waashk/dblp huggingface dataset:
human brain myelination from birth to 4.5 years. the myelination of white matter from birth through the first years of life has been studied qualitatively and it is well know the myelination occurs in a orderly and predictable manner, proceeding in a caudocranial direction, from deep to superficial and from posterior to anterior. even if the myelination is a continuous process, it is useful to characterize myelination evolution in normal brain development in order to better study demyelinating diseases. the quantification of myelination has only been studied for neonates. the original contribution of this study is to develop a method to characterize and visualize the myelination pattern using mri data from a group of normal subjects from birth to just over 4 years of age. the method includes brain extraction and tissue classification in addition to the analysis of t2 relaxation times to attempt to separate myelinated and unmyelinated white matter. the results agree previously published qualitative observations.
influence of organ motion and contrast enhancement on image registration. we present an exploration of the interplay between the extent of organ motion and contrast-enhancement on image registration success. a model of a dce mri of the liver incorporating an isotropic elastic non-rigid deformation is used to simulate both breathing and breath-hold data, a volume-preserving modification for tumour regions is included. contrast enhancement is simulated by applying a pharmacokinetic model. for each simulated dataset, a direct fluid registration of each image to the first in the dataset is compared to a contrast-enhancement guided method known as progressive principal component registration (ppcr). analysis of the correction to the deformation fields, tumour volume change and dispersion of joint image histograms are used to show the importance of motion type on ppcr success and of enhancement level on direct fluid registration success. for breathing motion, ppcr registers groups of images to separate locations, but maintains enhancing tumour volume. this is not the case for direct registration with volume changes of up to 7%. for inconsistent breath-hold depth, ppcr out-performs direct registration, particularly for large enhancement levels. analysis of the joint histograms suggests that the generation of target images using ppcr reduces dispersion due to contrast enhancement. since this distinction is not made using direct registration, it is unable to register images when large enhancement intensity changes are present.
localization of pelvic anatomical coordinate system using us/atlas registration for total hip replacement. in total hip replacement (thr) procedures, misalignment of the acetabular component can lead to dislocation and impingement. for the successful alignment of acetabular component, precise estimation of pelvic anatomical coordinate system is necessary. conventional navigation systems use ct scan or fluoroscopy, or involve implanted bone fiducials or invasive probing of bony landmarks to locate the anatomical coordinate. in this paper, an ultrasound-based approach is proposed that exploits prior knowledge about the anatomy of the pelvis in the form of a 3d surface atlas. tracked ultrasound images are utilized to extract sample points from the surface of the pelvis. a generic coordinate system in the specific patient is localized by registering these points to a statistical atlas of the pelvis in which a canonical anatomical coordinate system had been defined. this technique has been evaluated using simulation, dry bone, and cadaver experiments and was able to localize the anatomical coordinate system with the accuracy of about 1 degree.
adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification. the inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. in this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. these are captured using the adaptive wavelet packet transform (adwpt) and local binary patterns (lbps), respectively. these two different textural features are combined together and used for classification. the effect of various dimensionality reduction techniques on classification performance is also investigated. we show that high classification accuracies can be achieved using adwpt. although lbp features do not provide higher overall classification accuracies than adwpt, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.
cooperative robot assistant for retinal microsurgery. this paper describes the development and results of initial testing of a cooperative robot assistant for retinal microsurgery. in the cooperative control paradigm, the surgeon and the robot share control of a tool attached to the robot through a force sensor. the system senses forces exerted by the operator on the tool and uses this information in various control modes to provide smooth, tremor-free, precise positional control and force scaling. the robot manipulator is specifically designed with retinal microsurgery in mind, having high efficacy, flexibility and ergonomics while meeting the accuracy and safety requirements of microsurgery. we have tested this robot on a biological model and we report the results for reliably cannulating ~80 μm diameter veins (equivalent in size to human retinal veins). we also describe improvements to the robot and the experimental setup facilitating more advanced set of experiments.
