Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-operative surgical simulation of an acetabular press fit: assembly strains in bone

An idealized axisymmeuic finite element model of the acetabulum and acetabular implant was analyzed. The assembly strains induced in the bone by the press fit insertion of an oversized cementless acetabular implant were examined. The interaction between implant and bone was modeled as a nonlinear contact-coupled interface to simulate the actual process of press fitting the oversized implant into the prepared acetabulat bone bed. INTRODUCTION Initial implant stability of the cementless acetabular implant is commonly achieved by press fitting oversized acetabular components. Implicit in this concept is “pre-straining” of the bone which must occur at the time of implantation. Assembly strains are expected to be very different in both magnitude and direction from the strains induced by normal joint forces. and can greatly change the mechanical environment and load transfer pattems. The mechanical consequences of an interference fit and the development of these assembly strains in bone are critical to our understanding of botb the biologic response of bone to mechanical stimuli and the way in which the bondimplant consuuct may be modeled. ”be purpose of this study was to determine the effect of the amount of press fit and the size of the implant on the magnitude of resultant assembly strains.
Understanding feature modularity

In this dissertation, we explore language and modularity support for features founded on an algebraic model geared for program synthesis. Our model integrates ideas from collaboration-based designs, mixin layers, aspect oriented programming, multidimensional separation of concerns, and generative programming. We assess our model with an implementation of a non-trivial product line case study, and evaluate feature support in emerging modularization technologies.
Adaptive Probabilistic Generalized LR Parsing

Various issues in the implementation of generalized LR parsing with probability are discussed. A method for preventing the generation of infinite numbers of states is described and the space requirements of the parsing tables are assessed for a substantial natural-language grammar. Because of a high degree of ambiguity in the grammar, there are many multiple entries and the tables are rather large. A new method for grammar adaptation is introduced which may help to reduce this problem. A probabilistic version of the Tomita parse forest is also described.
First-Class Synchronous Operations in Standard ML

In [Reppy88], we introduced a new language mechanism, first-class synchronous operations, for synchronous message passing. In our approach, synchronous operations are represented by first-class values called events. Events can be combined in various ways, allowing a user to define new synchronization abstractions (e.g., remote procedure call), which have equal status with the built-in operations. This paper describes this mechanism and presents a new implementation of events as part of a coroutine package for Standard ML. The coroutine package is written entirely in SML, using first-class continuations, and provides very light-weight processes. First-class continuations provide a natural way to represent events that closely follows an operational semantics for events.
BIG DATA : From hype to reality

Big data is all of a sudden everywhere. It is too big to ignore! It has been six decades since the computer revolution, four decades after the development of the microchip, and two decades of the modern Internet! More than a decade after the 90s “. com” fizz, can Big Data be the next Big Bang? Big data reveals part of our daily lives. It has the potential to solve virtually any problem for a better urbanized global. Big Data sources are also very interesting from an official statistics point of view. The purpose of this paper is to explore the conceptions of big data and opportunities and challenges associated with using big data especially in official statistics. “A petabyte is the equivalent of 1,000 terabytes, or a quadrillion bytes. One terabyte is a thousand gigabytes. One gigabyte is made up of a thousand megabytes. There are a thousand thousand—i.e., a million—petabytes in a zettabyte” (Shaw 2014). And this is to be continued…
Modeling and simulation of a satellite constellation based on Petri nets

This paper presents the approach taken by the French Space Agency for studying the availability of today's relevant space systems: satellite constellations. These non-Markovian distributed systems require powerful models to be described and analyzed. Over a generic example of a satellite constellation we illustrate Petri net's ability to deal with such complex projects from both modeling and evaluation view points. Results of such studies overcome the sole verification of availability objectives and actually provide interesting outputs for project managers. This is shown through the interpretation of typical results.
Single Case Experimental Designs: Strategies for Studying Behavior Change

Important text offers lucid explanation of how to regulate variables and maintain control over statistics in order to achieve quality control over manufactured products, crops. 250 pages. "While it is usually helpful to launch improvement programs, many such programs soon get bogged down in detail. They either address the wrong problems, or they keep beating on. Data Analysis and SPC in a service setting. Emphasis is give to three basic questions of quality improvement: What do you want to accomplish. A pioneer in learning organizations offers five disciplines that reveal the link between far-flung causes and immediate effects and that can save organizations from becoming. Written for the practitioner by a practitioner, this plain-language guide to the lean production system delivers a comprehensive insider's view of lean manufacturing. The. Actual experiences of a CEO how to make continuous improvement in manufacturing succeed for your company, Hank McHale, Mar 1, 1995, Business & Economics, 160 pages. This book emphasizes action. McHale tells you how to apply the four basic management elements of planning, organizing, directing/coaching, and controlling to help you drive.
The benefits of the Met Office variable resolution NWP model for forecasting convection

