paper presents the overview and the introduction of commercialized robotic products in the industrial service, maintenance and repair sectors. General facts of the industrial service are briefly described, and then we focus on four specific applications including motor /generator inspection, solar panel inspection /cleaning, tank inspection and pipe inspection. For each application, service process characteristics, operational details, technical challenges, requirements are described. Robotics solutions with commercialized products of each application area were introduced and detailed with special features and specification.
Distributed resource reservation for real time sessions in multi-hop wireless networks

Quality of Service (QoS) provisioning in multi-hop wireless networks (MHWNs) has become a promising and challenging topic in the wireless communication research community. In this paper, a novel distributed MAC scheduling protocol with reservation mechanism is proposed for enhancing the QoS performance of IEEE 802.11e-based MHWNs. The proposed mechanism guarantees resources in pre-configured contention-free period (CFP) for real-time sessions (RTSNs) in the network in a distributed manner. Here, resource reservation (RR) for multiple sessions can be made without any collision through an effective signalling process which coordinates the RR for RTSNs. Distributed admission control (AC) ensures that the existing RTSNs are not violated from QoS guarantees. In addition, a concurrent transmission (CT) mechanism is implemented within the pre-configured CFP. This is to further improve the bandwidth utilization by synchronizing the reserved transmissions among different nodes which do not interfere with each other. Simulation results indicate that the proposed scheduling mechanism can address the problems posed by inter- and intra-flow interference and can achieve guaranteed QoS for admitted RTSNs while providing fairness for the other sessions in the network.
Independently Extensible Solutions to the Expression Problem

The expression problem is fundamental for the development of extensible software. Many (partial) solutions to this problem have been proposed in the past, but the question of how to use different, independent extensions jointly has received less attention so far. This paper proposes solutions to the expression problem that make it possible to combine independent extensions in a flexible, modular, and typesafe way. The solutions, formulated in the programming language Scala, are affected with only a small implementation overhead and are relatively easy to implement by hand.
Effects of Sex, Race, Ethnicity and Marital Status on the Relationship between Intelligence and Fertility

A negative relationship between intelligence and fertility in the United States has been described repeatedly, but little is known about the mechanisms that are responsible for this effect. Using data from the NLSY79, we investigate this issue separately for Blacks, non-Hispanic Whites and Hispanics. The major findings are: (1) Differential fertility would reduce the average IQ of the American population by up to 1.2 points per generation in the absence of migration and environmental changes; (2) About 0.4 points of the effect is caused by selection within racial and ethnic groups, and the rest is caused by between-group selection; (3) Differential fertility by intelligence is greatest in Hispanics and smallest in non-Hispanic Whites; (4) The fertility-reducing effect of intelligence is greater in females than males; (5) The IQfertility relationship is far stronger for unmarried than married people, especially females; (5) High intelligence does not reduce the desire for children; (6) High intelligence does not reduce the likelihood of marriage; (7) Education is the principal mediator of the IQ effect for married women.
Towards autonomic emergency communications: Evolution of cooperative networking

The most recent evolution of and the advancement in the area of autonomic cooperative networking appears to be very appealing when it comes to emergency system design. Emergency networks are formed, among others, by the First Responders present and operating in the area of incident. Thus it is necessary to guarantee an accurate and immediate deployment of the Chief First Responders coordinating the actions undertaken by the First Responders belonging to their groups. Consequently, the entire networked system is expected to be able to adapt accurately to the changing external conditions. More specifically, it is possible to instantiate cooperative transmission between different Chief First Responders so that they may mutually support each other or one another when their respective First Responders are served. This paper then provides an analysis of several different cooperative set-ups of such a type allowing to quantify the potential gains which would result from autonomic switching between or among them.