In recent years, enterprises have been targeted by advanced adversaries who leverage creative ways to infiltrate their systems and move laterally to gain access to critical data. One increasingly common evasive method is to hide the malicious activity behind a benign program by using tools that are already installed on user computers. These programs are usually part of the operating system distribution or another user-installed binary, therefore this type of attack is called “Living-Off-The-Land”. Detecting these attacks is challenging, as adversaries may not create malicious files on the victim computers and anti-virus scans fail to detect them.

This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time.

We propose a method for the synthesis of the magnitudes of Head-related Transfer Functions (HRTFs) using a sparse representation of anthropometric features. Our approach treats the HRTF synthesis problem as finding a sparse representation of the subject's anthropometric features w.r.t. the anthropometric features in the training set. The fundamental assumption is that the magnitudes of a given HRTF set can be described by the same sparse combination as the anthropometric data. Thus, we learn a sparse vector that represents the subject's anthropometric features as a linear superposition of the anthropometric features of a small subset of subjects from the training data. Then, we apply the same sparse vector directly on the HRTF tensor data. For evaluation purpose we use a new dataset, containing both anthropometric features and HRTFs. We compare the proposed sparse representation based approach with ridge regression and with the data of a manikin (which was designed based on average anthropometric data), and we simulate the best and the worst possible classifiers to select one of the HRTFs from the dataset. For instrumental evaluation we use log-spectral distortion. Experiments show that our sparse representation outperforms all other evaluated techniques, and that the synthesized HRTFs are almost as good as the best possible HRTF classifier.

We propose a method to aggregate noisy ordinal labels collected from a crowd of workers or annotators. Eliciting ordinal labels is important in tasks such as judging web search quality and consumer satisfaction. Our method is motivated by the observation that workers usually have difficulty distinguishing between two adjacent ordinal classes whereas distinguishing between two classes which are far away from each other is much easier. We develop the method through minimax conditional entropy subject to constraints which encode this observation. Empirical evaluations on real datasets demonstrate significant improvements over existing methods.

Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-linear stacking methods for ensemble learning with applications specifically to speechclass posterior probabilities as computed by the convolutional, recurrent, and fully-connected deep neural networks. Convex optimization problems are formulated and solved, with analytical formulas derived for training the ensemble-learning parameters. Experimental results demonstrate a significant increase in phone recognition accuracy after stacking the deep learning subsystems that use different mechanisms for computing high-level, hierarchical features from the raw acoustic signals in speech.

This paper introduces Trill -- a new query processor for analytics. Trill fulfills a combination of three requirements for a query processor to serve the diverse big data analytics space: (1) Query Model : Trill is based on a tempo-relational model that enables it to handle streaming and relational queries with early results, across the latency spectrum from real-time to offline; (2) Fabric and Language Integration : Trill is architected as a high-level language library that supports rich data-types and user libraries, and integrates well with existing distribution fabrics and applications; and (3) Performance : Trill's throughput is high across the latency spectrum. For streaming data, Trill's throughput is 2-4 orders of magnitude higher than comparable streaming engines. For offline relational queries, Trill's throughput is comparable to a major modern commercial columnar DBMS. Trill uses a streaming batched-columnar data representation with a new dynamic compilation-based system architecture that addresses all these requirements. In this paper, we describe Trill's new design and architecture, and report experimental results that demonstrate Trill's high performance across diverse analytics scenarios. We also describe how Trill's ability to support diverse analytics has resulted in its adoption across many usage scenarios at Microsoft.

In this paper, we propose an image-based detection method to identify web-based scareware attacks that is robust to evasion techniques. We evaluate the method on a large-scale data set that resulted in an equal error rate of 0.018%. Conceptually, false positives may occur when a visual element, such as a red shield, is embedded in a benign page. We suggest including additional orthogonal features or employing graders to mitigate this risk. A novel visualization technique is presented demonstrating the acquired classifier knowledge on a classified screenshot.

Exploratory analysis on big data requires us to rethink data management across the entire stack -- from the underlying data processing techniques to the user experience. We demonstrate Stat! -- a visualization and analytics environment that allows users to rapidly experiment with exploratory queries over big data. Data scientists can use Stat! to quickly refine to the correct query, while getting immediate feedback after processing a fraction of the data. Stat! can work with multiple processing engines in the backend; in this demo, we use Stat! with the Microsoft StreamInsight streaming engine. StreamInsight is used to generate incremental early results to queries and refine these results as more data is processed. Stat! allows data scientists to explore data, dynamically compose multiple queries to generate streams of partial results, and display partial results in both textual and visual form.