High‐resolution NWP models which can explicitly allow convection (albeit poorly resolved) are usually run in limited‐area domains, and are nested inside coarser resolution models with parametrized convection. The mismatch of the grids and model physics at the boundaries of the limited‐area fine resolution model can be a major source of model error. Two major issues are the change in the representation of convection (parameterized to explicit) as air enters the fine resolution model and the limited boundary updating frequency. In this paper, a variable‐grid, fine‐resolution, limited‐area version of the Met Office's Unified Model (UM), developed with the aim of addressing this and related problems with nested models is described. In this variable resolution model, the grid size varies smoothly from coarser (but still convection permitting) resolution at the outer boundaries to a uniform fine resolution in the interior of the domain. In this paper we present results from a comparison of this variable grid model with the analogous results from an equivalent nested model set with uniform high‐resolution model nested inside a lower resolution one. The comparison is carried out for a number of convective cases. It is found that the variable resolution model gives very similar results to the nested model system in the inner fixed resolution part of the domain away from the boundaries, both in individual case studies and when statistics are aggregated over cases. This gives confidence in the validity of the variable resolution approach. It is shown that the variable resolution model also gives the hoped for benefits of reducing artefacts at the boundaries.
Evaluation of public participation towards sustainable water management: an institutional perspective.

Sustainable water management has been on the global agenda for more than thirty years. The United Nations Conference for Water was held in 1977 as the initial effort on an international level. However, water issues faded from the agenda until the 1990s, at which time they gained a new impetus. Through the International Conference on Water and the Environment, which proved to be a major initiative for action through the Dublin Principles (ICWE 1992). The Dublin Principles outlined the challenging need to integrate the following dimensions of sustainable water management:
Computation of optimal switching patterns for voltage-controlled inverters using neural-network software

The feasibility of neural networks for optimal voltage control of three-phase power inverters was investigated. The case study involved elimination of the 5th, 7th, and 11th harmonics of the output voltage using four switching angles per quarter-cycle of the output frequency. Regular and sparse neural networks were experimented with. The regular network requires training, but the number of neurons is low. Training is not necessary for the sparse network which can be synthesized using a simple formula. The sparse network also offers higher accuracy than the regular network, at the expense of a significantly higher neuron count. Results of computer simulations have confirmed the viability of the proposed technique. <<ETX>>
ILP modeling of many-to-many replicated multimedia communication

On-line communication services were evolving from a simple text-based chats towards sophisticated videopresence appliances. The bandwidth consumption of those services is constantly growing due to the technology development and high user and business needs. That fact leads us to implement optimization mechanisms into the multimedia communication scenarios. In this paper, the authors concentrate on many-to-many (m2m) communication, that is mainly driven by the growing popularity of on-line conferences and telepresence applications. An overlay model where m2m flows are optimally established on top of a given set of network routes is formulated and a joint model where the network routes and the m2m flows are jointly optimized. In the models, the traffic traverses through replica servers, that are responsible for stream aggregation and compression. Models for both predefined replica locations and optimized server settlement are presented. Each model is being followed by a comprehensive description and is based on real teleconference systems.
Watermarking digital image and video data

A chuck has a chuck body rotatable about an axis and formed with a plurality of angularly spaced radially extending inner guides and a plurality of angularly spaced and radially extending outer guides. An inner jaw part wholly received within the chuck body is radially displaceable in each of the inner guides and an outer jaw part projecting axially from the chuck body is radially displaceable in each of the outer guides. An operating element is engaged with all of the inner jaw parts to jointly radially displace them. A coupling member is axially displaceable in each of the inner jaw parts between a coupling position in which each of the inner parts is locked for joint radial movement with the respective outer part and an axially offset decoupling position allowing relative radial displacement of the outer jaw parts and the respective inner jaw parts. These coupling members may be independently operable, or jointly operable by means of a cam ring.
Hierarchical Neural Networks for Behavior-Based Decision Making

