waashk/dblp huggingface dataset:
parsing noun phrase structure with ccg. this paper describes a new method for image smoothing. we view the image features as residing on a differential manifold, and we work with a representation based on the exponential map for this manifold (i.e. the map from the manifold to a plane that preserves geodesic distances). on the exponential map we characterise the features using a riemannian weighted mean. we show how both gradient descent and newton's method can be used to find the mean. based on this weighted mean, we develop an edge-preserving filter that combines gaussian and median filters of gray-scale images. we demonstrate our algorithm both on direction fields from shape-from-shading and tensor-valued images
cohesive phrase-based decoding for statistical machine translation. probabilistic risk assessment is a technique to assess the probability of failure or success of a mission. results provided by the risk assessment methodology are used to make decisions concerning choice of upgrades, scheduling of maintenance, decision to launch, etc. however, current pra neglects the contribution of software to the risk of failure of the mission. this paper presents a framework for "integrating software into pra", a methodology for systematic integration of the software contribution to risk in system failure analysis. in particular, we established a software-related failure mode taxonomy and a three-level pra sub-model to account for the impact of software to the classical pra structure. application and validation of the taxonomy are discussed in this paper. future research is also summarized.
a critical reassessment of evaluation baselines for speech summarization. medical systems require the end-to-end high availability and scalability. there are many components in a medical system, and each individual component contributes to the overall system availability and scalability. we present the formulas to assess the overall system availability and capacity planning, and we architect approaches and technologies to make all components highly available in all tiers by clustering, workload management, and replication. we discuss how to unify all medical systems and design the uniform framework for different kinds of medical systems to hook up with this framework so that we can maintain a highly available and highly scalable system with ease. we use the n-tier distributed component based architecture and j2ee technologies as well as web services to interact with .net and others. we discuss how to program in the framework for high availability and scalability and how to upgrade application software without service interruption to achieve continuous availability. we show how to add or reduce hardware capacity in each tier and how to maintain hardware and software without service interruption as well as how to balance the capacity and availability among tiers.
learning bilingual lexicons from monolingual corpora. this paper examines graph-theoretic properties of existing peer-to-peer networks and proposes a new infrastructure based on optimal-diameter de bruijn graphs. since generalized de bruijn graphs exhibit very short average distances and high resilience to node failure, they are well suited for distributed hash tables (dhts). using the example of chord, can, and de bruijn, we study the routing performance, graph expansion, clustering properties, and bisection width of each graph. having confirmed that de bruijn graphs offer the best diameter and highest connectivity among the existing peer-to-peer structures, we offer a very simple incremental building process that preserves optimal properties of de bruijn graphs under uniform user joins/departures. we call the combined peer-to-peer architecture optimal diameter routing infrastructure.
semi-supervised sequential labeling and segmentation using giga-word scale unlabeled data. in many computer vision algorithms, the well known euclidean or ssd (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise is gaussian. however, gaussian noise distribution assumption is often invalid. previous research has found that other metrics such as double exponential metric or cauchy metric provide better results, in accordance with the maximum likelihood approach. in this paper, we examine different error metrics and provide a theoretical approach to derive a rich set of nonlinear estimations. our results on image databases show more robust results are obtained for noise estimation based on the proposed error metric analysis.