Deep Neural Networks (DNNs) have shown remarkable success in pattern recognition tasks. However, parallelizing DNN training across computers has been difficult. We present the Deep Stacking Network (DSN), which overcomes the problem of parallelizing learning algorithms for deep architectures. The DSN provides a method of stacking simple processing modules in buiding deep architectures, with a convex learning problem in each module. Additional fine tuning further improves the DSN, while introducing minor non-convexity. Full learning in the DSN is batch-mode, making it amenable to parallel training over many machines and thus be scalable over the potentially huge size of the training data. Experimental results on both the MNIST (image) and TIMIT (speech) classification tasks demonstrate that the DSN learning algorithm developed in this work is not only parallelizable in implementation but it also attains higher classification accuracy than the DNN.

An important way to make large training sets is to gather noisy labels from crowds of nonexperts. We propose a minimax entropy principle to improve the quality of these labels. Our method assumes that labels are generated by a probability distribution over workers, items, and labels. By maximizing the entropy of this distribution, the method naturally infers item confusability and worker expertise. We infer the ground truth by minimizing the entropy of this distribution, which we show minimizes the Kullback-Leibler (KL) divergence between the probability distribution and the unknown truth. We show that a simple coordinate descent scheme can optimize minimax entropy. Empirically, our results are substantially better than previously published methods for the same problem.

In this work, we present a novel means of browsing recorded audio conversations. The method we develop produces scalable summaries of the recognized speech, in which we can increase the amount of text continuously with the desired level of detail to best fill the available space. We present an interface in which a user can view an entire conversation in one screen, but can also quickly zoom in to see the full transcript; the corresponding audio can be easily played as well. The scaling is achieved via a combination of topic segmentation and informative phrase selection, where the threshold for informativeness decreases with increasing level of detail. Finally, we evaluate our method and interface against a baseline interface with a user study.

Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges. Often advertisers wish to either target (or avoid) some specific content on web pages which may appear only in a small part of the page. Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training. In this paper we investigate ways to learn for sub-document classification when only page level labels are available - these labels only indicate if the relevant content exists in the given page or not. We propose the application of multiple-instance learning to this task to improve the effectiveness of traditional methods. We apply sub-document classification to two different problems in contextual advertising. One is "sensitive content detection" where the advertiser wants to avoid content relating to war, violence, pornography, etc. even if they occur only in a small part of a page. The second problem involves opinion mining from review sites - the advertiser wants to detect and avoid negative opinion about their product when positive, negative and neutral sentiments co-exist on a page. In both these scenarios we present experimental results to show that our proposed system is able to get good block level labeling for free and improve the performance of traditional learning methods.

This paper presents a redundant bit vector approach for indexing and retrieval of handwritten words captured using an electronic pen or tablet. Handwritten words (cursive or print) are first segmented into strokes and each stroke is featurized using a neural network. Oriented principal component analysis (OPCA) is used for dimensionality reduction while ensuring robustness to handwriting variation (noise). Redundant bit vectors are used to index the resulting low dimensional representations for efficient storage and retrieval. Experimental results on large datasets with 898,652 handwritten words show good retrieval performance that is robust to handwriting variations and generalizes well over different writers and writing styles.

Web servers on the Internet need to maintain high reliability, but the cause of intermittent failures of web transactions is non-obvious. We use Bayesian inference to diagnose problems with web services. This diagnosis problem is far larger than any previously attempted: it requires inference of 10^4 possible faults from 10^5 observations. Further, such inference must be performed in less than a second. Inference can be done at this speed by combining a variational approximation, a mean-field approximation, and the use of stochastic gradient descent to optimize a variational cost function. We use this fast inference to diagnose a time series of anomalous HTTP requests taken from a real web service. The inference is fast enough to analyze network logs with billions of entries in a matter of hours.