This paper introduces the concept of Hierarchical Neural Networks as a viable strategy for behaviorbased decision making in certain contexts. Hierarchical Neural Networks, or HNNs, refers in this case to a system in which multiple neural networks are connected in a manner similar to an acyclic graph. In this way, responsibility can be divided between each neural network in every layer simplifying the vector of inputs, of outputs, and the overall complexity of each network resulting in improved performance. This approach is shown to outperform a single neural network when both systems are tasked to learn a survival strategy incorporating several behaviors in a real-time environment.
Evolutionary algorithm based neural network controller with selective sensor usage for autonomous mobile robot navigation

This paper deals with designing a neural network based navigator that is optimized in a user-defined sense for a mobile robot using ultrasonic sensors to travel to a goal position safely and efficiently without any prior map of the environment. The neural network has a dynamically reconfigurable structure that not only can optimize the weights but also the input sensory connectivity in order to meet any user-defined objective. Further, in order to enhance generalization capability of a single network, a modular network is used in which each network module is optimized for a specific local environment based on environment classification. After training all the modules, competitive and cooperative module coordination methods are applied and compared. Both computer simulation and real experiments show the effective performance of the algorithm.
İnternet Ortamındaki Tüketici Yorumlarından Özet Bilgi Çıkarımı

information extraction from consumer's comments on internet media In this study, a system developed to summarize by automatically evaluating comments about product with using text mining techniques will be described. The data has been primarily went through morphological analysis process, because they are texts written in natural language. Words and adjectives meaning positive or negative are determined. They show product features in texts. The tree structure is established according to Turkish grammar rules as subordinate and modified words are designated. The software which uses the depth-first search algorithm on the tree structure is developed. Data from result of software is stored in the SQL database. When any inquiry is made from these data depending on any property of product, numerical information which indicates the degree of satisfaction about this property is obtained.
Control and analysis of the gait of snake robots

Snake's gait enables it to climb a hill or wind up a tree and climbing. Hirose (1987) has long investigated the motion of snakes and made several snake robots. He also showed that the trace of the snake body draws the so called "serpenoid curve". However, the control of the position of the snake head was shown to be difficult. In this paper, we show the dynamical position control of the snake robot and discuss the effect of constraining the trace of the head to a "serpenoid curve".
Empirical CALL evaluation

CALL evaluation is important because it is the most efficient means to prove CALL effectiveness. While both learning process and learning outcome should be investigated in empirical evaluation, the precise relationship between the two needs to be examined closely. Only by doing so can we identify useful CALL design features that facilitate relevant user-computer interaction which can lead to an improved learning outcome. This study examined how certain user actions can affect or predict receptive/productive vocabulary retention in a computer-assisted vocabulary learning (CAVL) program. Results showed that only the user actions which contributed to identifiable levels of vocabulary processing--those at which (a) the initial meaning-form mappings of the vocabulary items are established and (b) the vocabulary items are practiced in different contexts--significantly accounted for vocabulary retention. In addition, user actions accounted for receptive vocabulary retention better than for productive vocabulary retention.
Dynamic Programming with Multiple Candidates and its Applications to Sign Language and Hand Gesture Recognition

Dynamic programming has been widely used to solve various kinds of optimization problems. In this work, we show that two crucial problems in video-based sign language and gesture recognition systems can be attacked by dynamic programming with additional multiple observations. The first problem occurs at the higher (sentence) level. Movement epenthesis [1] (me ), i.e., the necessary but meaningless movement between signs, can result in difficulties in modeling and scalability as the number of signs increases. The second problem occurs at the lower (feature) level. Ambiguity of hand detection and occlusion will propagate errors to the higher level. We construct a novel framework that can handle both of these problems based on a dynamic programming approach. The me has only be modeled explicitly in the past. Our proposed method tries to handle me in a dynamic programming framework where we model the me implicitly. We call this enhanced Level Building (eLB) algorithm. This formulation also allows the incorporation of statistical grammar models such as bigrams and trigrams. Another dynamic programming process that handles the problem of selecting among multiple hand candidates is also included in the feature level. This is different from most of the previous approaches, where a single observation is used. We also propose a grouping process that can generate multiple, overlapping hand candidates. We demonstrate our ideas on three continuous American Sign Language data sets and one hand gesture data set. The ASL data sets include one with a simple background, one with a simple background but with the signer wearing short sleeved clothes, and the last with a complex and changing background. The gesture data set contains color gloved gestures with a complex background. We achieve within 5% performance loss from the automatically chosen me score compared with the manually chosen me score. At the low level, we first over segment each frame to get a list of segments. Then we use a greedy method to group the segments based on different grouping cues. We also show that the performance loss is within 5% when we compare this method with manually selected feature vectors.
An example-based face relighting