modelling dynamic fronto-parietal behaviour during minimally invasive surgery - a markovian trip distribution approach. learning to perform minimally invasive surgery (mis) requires considerable attention, concentration and spatial ability. theoretically, this leads to activation in executive control (prefrontal) and visuospatial (parietal) centres of the brain. a novel approach is presented in this paper for analysing the flow of fronto-parietal haemodynamic behaviour and the associated variability between subjects. serially acquired functional near infrared spectroscopy (fnirs) data from fourteen laparoscopic novices at different stages of learning is projected into a low-dimensional `geospace', where sequentially acquired data is mapped to different locations. a trip distribution matrix based on consecutive directed trips between locations in the geospace reveals confluent fronto-parietal haemodynamic changes and a gravity model is applied to populate this matrix. to model global convergence in haemodynamic behaviour, a markov chain is constructed and by comparing sequential haemodynamic distributions to the markov's stationary distribution, inter-subject variability in learning an mis task can be identified.
belief propagation for depth cue fusion in minimally invasive surgery. in minimally invasive surgery, dense 3d surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. despite recent developments in 3d tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. the need for accurate and robust tissue deformation recovery has motivated research into fusing multiple visual cues for depth recovery. in this paper, a markov random field (mrf) based bayesian belief propagation framework has been proposed for the fusion of different depth cues. by using the underlying mrf structure to ensure spatial continuity in an image, the proposed method offers the possibility of inferring surface depth by fusing the posterior node probabilities in a node's markov blanket together with the monocular and stereo depth maps. detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique.
cohabitation and cooperation of chorus and macos. this paper describes experimental work on cohabitation and cooperation between a distributed operating system (chorus1) and an event driven operating system (macos2). our aims were to exploit the graphical and the musical capabilities of macintosh hardware and software directly from chorus applications, while minimizing our efforts in the field of device drivers and hardware interfaces. the work was carried out in four major stages. the first stage was to port the chorus kernel on the macintosh hardware. in the second stage we changed the way chorus managed the hardware in order to keep the macos system alive. conversely, we modified slightly the way chorus was booted so as to present it as an application to macos. this led us to the third stage, which was to share system events (e.g. hardware interrupts) between the two systems. the chorus system allows one to have multiple functions connected to an interrupt. this feature was used to connect both an internal chorus driver and a low level function to an interrupt. the low level function leads to the macos interrupt driver. the fourth stage is currently being carried out. it consists in the design and implementation of an interface permitting user level events (as system calls) to cross the borders of the two systems. this paper describes each stage and draws lessons about system software cohabitation and reusability.
from v to vanguard: the evolution of a distributed object-oriented microkernel interface. the vanguard operating system kernel was designed and implemented as a research testbed for distributed applications and higher-level operating system services. using the design of the v-system as a starting point, we developed an extensible set of operating system services, organized in an object type hierarchy. we also implemented a modular os (micro) kernel that implements these services. an important part of any microkernel design is its exported interface, as the design of this interface affects the ease with which programmers can develop higher-level operating system services on top of the kernel. in this paper we describe several notable features of the vanguard microkernel interfacein particular, its process and object model, its object identification scheme, and its use of group communication. we show how these features lead to a simple yet powerful interface that avoids the need to provide an excessive number of operations.
object-oriented transaction processing in the keykos microkernel. three major technological directions in computer technology are transaction processing, object orientation, and microkernel operating systems. the keykos operating system and the keytxf transaction processing system combine all three of these technologies. the design of keykos directly provides operating system level objects on a microkernel base. in order to maintain the integrity of these objects, keykos takes periodic checkpoints of the entire system. in addition, keykos provides facilities for transaction processing which achieve very high transaction rates. object oriented technology facilitates construction and reuse of transaction applications. this paper describes how these ideas are combined in the keykos system.
a flexible external paging interface. in this paper we describe an aspect of the spring virtual memory system that was influenced by the distributed object-oriented architecture of spring. the virtual memory system supports external pagers like those provided in the mach operating system, yet the architecture is more flexible and provides better caching opportunities than is possible in other systems. a novel aspect of the architecture is the separation of the memory abstraction from the interface that provides the paging operations. this separation provides considerable caching opportunities in our file system, and it facilitates our extensible stackable file system architecture. the virtual memory architecture described in this paper is implemented and has been in use for over three years as part of the experimental spring operating system.
