Query: Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News

Relevant passages:
Hierarchical Attention Networks for Document Classification <=SEP=> We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences.

Interactive Attention Networks for Aspect-Level Sentiment Classification <=SEP=> Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.

Using Structured Events to Predict Stock Price Movement: An Empirical Investigation <=SEP=> It has been shown that news events influence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of structured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts. Both linear and nonlinear models are employed to empirically investigate the hidden and complex relationships between events and the stock market. Largescale experiments show that the accuracy of S&P 500 index prediction is 60%, and that of individual stock prediction can be over 70%. Our event-based system outperforms bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data.

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction <=SEP=> Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.

A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

Logic design with unipolar memristors <=SEP=> Memristors are novel devices that could naturally be designed as memory elements. Recently, several methods of designing memristors for logic operations have been proposed. These methods, mostly, make use of bipolar memristors. In this paper, we propose a method for performing logic with unipolar memristors based on OR and NOT logic gates. An integration of the basic building blocks into more complicated logic functions is described and demonstrated. Our results indicate that any logic function could be performed using an external controller. Thus, adding the capability of performing logic computations to numerous types of unipolar memristive materials in addition to their memory capability.

Computational intelligence in sports: Challenges and opportunities within a new research domain <=SEP=> Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain. © 2015 Elsevier Inc. All rights reserved.

The inferior alveolar nerve's loop at the mental foramen and its implications for surgery. <=SEP=> BACKGROUND
In this study, the authors aimed to identify and measure the anterior extension of the alveolar loop (aAL) and the caudal extension of the alveolar loop (cAL) of the inferior alveolar nerve by using cone-beam computed tomography (CBCT). They also aimed to provide recommendations for surgery in the anterior mandible.


METHODS
In this retrospective case study of the frequency and extension of aAL and cAL, the authors evaluated 1,384 mandibular sites in 694 CBCT scans of dentate and partly edentulous patients, performed mainly for further diagnosis before removal of the mandibular third molars between January 2009 and February 2013, by using multiplanar reconstructions.


RESULTS
The frequency of aAL was 69.73 percent and of cAL was 100 percent. The mean value for aAL was 1.16 millimeters, with a range of 0.3 to 5.6 mm; the mean value for cAL was 4.11 mm, with a range of 0.25 to 8.87 mm. For aAL, 95.81 percent of the sites showed values of 0 to 3 mm; for cAL, 93.78 percent of the sites showed values of 0.25 to 6 mm. Dentate patients showed statistically significantly higher values for cAL than did partly edentulous patients (P = .043). CBCT resolution had a statistically significant impact on cAL measurements (P = .001), with higher values at higher resolution.


CONCLUSIONS
This study showed a high frequency of and large variations in aAL and cAL. In contrast to panoramic radiography, CBCT has been shown to be a reliable tool for identifying and measuring the AL. Therefore, preoperative diagnosis with CBCT is recommended for planning three-dimensional tasks such as implant placement in the vicinity of the mental foramen.


PRACTICAL IMPLICATIONS
Owing to the variability of aAL and cAL measurements, it is difficult to recommend reliable safety margins for surgical procedures such as implant placement, bone harvesting or genioplasty Depending on the indication, the clinician should consider preoperative diagnosis by means of CBCT.

The Synthetic Data Vault <=SEP=> The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.

CSIsnoop: Attacker Inference of Channel State Information in Multi-User WLANs <=SEP=> Channel State Information (CSI) has been proposed to enhance physical layer security between a transmitter and a receiver because it decorrelates over half wavelength distances in rich scattering environments. Consequently, CSI was employed to generate passwords, to authenticate the source of packets, and to inject artificial noise to thwart eavesdroppers. However, in this paper, we present CSIsnoop, and show that an attacker can infer CSI in a multi-user WLAN, even if both channel sounding sequences from the access point and CSI measurement feedback from the clients are encrypted. The insights of CSIsnoop are that the CSI of clients can be computed based on transmit beamforming weights at the access point, and that the transmit beamforming weights can be estimated from downlink multi-user transmission. We implement CSIsnoop on a software defined radio and conduct experiments in various indoor environments. Our results show that on average CSIsnoop can infer CSI of the target client with an absolute normalized correlation of over 0.99, thereby urging reconsideration of the use of CSI as a tool to enhance physical layer security in multi-user WLANs.

ROS play a critical role in the differentiation of alternatively activated macrophages and the occurrence of tumor-associated macrophages <=SEP=> Differentiation to different types of macrophages determines their distinct functions. Tumor-associated macrophages (TAMs) promote tumorigenesis owing to their proangiogenic and immune-suppressive functions similar to those of alternatively activated (M2) macrophages. We report that reactive oxygen species (ROS) production is critical for macrophage differentiation and that inhibition of superoxide (O2−) production specifically blocks the differentiation of M2 macrophages. We found that when monocytes are triggered to differentiate, O2− is generated and is needed for the biphasic ERK activation, which is critical for macrophage differentiation. We demonstrated that ROS elimination by butylated hydroxyanisole (BHA) and other ROS inhibitors blocks macrophage differentiation. However, the inhibitory effect of ROS elimination on macrophage differentiation is overcome when cells are polarized to classically activated (M1), but not M2, macrophages. More importantly, the continuous administration of the ROS inhibitor BHA efficiently blocked the occurrence of TAMs and markedly suppressed tumorigenesis in mouse cancer models. Targeting TAMs by blocking ROS can be a potentially effective method for cancer treatment.