phrase table training for precision and recall: what makes a good phrase and a good phrase pair? survivability is a key concern in modern network design in order to achieve fast service restorability against network failures. this paper investigates the problem of survivable dynamic connection provisioning in general telecom backbone networks, which are mesh structured. these networks employ optical fibers, which may fail due to network outages such as fiber cuts, etc. our study applies to survivability of optical wavelength-division multiplexing (wdm) as well as multi-protocol label switching (mpls) networks. for survivability study, we assume differentiated services where connections may have different availability requirements, so they may be provisioned differently with protection (if needed) based on their availability requirements and current network state. therefore, it may be possible that connections with the same source, destination, and availability requirement are provisioned differently (unprotected, shared-path protected, or dedicated-path protected) at different times based on current network state. such differentiated provisioning can provide diverse levels of service performance and achieve network resource optimization flexibly. our main contributions are as follows. first, we develop an analytical model to quantify the availabilities of connections with various protection modes, i.e., unprotected, dedicated-path protected, and shared-path protected. particular emphasis is placed on computing a connection's availability with shared-path protection by employing the link-vector technique, because this technique can maximally explore the sharing potential among backup paths and achieve bandwidth-assignment flexibility. based on the mathematical model, we then present a novel provisioning strategy for dynamic connection requests in which multiple levels of services are provided and different protection schemes may be applied to different connections. the strategy jointly considers both connection availability satisfaction and resource optimizatio- - n. numerical results show very good accuracy of our model and high effectiveness of our provisioning strategy
inducing gazetteers for named entity recognition by large-scale clustering of dependency relations. it is a key problem to approximate data of complex surface in many graphics and image processing applications. it is difficult to compute the control parameters of nurbs (non uniform rational b-spline) effecting the result of fitted shape. an evolutionary programming algorithm is used to optimize the weight and knot by minimizing the sum square error between the fitted and target curve and surface. the results obtained from curves and surfaces shows that comparing to knot optimization, the weight optimization is a better option because knot optimization requires a good initial location of knot vector.
joint processing and discriminative training for letter-to-phoneme conversion. as gene order evolves through a variety of chromosomal rearrangements, conserved segments provide important insight into evolutionary relationships and functional roles of genes. however, gene loss within otherwise conserved segments, as typically occurs following large-scale genome duplication, has received limited algorithmic study. this has been a major impediment to comparative genomics in certain taxa, such as plants and fish. we propose a heuristic algorithm/or the inference of ancestral gene order in a set of related genomes that have undergone large-scale duplication and gene loss. first, approximately conserved (i.e. homologous) segments are identified using pairwise local genome alignment. second, homologous segments are iteratively clustered under the control of two parameters, (1) the minimal required number of shared genes between two clusters and (2) the maximal allowed number of rearrangement breakpoints along the lineage leading to each descendant segment. finally, we compute an estimated ancestral gene order for each cluster that is optimal in some sense. we evaluate the performance of this algorithm on simulated data that models a genome evolving by large-scale duplication, duplicate gene loss, transposition, translocation, and inversion. the results suggest that long segments of ancestral gene order may be reconstructed following moderate levels of rearrangement with only minor loss of accuracy.
measure word generation for english-chinese smt systems. when a data-parallel language like fortran 90 is compiled for a distributed-memory machine, aggregate data objects (such as arrays) are distributed across the processor memories. the mapping determines the amount of residual communication needed to bring operands of parallel operations into alignment with each other. a common approach is to break the mapping into two stages: first, an alignment that maps all the objects to an abstract template, and then a distribution that maps the template to the processors. the authors solve two facets of the problem of finding alignments that reduce residual communication, i.e., determining both the alignments that vary in loops, and the objects that should have replicated alignments. they show that loop-dependent mobile alignment is sometimes necessary for optimum performance, and they provide algorithms with which a compiler can determine good mobile alignments for objects within do loops. they also identify situations in which replicated alignment is either required by the program itself (via spread operations) or can be used to improve performance. an algorithm based on network flow that determines which objects to replicate so as to minimize the total amount of broadcast communication in replication is proposed.
vector-based models of semantic composition. this paper addresses the joint design of multiuser precoding and scheduling for downlink multiuser (mu-) multiple-input-multiple-output (mimo) systems with imperfect channel state information at the transmitter (csit). the major issue is to control the increased co-channel-interference (cci) caused by csit error among users sharing the same time-frequency resource. we propose an advanced joint multiuser precoding and scheduling technique, which uses the statistical information of the csit error to predict the relationship among cci, precoding matrixes and scheduling results, and utilizes such information to improve the precoding and scheduling algorithms for better cci control. simulation results have shown that considerable capacity improvement can be achieved by the proposed technique in the case of imperfect csit.