kernel support for the wisconsin wind tunnel. this paper describes a kernel interface that provides an untrusted user-level process (an executive) with protected access to memory management functions, including the ability to create, manipulate, and execute within subservient contexts (address spaces). page motion callbacks not only give the executive limited control over physical memory management, but also shift certain responsibilities out of the kernel, greatly reducing kernel state and complexity. the executive interface was motivated by the requirements of the wisconsin wind tunnel (wwt), a system for evaluating cache-coherent shared-memory parallel architectures. wwt uses the executive interface to implement a fine-grain user-level extension of li's shared virtual memory on a thinking machines cm-5, a message-passing multicomputer. however, the interface is sufficiently general that an executive could act as a multiprogrammed operating system, exporting an alternative interface to the threads running in its subservient contexts. the executive interface is currently implemented as an extension to cmost, the standard operating system for the cm-5. in cmost, policy decisions are made on a central, distinct control processor (cp) and broadcast to the processing nodes (pns). the pns execute a minimal kernel sufficient only to implement the cp's policy. while this structure efficiently supports some parallel application models, the lack of autonomy on the pns restricts its generality. adding the executive interface provides limited autonomy to the pns, creating a structure that supports multiple models of application parallelism. this structure, with autonomy on top of centralization, is in stark contrast to most microkernel-based parallel operating systems in which the nodes are fundamentally autonomous.
user level ipc and device management in the raven kernel. the increasing bandwidth of networks and storage devices in recent years has placed greater emphasis on the performance of low level operating system services. data must be delivered between hardware devices and user applications in an efficient matter. motivated by the need for low overhead operating system services, the raven kernel utilizes user level implementation techniques to reduce kernel intervention for many common services. in particular, our user level send/receive/reply communication implementation generates no kernel interactions per iteration in the best case, and two kernel interactions in the worst case. in more general cases, we observe approximately one kernel interaction for every two send/receive/reply iterations. device driver support is also done entirely at the user level reducing copy costs and context switching.
fast interrupt priority management in operating system kernels. in this paper we describe a new, low-overhead technique for manipulating processor interrupt state in an operating system kernel. both uniprocessor and multiprocessor operating systems protect against uniprocessor deadlock and data corruption by selectively enabling and disabling interrupts during critical sections. this happens frequently during latency-critical activities such as ipc, scheduling, and memory management. unfortunately, the cycle cost of modifying the interrupt mask has increased by an order of magnitude in recent processor architectures. in this paper we describe optimistic interrupt protection, a technique which substantially reduces the cost of interrupt masking by optimizing mask manipulation for the common case of no interrupts. we present results for the mach 3.0 microkernel_operating_system,_although_the technique is applicable to other kernel architectures, both micro and monolithic, that rely on interrupts to manage devices.
design and implementation of an object-oriented 64-bit single address space microkernel. in the mid eighties, the system architecture research centre at city university developed a message-passing, unix compliant micro kernel (meshix) for our own scalable distributed memory architecture (topsy). over the last two years we have been engaged in a research programme aimed at learning from this experience, and developing a new operating system based on these lessons. the result is the angel microkernel. this paper sets out the lessons we have learnt from meshix, how this has influenced the design of angel and outlines our current design of angel and its c++ implementation. we will also describe our future plans and hopes for angel, and the lessons that we have learnt from the design and implementation process.
identifying cognates by phonetic and semantic similarity. i present a method of identifying cognates in the vocabularies of related languages. i show that a measure of phonetic similarity based on multivalued features performs better than "orthographic" measures, such as the longest common subsequence ratio (lcsr) or dice's coefficient. i introduce a procedure for estimating semantic similarity of glosses that employs keyword selection and wordnet. tests performed on vocabularies of four algonquian languages indicate that the method is capable of discovering on average nearly 75% percent of cognates at 50% precision.
chunking with support vector machines. we apply support vector machines (svms) to identify english base phrases (chunks). svms are known to achieve high generalization performance even with input data of high dimensional feature spaces. furthermore, by the kernel principle, svms can carry out training with smaller computational overhead independent of their dimensionality. we apply weighted voting of 8 svms-based systems trained with distinct chunk representations. experimental results show that our approach achieves higher accuracy than previous approaches.