Loc-Auth: Location-enabled authentication through attribute-based encryption <=SEP=> Traditional user authentication involves entering a username and password into a system. Strong authentication security demands, among other requirements, long, frequently hard-to-remember passwords. Two-factor authentication aids in the security, even though, as a side effect, might worsen user experience. We depict a mobile sign-on scheme that benefits from the dynamic relationship between a user's attributes, the service the user wishes to utilize, and location (where the user is, and what services are available there) as an authentication factor. We demonstrate our scheme employing Bluetooth Low Energy beacons for location awareness and the expressiveness of Attribute-Based Encryption to capture and leverage the described relationship. Bluetooth Low Energy beacons broadcast encrypted messages with encoded access policies. Within range of the beacons, a user with appropriate attributes is able to decrypt the broadcast message and obtain parameters that allow the user to perform a short or simplified login.

Reconstruction of the high urogenital sinus: early perineal prone approach without division of the rectum. <=SEP=> PURPOSE
Reconstruction of the vagina and external genitalia in the infant is quite challenging, particularly when a urogenital sinus is associated with high confluence of the vagina and urethra. Many surgeons believe that children with such a malformation should undergo staged or delayed reconstruction, so that vaginoplasty is done when the child is older and larger. Vaginoplasty early in life is thought to be difficult due to patient size and poor visualization. The posterior sagittal approach has been beneficial for acquiring exposure to high urogenital sinus anomalies but it has been thought to require splitting of the rectum and temporary colostomy. We report a modification of this technique.


MATERIALS AND METHODS
In the last 5 years all patients with urogenital sinus anomalies underwent reconstruction using a single stage approach regardless of the level of confluence. In 8 patients with a high level of confluence reconstruction was performed using a perineal prone approach. Exposure was achieved without division of the rectum. The operative technique is presented in detail.


RESULTS
This midline perineal prone approach has allowed excellent exposure of the high vagina even in infants. In all 8 patients reconstruction was done without difficulty and no patient required incision of the rectum or colostomy. This procedure did not preclude the use of a posteriorly based flap for vaginal reconstruction.


CONCLUSIONS
While patients with low confluence can be treated with single posteriorly based flap vaginoplasty, those with higher confluence may benefit from a perineal prone approach to achieve adequate exposure for pull-through vaginoplasty. This prone approach to the high urogenital sinus anomaly can be performed without division of the rectum, provides excellent exposure of the high confluence even in small children and does not preclude the use of posterior flaps for vaginal reconstruction.

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop <=SEP=> While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.

Women “ Take Care , ” Men “ Take Charge ” : Managers ’ Stereotypic Perceptions of Women and Men Leaders <=SEP=> This study explored possible underpinnings of findings from earlier research in which women’s overall leadership competence was less favorably evaluated than men’s. The authors examined perceptions held by senior managers, 34% of whom were CEOs, of women’s and men’s effectiveness at 10 key leadership behaviors. Respondents generally perceived that women were more effective than men at caretaking leader behaviors; and that men were more effective than women at actionoriented, “take-charge” leader behaviors. Notably, male respondents perceived that the behavior at which men leaders most outperformed women was problemsolving. The authors propose that this perception could potentially undermine the influence of women leaders.

Evaluation of Objective Quality Measures for Speech Enhancement <=SEP=> In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.

A Combination of Amino Acids and Caffeine Enhances Sprint Running Capacity in a Hot, Hypoxic Environment. <=SEP=> Heat and hypoxia exacerbate central nervous system (CNS) fatigue. We therefore investigated whether essential amino acid (EAA) and caffeine ingestion attenuates CNS fatigue in a simulated team sport-specific running protocol in a hot, hypoxic environment. Subelite male team sport athletes (n = 8) performed a repeat sprint running protocol on a nonmotorized treadmill in an extreme environment on 4 separate occasions. Participants ingested one of four supplements: a double placebo, 3 mg.kg-1 body mass of caffeine + placebo, 2 x 7 g EAA (Musashi Create)+placebo, or caffeine + EAA before each exercise session using a randomized, double-blind crossover design. Electromyography (EMG) activity and quadriceps evoked responses to magnetic stimulation were assessed from the dominant leg at preexercise, halftime, and postexercise. Central activation ratio (CAR) was used to quantify completeness of quadriceps activation. Oxygenation of the prefrontal cortex was measured via near-infrared spectroscopy. Mean sprint work was higher (M = 174 J, 95% CI [23, 324], p < .05, d = 0.30; effect size, likely beneficial) in the caffeine + EAA condition versus EAAs alone. The decline in EMG activity was less (M = 13%, 95% CI [0, 26]; p < .01, d = 0.58, likely beneficial) in caffeine + EAA versus EAA alone. Similarly, the pre- to postexercise decrement in CAR was significantly less (M = -2.7%, 95% CI [0.4, 5.4]; p < .05, d = 0.50, likely beneficial) when caffeine + EAA were ingested compared with placebo. Cerebral oxygenation was lower (M = -5.6%, 95% CI [1.0, 10.1]; p < .01, d = 0.60, very likely beneficial) in the caffeine + EAA condition compared with LNAA alone. Co-ingestion of caffeine and EAA appears to maintain muscle activation and central drive, with a small improvement in running performance.