This paper introduces a new algorithm for implementing subband, adaptive filtering using recursive least squares (RLS) with round robin regularization. We show that modern microprocessors with SEMD (single instruction, multiple data) instructions can now implement RLS for practical problems thereby avoiding the numerical stability issues associated with fast RLS (FRLS). The desired signal may be multichannel as in the stereo, acoustic echo cancellation (AEC) problem where the separate channels of the playback signals are often highly correlated. In this case, the recursive computation of the inverse correlation matrix in RLS will diverge. To avoid this problem, we extend adaptive subband RLS to include round robin regularization. The new, regularized RLS (RRLS) algorithm has been implemented in real-time on a personal computer (PC) for the stereo AEC problem and performs well in typical PC scenarios

In this paper, we present a speaker identification algorithm for a microphone array based on a first-order joint hidden Markov model (HMM) where the observations correspond to the angle of arrival of the speech and the speech spectrum. The goal of the research is to investigate whether including angle of arrival information improves the speaker identification error rates compared to an algorithm based on the speech spectrum only. The spectral model consists of a Gaussian mixture model (GMM) using multiple discriminant analysis (MDA) coefficients and the angle model includes a separate histogram for each participant. The convergence time of the joint HMM is improved by estimating the GMM for each of the meeting participants prior to the start of the meeting and initializing each participant's spectral GMM in the joint HMM to the pretrained parameter values. The performance of the algorithm is analyzed from data collected during live meetings recorded using an eight element, circular microphone array. For meetings where the participants are stationary, the results show significant improvement over a single channel speaker ID algorithms based on spectrum only

Audio fingerprinting is a powerful tool for identifying file-based or streaming audio, using a database of fingerprints. The paper presents two new applications of audio fingerprinting: duplicate detection, whose goal is to identify duplicate audio clips in a set, even if they differ in compression quality or duration, and thumbnail generation, which aims to provide a representative short clip of a music track. Neither application requires an external database of fingerprints. Thanks to the robustness of the fingerprinting engine, both applications perform well; the duplicate detector has a false positive rate that is conservatively bounded above by 1% on a very large data set, and the thumbnail generator significantly outperforms using a fixed window.

Images magnified by standard methods display a degradation of detail that is particularly noticeable in the blurry edges of text. Current super-resolution algorithms address the lack of sharpness by filling in the image with probable details. These algorithms break the outlines of text. Our novel algorithm for super-resolution of text magnifies images in real-time by interpolation with a variable linear filter. The coefficients of the filter are determined nonlinearly from the neighborhood to which it is applied. We train the mapping that defines the coefficients to specifically enhance edges of text, producing a conservative algorithm that infers the detail of magnified text. Possible applications include resizing web page layouts or other interfaces, and enhancing low resolution camera captures of text. In general, learning spatially-variable filters is applicable to other image filtering tasks.

In this paper, we show the novel application of hidden conditional random fields (HCRFs) – conditional random fields with hidden state sequences – for modeling speech. Hidden state sequences are critical for modeling the non-stationarity of speech signals. We show that HCRFs can easily be trained using the simple direct optimization technique of stochastic gradient descent. We present the results on the TIMIT phone classification task and show that HCRFs outperforms comparable ML and CML/MMI trained HMMs. In fact, HCRF results on this task are the best single classifier results known to us. We note that the HCRF framework is easily extensible to recognition since it is a state and label sequence modeling technique. We also note that HCRFs have the ability to handle complex features without any change in training procedure. 1.

A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MILBoost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier.

We introduce an elegant and novel design for a speech detector which estimates the probability of the presence of speech in each time-frequency bin, as well as in each frame. The proposed system uses discriminative estimators based on logistic regression, and incorporates spectral and temporal correlations in the same framework. The detector is flexible enough to be configured in a single level or a "stacked" bilevel architecture depending on the needs of the application. An important part of the proposed design is the use of a new set of features: the normalized logarithm of the estimated posterior signal-to-noise ratio. These can be easily and automatically generated by tracking the noise spectrum online. We present results on the AURORA database to demonstrate that the overall design is simple, flexible and effective.

This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MTIVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than random sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.

In this paper, we introduce a new framework for speech detection using convolutional networks. We propose a network architecture that can incorporate long and short-term temporal and spectral correlations of speech in the detection process. The proposed design is able to address many shortcomings of existing speech detectors in a unified new framework: First, it improves the robustness of the system to environmental variability while still being fast to evaluate. Second, it allows for a framework that is extendable to work under different time-scales for different applications. Finally, it is discriminative and produces reliable estimates of the probability of presence of speech in each frame for a wide variety of noise conditions. We propose that the inputs to the system be features that are measures of the true signal-to-noise ratio of a set of frequency bands of the signal. These can be easily and automatically generated by tracking the noise spectrum online. We present preliminary results on the AURORA database to demonstrate the effectiveness of the detector over conventional Gaussian detectors. 1.