a corpus-based account of regular polysemy: the case of context-sensitive adjectives. in this paper we investigate polysemous adjectives whose meaning varies depending on the nouns they modify (e.g., fast). we acquire the meanings of these adjectives from a large corpus and propose a probabilistic model which provides a ranking on the set of possible interpretations. we identify lexical semantic information automatically by exploiting the consistent correspondences between surface syntactic cues and lexical meaning. we evaluate our results against paraphrase judgments elicited experimentally from humans and show that the model's ranking of meanings correlates reliably with human intuitions: meanings that are found highly probable by the model are also rated as plausible by the subjects.
a finite-state approach to machine translation. the problem of machine translation can be viewed as consisting of two subproblems (a) lexical selection and (b) lexical reordering. we propose stochastic finite-state models for these two subproblems in this paper. stochastic finite-state models are efficiently learnable from data, effective for decoding and are associated with a calculus for composing models which allows for tight integration of constraints from various levels of language processing. we present a method for learning stochastic finite-state models for lexical choice and lexical reordering that are trained automatically from pairs of source and target utterances. we use this method to develop models for english-japanese translation and present the performance of these models for translation on speech and text. we also evaluate the efficacy of such a translation model in the context of a call routing task of unconstrained speech utterances.
learning optimal dialogue management rules by using reinforcement learning and inductive logic programming. developing dialogue systems is a complex process. in particular, designing efficient dialogue management strategies is often difficult as there are no precise guidelines to develop them and no sure test to validate them. several suggestions have been made recently to use reinforcement learning to search for the optimal management strategy for specific dialogue situations. these approaches have produced interesting results, including applications involving real world dialogue systems. however, reinforcement learning suffers from the fact that it is state based. in other words, the optimal strategy is expressed as a decision table specifying which action to take in each specific state. it is therefore difficult to see whether there is any generality across states. this limits the analysis of the optimal strategy and its potential for re-use in other dialogue situations. in this paper we tackle this problem by learning rules that generalize the state-based strategy. these rules are more readable than the underlying strategy and therefore easier to explain and re-use. we also investigate the capability of these rules in directing the search for the optimal strategy by looking for generalization whilst the search proceeds.
identifying user corrections automatically in spoken dialogue systems. we present results of machine learning experiments designed to identify user corrections of speech recognition errors in a corpus collected from a train information spoken dialogue system. we investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. our best performing features reduce classification error from baselines of 25.70-28.99% to 15.72%.
unsupervised learning of name structure from coreference data. we present two methods for learning the structure of personal names from unlabeled data. the first simply uses a few implicit constraints governing this structure to gain a toehold on the problem --- e.g., descriptors come before first names, which come before middle names, etc. the second model also uses possible coreference information. we found that coreference constraints on names improve the performance of the model from 92.6% to 97.0%. we are interested in this problem in its own right, but also as a possible way to improve named entity recognition (by recognizing the structure of different kinds of names) and as a way to improve noun-phrase coreference determination.
edit detection and parsing for transcribed speech. we present a simple architecture for parsing transcribed speech in which an edited-word detector first removes such words from the sentence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. the edit detector achieves a misclassification rate on edited words of 2.2%. (the null-model, which marks everything as not edited, has an error rate of 5.9%.) to evaluate our parsing results we introduce a new evaluation metric, the purpose of which is to make evaluation of a parse tree relatively indifferent to the exact tree position of edited nodes. by this metric the parser achieves 85.3% precision and 86.5% recall.
multipath translation lexicon induction via bridge languages. this paper presents a method for inducing translation lexicons based on transduction models of cognate pairs via bridge languages. bilingual lexicons within languages families are induced using probabilistic string edit distance models. translation lexicons for arbitrary distant language pairs are then generated by a combination of these intra-family translation models and one or more cross-family on-line dictionaries. up to 95% exact match accuracy is achieved on the target vocabulary (30-68% of inter-family test pairs). thus substantial portions of translation lexicons can be generated accurately for languages where no bilingual dictionary or parallel corpora may exist.
corpus-based np modifier generation. this paper describes how we annotated and analysed the np modifiers in a corpus of museum descriptions to discover rules for the selection and realisation of such modifiers, in particular non-referring ones. we implemented the regularities into an extension of the ilex system to generate complex nps capable of serving multiple communicative goals.