using adaptor grammars to identify synergies in the unsupervised acquisition of linguistic structure. program phase analysis has many applications in computer architecture design and optimization. recently, there has been a growing interest in employing wavelets as a tool for phase analysis. nevertheless, the examined scope of workload characteristics and the explored benefits due to wavelet-based analysis are quite limited. this work further extends prior research by applying wavelets analysis to abundant types of program execution statistics and quantifying the benefits of wavelet analysis in terms of accuracy, scalability and robustness in phase classification. experimental results on spec cpu 2000 benchmarks show that compared with methods that work in the time domain, wavelet domain phase analysis achieves higher accuracy and exhibits superior scalability and robustness. we examine and contrast the effectiveness of applying wavelets to a wide range of runtime workload execution characteristics. we find that wavelet transform significantly reduces temporal dependence in the sampled workload statistics and therefore simple models which are insufficient in the time domain become quite accurate in the wavelet domain. more attractively, we show that different types of workload execution characteristics in wavelet domain can be assembled together to further improve phase classification accuracy. for long-running, complex and real-world workloads, a scalable phase analysis technique is essential to capture the manifested large-scale program behavior. in this study, we show that such scalability can be achieved by applying wavelet analysis of high dimension sampled workload statistics to alleviate the counter overflow problem which can negatively affect phase classification accuracy. by exploiting the wavelet denoising capability, we show in this paper that phase classification can be performed robustly under program execution variability. to our knowledge, this work presents the first effort on using wavelets to improve scalability and robustness in phase analysis
a new string-to-dependency machine translation algorithm with a target dependency language model. there are almost more than 4,000 sorts of algae which could result in the red tide in the world, but only two or three, named the dominant species, place a premium on red tide at a time. this paper presents a method which uses the hyper-spectral images of different familiar dominant species to train the different networks respectively, then synthesizes the outputs of the networks with the same weight to recognize the red tide. it not only conquers the difficulties that are the selection of the training data and the network's training method, but also improves the generalization ability of the network system effectively. on the other hand, based on the neural network ensembles, the red tide recognition model could be extended easily and need not remodel the other networks. a mass of comparison experiments prove that the method recognizes the red tide and the dominant species effectively
intensional summaries as cooperative responses in dialogue: automation and evaluation. the cdna microarray image technology is a powerful tool for monitoring the expressions of thousands of genes simultaneously. an experiment is comprised of hundreds of images, each image easily over 30mb. since image processing and statistical analysis tools are still under development, the images are always kept. current focus on the development of standards makes efficient data transmission an important problem. though the cost of disk space for storage is decreasing, efficient transmission requires compression. we have developed a partially progressive compression scheme for microarray images, allowing for fast decoding and reprocessing of image subsets. the scheme also permits locally varying image distortion or loss. the degree of loss can be chosen on-line, or be based on local parameters such as the spot intensities, or signal-to-noise ratios. the minimum decodable bitrate depends on the initial choice of parameters. we find that a bitrate of 4.1 bpp (cmp 32 bpp uncompressed) is sufficient for most tasks, such as image segmentation, and gene expression level extraction with a variety of existing methods. the lossless bitrate is about 17.5 bpp, comparable to the state-of-the-art lossless schemes, yet with the added flexibility of a progressive scheme. our scheme has been tested on microarray images from different labs, and on images of varying quality.
combining em training and the mdl principle for an automatic verb classification incorporating selectional preferences. in this paper, we propose an effective coarse-to-fine algorithm to detect text in video. firstly, in coarse-detection section, stroke filter is employed to detect all candidate stroke pixels, and then a fast region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by projection operation. secondly, in fine-detection section, correct text regions are selected from candidate ones by support vector machine (svm) model and stroke features, and text regions in multi-resolution are integrated. finally, the result is optimized significantly according to temporal correlation information. experimental results show that our algorithm achieves real-time performance and is robust for the variation of language, font, size, color and noise of text caused by low frame resolution in video.
contextual preferences. the wireless link with fixed parameters is subject to time-varying channel, and the battery energy is limited in mobile terminals. thus we consider the co-existence of 20 mstti and 5 ms tti in e-dch of td-scdma hsupa. in this paper, based on the comparative study on the link performances with different tti and the analysis on the link simulation results, we discuss the different tti's influence on the terminal power consumption. furthermore, the system level performances of semi-static tti and traditional single tti configurations are compared. finally the conclusions are drawn: 20 ms and 5 mstti should co-exist in td-scdma hsupa, and be adopted respectively in different channel condition to save the terminal power. the data throughput of semi-static tti configuration is greater than those of both single tti configurations, and the semi-static tti selection between 20 ms and 5 ms is implemented according to the moving speed of a specific ue.