No abstract available.

Neural networks are a powerful technology forclassification of visual inputs arising from documents.However, there is a confusing plethora of different neuralnetwork methods that are used in the literature and inindustry. This paper describes a set of concrete bestpractices that document analysis researchers can use toget good results with neural networks. The mostimportant practice is getting a training set as large aspossible: we expand the training set by adding a newform of distorted data. The next most important practiceis that convolutional neural networks are better suited forvisual document tasks than fully connected networks. Wepropose that a simple do-it-yourself implementation ofconvolution with a flexible architecture is suitable formany visual document problems. This simpleconvolutional neural network does not require complexmethods, such as momentum, weight decay, structure-dependentlearning rates, averaging layers, tangent prop,or even finely-tuning the architecture. The end result is avery simple yet general architecture which can yieldstate-of-the-art performance for document analysis. Weillustrate our claims on the MNIST set of English digitimages.

In this paper, we describe RARE (Robust Audio Recognition Engine): a system for identifying audio streams and files. RARE can be used in a variety of applications: from enhancing the consumer listening experience to cleaning large audio databases. RARE was designed with two key qualities in mind: robustness to distortion of the audio, and lookup speed. RARE identifies audio clips in a stream against a database of 1/4 million songs in real time using approximately 10% CPU on an 850 MHz P3, and with a measured false positive rate of 1.5x10-8 per clip, per database entry, at a false negative rate of 0.2% per clip. We demo RARE in real-time on a stream and on distorted files.

This paper applies fast sparse multidimensional scaling (MDS) to a large graph of music similarity, with 267K vertices that represent artists, al- bums, and tracks; and 3.22M edges that represent similarity between those entities. Once vertices are assigned locations in a Euclidean space, the locations can be used to browse music and to generate playlists. MDS on very large sparse graphs can be effectively performed by a family of algorithms called Rectangular Dijsktra (RD) MDS algorithms. These RD algorithms operate on a dense rectangular slice of the distance matrix, created by calling Dijsktra a constant number of times. Two RD algorithms are compared: Landmark MDS, which uses the Nystr(cid:246)m ap- proximation to perform MDS; and a new algorithm called Fast Sparse Embedding, which uses FastMap. These algorithms compare favorably to Laplacian Eigenmaps, both in terms of speed and embedding quality.

Mapping audio data to feature vectors for the classification, retrieval or identification tasks presents four principal challenges. The dimensionality of the input must be significantly reduced; the resulting features must be robust to likely distortions of the input; the features must be informative for the task at hand; and the feature extraction operation must be computationally efficient. We propose distortion discriminant analysis (DDA), which fulfills all four of these requirements. DDA constructs a linear, convolutional neural network out of layers, each of which performs an oriented PCA dimensional reduction. We demonstrate the effectiveness of DDA on two audio fingerprinting tasks: searching for 500 audio clips in 36 h of audio test data; and playing over 10 days of audio against a database with approximately 240 000 fingerprints. We show that the system is robust to kinds of noise that are not present in the training procedure. In the large test, the system gives a false positive rate of 1.5 /spl times/ 10/sup -8/ per audio clip, per fingerprint, at a false negative rate of 0.2% per clip.

A key problem faced by audio identification, classification, and retrieval systems is the mapping of high-dimensional audio input data into informative lower-dimensional feature vectors. This paper explores an automatic dimensionality reduction algorithm called Distortion Discriminant Analysis (DDA). Each layer of DDA projects its input into directions which maximize the SNR for a given set of distortions. Multiple layers efficiently extract features over a wide temporal window. The audio input to DDA undergoes perceptually-relevant preprocessing and de-equalization, to further suppress distortions. We apply DDA to the task of identifying audio clips in an incoming audio stream, based on matching stored audio fingerprints. We show excellent test results on matching input fingerprints against 36 hours of stored audio data.