In this paper, we propose a new face relighting algorithm powered by a large database of face images captured under various known lighting conditions (a Multi-PIE database). Key insight of our algorithm is that a face can be represented by an assemble of patches from many other faces. The algorithm finds the most similar face patches in the database in terms of the lighting and the appearance. By assembling the matched patches, we can visualize the input face under various lighting conditions. Unlike existing face relighting algorithms, we neither use any kinds of face model nor make a physical assumption. Instead, our algorithm is a data-driven approach, synthesizing the appearance of the image patch using the appearance of the example patch. Using a data-driven approach, we can account for various intrinsic facial features including the non-Lambertian skin properties as well as the hair. Also, our algorithm is insensitive to the face misalignment. We demonstrate the performance of our algorithm by face relighting and face recognition experiments. Especially, the synthesized results show that the proposed algorithm can successfully handle various intrinsic features of an input face. Also, from the face recognition experiment, we show that our method is comparable to the most recent face relighting work.
Fast algo-tectures for discrete wavelet transforms

Here we present an FFT based architecture and algorithm for computing discrete wavelet transform of one dimensional discrete signals. The presented architecture is non recursive unlike dyadic subband decomposition and discrete wavelet transform coefficients at all resolutions can be generated simultaneously without waiting for generation of coefficients at a higher resolution. For long wavelet filters, this architecture is faster than architectures proposed so far, for DWT computation based on time domain convolvers. This architecture can be fully pipelined and complexity of control circuits for this architecture is much lower as compared to time domain convolvers and their systolic array implementations (which involve complex routing of data) proposed before. In time-domain convolution based architectures, with a single set of convolver, computation of DWT for an N-point signal takes a minimum of N cycles, whereas the proposed architecture, with full hardware implementation (no multiplexing of hardware) and when fully pipelined takes only a fraction of N cycles. This architecture will be suitable for analysis of signals like EEG, seismic data, etc., which are quasi-infinite, one dimensional signal streams. The speed advantage comes by using FFT based frequency domain convolutions and also due to consequent introduction of more parallelism.
Time-scale modification with inconsistent constraints

A set theoretic estimation approach is introduced for time-scale modification of complex acoustic signals. The method determines a signal that meets, in a least-squared error sense, desired temporal and spectral envelope constraints that are inconsistent. These constraints are generalized within the set theoretic framework to include other signal characteristics such as instantaneous frequency and group delay. The approach can enhance acoustic signals consisting of closely-spaced sequential time components, and is applicable to biological, underwater, and music sound processing.
Relationships Between Readability of Printed and Crt-Displayed Text

The feasibility of reading assessment and training via CRT-displayed text was investigated by implementing a computer-displayed version of the Nelson-Denny Reading Test. Experiments were conducted to directly assess the effects of CRT display upon reading, rather than lebibility test performance. Results suggested that standard legibility measures (e.g., scanning rate) may be inappropriate criteria in evaluating CRT displays in more usual reading tasks.
A novel clustering-based anonymization approach for graph to achieve Privacy Preservation in Social Network

Serious privacy concern rises with the prosperity of social network applications. To prevent the privacy of vertices or edges associated with entities in a social network from getting re-identified through background information or queries,a novel clustering-based approach is proposed to anonymize vertices and edges. Concepts of vertex similarity matrix and the distance between a vertex and a cluster are defined, based on which a k -anonymized graph approach is presented. The effectiveness of the approach is verified Through experiments that compare the performance of our method with that of SASN, an existing anonymization algorithm .
Performance Analysis of Compressed Sensing Given Insufficient Random Measurements

Most of the literature on compressed sensing has not paid enough attention to scenarios in which the number of acquired measurements is insufficient to satisfy minimal exact reconstruction requirements. In practice, encountering such scenarios is highly likely, either intentionally or unintentionally, that is, due to high sensing cost or to the lack of knowledge of signal properties. We analyze signal reconstruction performance in this setting. The main result is an expression of the reconstruction error as a function of the number of acquired measurements.