robust dialog management with n-best hypotheses using dialog examples and agenda. modelling the immune system plays an important role in the understanding of the computational aspects of the immune system, because most of existing artificial immune system models are designed for a specific application problem and have their advantages and disadvantages. in this paper, an ais model based on ontology is presented which can integrate several ais models and algorithms. secondly, this model has accurate semantic, shared knowledge and understood by computer. with the guidance of ais ontolgy, more and more effectual ais models can be created and implement evolution dynamicly.
a re-examination of query expansion using lexical resources. fluidification constitutes a relaxation technique to study discrete event systems through a continuous approximated model, thus avoiding the state explosion problem. in this paper, the approximation by timed continuous petri nets under infinite server semantics is studied. the main contribution of this work is the addition of gaussian noise in order to obtain a better (but stochastic) approximation when synchronizations are important.
linguistically motivated features for enhanced back-of-the-book indexing. a new second-order bandpass sigma-delta modulator employing a micro-mechanical resonator is presented. the micro-mechanical resonator is used to replace its electronic counterpart for its high q value. the design is based on the pulse-invariant transform and multi-feedback technique. a compensation circuit is proposed to cancel the anti-resonance in the micro-mechanical resonator in order to obtain the desired transfer function. the modulator is implemented in a 0.6 &mu;m cmos process with an external clamped-clamped beam micro-mechanical resonator. simulations show that a 47 db dynamic range can be achieved in a 200 khz bandwidth centered at 8 mhz when sampled at 32 mhz.
analyzing the errors of unsupervised learning. establishing structure-function relationships on the proteomic scale is a unique challenge faced by bioinformatics and molecular biosciences. large protein families represent natural libraries of analogues of a given catalytic or protein function, thus making them ideal targets for the investigation of structure-function relationships in proteins. to this end, we have developed a new technique for analyzing large amounts of detailed molecular structure information focusing on the functional centers of homologous proteins. our approach uses unsupervised machine learning, in particular, self-organizing maps. the information captured by a self-organizing map and stored in its reference models highlights the essential structure of the proteins under investigation and can be effectively used to study detailed structural differences and similarities among homologous proteins. our preliminary results obtained with a prototype based on these techniques demonstrate that we can classify proteins and identify common and unique structures within a family and, more importantly, identify common and unique structural features of different conformations of the same protein. the approach developed here outperforms many of today’s structure analysis tools. these tools are usually either limited by the number of proteins they can process at the same time or they are limited by the structural resolution they can accommodate, that is, many of the structural analysis tools that can handle multiple proteins at the same time limit themselves to secondary structure analysis and therefore miss fine structural nuances within proteins.
integrating graph-based and transition-based dependency parsers. the earliest-deadline-first (edf) scheduling of a sporadic real-time task system on a multiprocessor may require that the total utilization of the task system, usum, not exceed (m + 1)/2 on m processors if every deadline needs to be met. in recent work, we considered the alleviation of this under-utilization for task systems that can tolerate deadline misses by bounded amounts (i.e., bounded tardiness). we showed that if usum &le; m and tasks are not pinned to processors, then the tardiness of each task is bounded under both preemptive and non-preemptive edf. however, the tardiness bounds derived are applicable to every task in the task system, i.e., any task may incur maximum tardiness. in this paper, we consider supporting tasks whose tolerances to tardiness are less than that known to be possible under edf. we propose a new scheduling policy, called edf-hl, which is a variant of edf, and show that under edf-hl, any tardiness, including zero tardiness, can be ensured for a limited number of privileged tasks, and that bounded tardiness can be guaranteed to the remaining tasks if their utilizations are restricted. edf-hl reduces to edf in the absence of privileged tasks. the tardiness bound that we derive is a function of usum, in addition to individual task parameters. hence, tardiness for all tasks can be lowered by lowering usum. a simulation-based evaluation of the tardiness bounds that are possible is provided.
automatic image annotation using auxiliary text information. a novel circuit employing p-type low-temperature poly-si thin-film transistors is introduced for active matrix-organic light-emitting diode (am-oled) circuits to automatically detect short defects and switch to a spare oled. this design maintains the luminance of the oled pixel without changing the driving current in the event of defects. experimental results show that not only is fault tolerance capability obtained during operation, but also a significant amount (around 90%) of power consumption is saved compared with the standard driving circuits.