We present a new method for speech denoising and robust speech recognition. Using the framework of probabilistic models allows us to integrate detailed speech models and models of realistic non-stationary noise signals in a principled manner. The framework transforms the denoising problem into a problem of Bayes-optimal signal estimation, producing minimum mean square error estimators of desired features of clean speech from noisy data. We describe a fast and efficient implementation of an algorithm that computes these estimators. The effectiveness of this algorithm is demonstrated in robust speech recognition experiments, using the Wall Street Journal speech corpus and Microsoft Whisper large-vocabulary continuous speech recognizer. Results show significantly lower word error rates than those under noisy-matched condition. In particular, when the denoising algorithm is applied to the noisy training data and subsequently the recognizer is retrained, very low error rates are obtained.

This paper presents AutoDJ: a system for automatically generating mu- sic playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution of functions that generates the learned function. For playlist generation, AutoDJ learns a kernel from a large set of albums. This learned kernel is shown to be more effective at predicting users’ playlists than a reasonable hand-designed kernel.

Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

This paper presents a unified probabilistic framework for denoising and dereverberation of speech signals. The framework transforms the denois(cid:173) ing and dereverberation problems into Bayes-optimal signal estimation. The key idea is to use a strong speech model that is pre-trained on a large data set of clean speech. Computational efficiency is achieved by using variational EM, working in the frequency domain, and employing conjugate priors. The framework covers both single and multiple micro(cid:173) phones. We apply this approach to noisy reverberant speech signals and get results substantially better than standard methods.

Displays with repeating patterns of colored subpixels gain spatial resolution by setting individual subpixels rather than by setting entire pixels. This paper describes optimal filtering that produces subpixel values from a high-resolution input image. The optimal filtering is based on an error metric inspired by psychophysical experiments. Minimizing the error metric yields a linear system of equations, which can be expressed as a set of filters. These filters provide the same quality of font display as standard anti-aliasing at a point size 25% smaller. This optimization forms the filter design framework for Microsoft's ClearType.

Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1 Keywords Text categorization, classification, support vector machines, machine learning, information management.

We describe a system that can track a hand in a sequence of video frames and recognize hand gestures in a user-independent manner. The system locates the hand in each video frame and determines if the hand is open or closed. The tracking system is able to track the hand to within ±10 pixels of its correct location in 99.7% of the frames from a test set containing video sequences from 18 dif(cid:173) ferent individuals captured in 18 different room environments. The gesture recognition network correctly determines if the hand being tracked is open or closed in 99.1 % of the frames in this test set . The system has been designed to operate in real time with existing hardware.

Abstract We develop physically‐based graphics models of non‐rigid objects capable of heat conduction, thermoelasticity, melting and fluid‐like behaviour in the molten state. These deformable models feature non‐rigid dynamics governed by Lagrangian equations of motion and conductive heat transfer governed by the heat equation for non‐homogeneous, non‐isotropic media. In its solid state, the discretized model is an assembly of hexahedral finite elements in which thermoelastic units interconnect particles situated in a lattice. The stiffness of a thermoelastic unit decreases as its temperature increases, and the unit fuses when its temperature exceeds the melting point. The molten state of the model involves a molecular dynamics simulation in which ‘fluid’ particles that have broken free from the lattice interact through long‐range attraction forces and short‐range repulsion forces. We present a physically‐based animation of a thermoelastic model in a simulated physical world populated by hot constraint surfaces.

We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.

Simulating flexible models can create aesthetic motion for computer animation. Animators can control these motions through the use of constraints on the physical behavior of the models. This paper shows how to use mathematical constraint methods based on physics and on optimization theory to create controlled, realistic animation of physically-based flexible models. Two types of constraints are presented in this paper: reaction constraints (RCs) and augrmented Lagrangian constraints (ALCs). RCs allow the fast computation of collisions of flexible models with polygonal models. In addition, RCs allow flexible models to be pushed and pulled under the control of an animator. ALCs create animation effects such as volume-preserving squashing and the molding of taffy-like substances. ALCs are compatible with RCs. In this paper, we describe how to apply these constraint methods to a flexible model that uses finite elements.

The theory of elasticity describes deformable materials such as rubber, cloth, paper, and flexible metals. We employ elasticity theory to construct differential equations that model the behavior of non-rigid curves, surfaces, and solids as a function of time. Elastically deformable models are active: they respond in a natural way to applied forces, constraints, ambient media, and impenetrable obstacles. The models are fundamentally dynamic and realistic animation is created by numerically solving their underlying differential equations. Thus, the description of shape and the description of motion are unified.