KDD meets Big Data <=SEP=> Cross-Industry Standard process model (CRISP-DM) was developed in the late 90s by a consortium of industry participants to facilitate the end-to-end data mining process for Knowledge Discovery in Databases (KDD). While there have been efforts to better integrate with management and software development practices, there are no extensions to handle the new activities involved in using big data technologies. Data Science Edge (DSE) is an enhanced process model to accommodate big data technologies and data science activities. In recognition of the changes, the author promotes the use of a new term, Knowledge Discovery in Data Science (KDDS) as a call for the community to develop a new industry standard data science process model.

TED-LIUM: an Automatic Speech Recognition dedicated corpus <=SEP=> This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4%. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.

Smart street light system looking like usual street lights based on sensor networks <=SEP=> Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.

Bias and ignorance in demographic perception <=SEP=> When it comes to knowledge of demographic facts, misinformation appears to be the norm. Americans massively overestimate the proportions of their fellow citizens who are immigrants, Muslim, LGBTQ, and Latino, but underestimate those who are White or Christian. Previous explanations of these estimation errors have invoked topic-specific mechanisms such as xenophobia or media bias. We reconsidered this pattern of errors in the light of more than 30 years of research on the psychological processes involved in proportion estimation and decision-making under uncertainty. In two publicly available datasets featuring demographic estimates from 14 countries, we found that proportion estimates of national demographics correspond closely to what is found in laboratory studies of quantitative estimates more generally. Biases in demographic estimation, therefore, are part of a very general pattern of human psychology—independent of the particular topic or demographic under consideration—that explains most of the error in estimates of the size of politically salient populations. By situating demographic estimates within a broader understanding of general quantity estimation, these results demand reevaluation of both topic-specific misinformation about demographic facts and topic-specific explanations of demographic ignorance, such as media bias and xenophobia.

ITIL-based IT service support process reengineering <=SEP=> The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best practices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.

Approaches to Interpreter Composition <=SEP=> In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.

Nonlinear Scale Space with Spatially Varying Stopping Time <=SEP=> A general scale space algorithm is presented for denoising signals and images with spatially varying dominant scales. The process is formulated as a partial differential equation with spatially varying time. The proposed adaptivity is semi-local and is in conjunction with the classical gradient-based diffusion coefficient, designed to preserve edges. The new algorithm aims at maximizing a local SNR measure of the denoised image. It is based on a generalization of a global stopping time criterion presented recently by the author and colleagues. Most notably, the method works well also for partially textured images and outperforms any selection of a global stopping time. Given an estimate of the noise variance, the procedure is automatic and can be applied well to most natural images.

Design procedure for two-stage CMOS operational amplifiers employing current buffer <=SEP=> The design procedure of the two-stage CMOS operational amplifiers employing Miller capacitor in conjunction with the common-gate current buffer is presented. Unlike the previously reported design strategy of the opamp of this type, which results in the opamp with a pair of nondominant complex conjugate poles and a finite zero, the proposed procedure is based upon the design strategy that results in the opamp with only one dominant pole. Design example of the proposed procedure is given.

Electroencephalography (EEG) Based Mobile Robot Control through an Adaptive Brain Robot Interface <=SEP=> This project mentioned a couple of brain controlled automaton supported Brain–computer interfaces (BCI). BCIs square measure systems that may by pass standard channels of communication (i.e., muscles and thoughts) to produce direct communication and management between the human brain and physical devices by translating totally different patterns of brain activity into commands in real time. With these commands mobile automaton may be controlled. The intention of the project work is to develop automaton that may assist the disabled folks in their standard of living to try and do some work freelance on others. Brain signals are detected by the brain wave device and it'll convert the info into packets and transmit through Bluetooth medium. Level instrument unit (LAU) can receive the brain wave information and it'll extract and method the signal victimization Mat lab platform. Then the management commands are transmitted to the robotic ARM module to method. With this whole system, we will choose AN object and place it consequently through the designed brain signals.

Face recognition using PCA and SVM <=SEP=> Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.

Probabilistic Extraction of Beat Positions from a Beat Activation Function <=SEP=> We present a probabilistic way to extract beat positions from the output (activations) of the neural network that is at the heart of an existing beat tracker. The method can serve as a replacement for the greedy search the beat tracker currently uses for this purpose. Our experiments show improvement upon the current method for a variety of data sets and quality measures, as well as better results compared to other state-of-the-art algorithms.

Learning diverse rankings with multi-armed bandits <=SEP=> Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.

Review of statistical shape spaces for 3D data with comparative analysis for human faces <=SEP=> With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question. In this paper, we review, analyze and compare different statistical models: from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically, in terms of both their statistical and computational aspects. We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.