Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.

In this paper, we investigate how to automatically extract attractive summaries for news propagation on microblogs and propose a novel system called ATSUM to achieve this goal via text attractiveness analysis. It first analyzes the sentences in a news article and automatically predict the attractiveness score of each sentence by using the support vector regression method. The predicted attractiveness scores are then incorporated into a summarization system. Experimental results on a manually labeled dataset verify the effectiveness of the proposed methods.

Automated topic identification is an essential component invarious information retrieval and knowledge representationtasks such as automated summary generation, categorization search and document indexing. In this paper, we present the Wikitop system to automatically generate topic trees from the input text by performing hierarchical classification using the Wikipedia Category Network (WCN). Our preliminary results over a collection of 125 articles are encouraging and show potential of a robust methodology for automated topic tree generation.

For Android malware detection, precise ground truth is a rare commodity. As security knowledge evolves, what may be considered ground truth at one moment in time may change, and apps once considered benign may turn out to be malicious. The inevitable noise in data labels poses a challenge to inferring effective machine learning classifiers. Our work is focused on approaches for learning classifiers for Android malware detection in a manner that is methodologically sound with regard to the uncertain and ever-changing ground truth in the problem space. We leverage the fact that although data labels are unavoidably noisy, a malware label is much more precise than a benign label. While you can be confident that an app is malicious, you can never be certain that a benign app is really benign, or just undetected malware. Based on this insight, we leverage a modified Logistic Regression classifier that allows us to learn from only positive and unlabeled data, without making any assumptions about benign labels. We find Label Regularized Logistic Regression to perform well for noisy app datasets, as well as datasets where there is a limited amount of positive labeled data, both of which are representative of real-world situations.

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals (Jaeger andHaas 2004; Maass, Natschläger, and Markram 2002). It canbe easily implemented in hardware. The performance ofthese analogue devices matches digital algorithms on a series of benchmark tasks (see e.g. (Soriano et al. 2015) fora review). Their capacities could be extended by feedingthe output signal back into the reservoir, which would allow them to be applied to various signal generation tasks(Antonik et al. 2016b). In practice, this requires a high-speed readout layer for real-time output computation. Herewe achieve this by means of a field-programmable gate array (FPGA), and demonstrate the first photonic reservoircomputer with output feedback. We test our setup on theMackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement.Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications forphotonic reservoir computing.

In general, which entity mentions in a text or dialogue refer to the same however, the difficulty of coreference resolution stems from real-world entity. Despite being actively investigated for 50 its reliance on sophisticated knowledge sources and inference years in the natural language processing (NLP) community, mechanisms (Mitkov et al. 2001). Despite its difficulty, it is still far from being solved. To better understand the difficulty coreference resolution is a core task in information extraction: of the task, consider the following sentence: it is the fundamental technology for consolidating the textual information about an entity, which is crucial for essentially The Queen Mother asked Queen Elizabeth II to transform all high-level NLP applications, such as question her sister, Princess Margaret, into a viable answering, text summarization, and machine translation.

There are many benefits to introducing students to the idea of doing projects where the outcome is unknown or unsure. Some have proposed that engaging students in research can help with retention of underrepresented groups. In this paper, we report on a particular approach we have used to introduce high school students to open-ended robotics projects in a three-week summer program. We describe the structure of our summer program, how we ramp the students up to speed, and we summarize the five open-ended "research" projects that the students work on. These projects can be adopted for open-ended work elsewhere by high school students or undergraduates.

Effective human-aware robots should anticipate their user’s intentions. During hand-eye coordination tasks, gaze often precedes hand motion and can serve as a powerful predictor for intent. However, cooperative tasks where a semi-autonomous robot serves as an extension of the human hand have rarely been studied in the context of hand-eye coordination. We hypothesize that accounting for anticipatory eye movements in addition to the movements of the robot will improve intent estimation. This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks. We found that with proper feature selection, accuracies exceeding 94% and AUC greater than 91% are achievable with several classification algorithms but that anticipatory gaze data did not improve intent prediction.

Alzheimer's disease (AD) is a genetically complex neurodegenerative disease, which leads to irreversible brain damage, severe cognitive problems and ultimately death. A number of clinical trials and study initiatives have been set up to investigate AD pathology, leading to large amounts of high dimensional heterogeneous data (biomarkers) for analysis. This paper focuses on combining clinical features from different modalities, including medical imaging, cerebrospinal fluid (CSF), etc., to diagnose AD and predict potential progression. Due to privacy and legal issues involved with clinical research, the study cohort (number of patients) is relatively small, compared to thousands of available biomarkers (predictors). We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information with limited training materials. Furthermore, we model (current and future) cognitive healthiness as a regression problem about age. By comparing the difference between predicted age and actual age, we manage to show statistical differences between different pathological stages. Verification tests are conducted based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Extensive comparison is made against different machine learning algorithms, i.e. Support Vector Machine (SVM), Random Forest (RF), Decision Tree and Multilayer Perceptron (MLP). Experimental results show that our proposed algorithm achieves better results than the comparison targets, which indicates promising robustness for practical clinical implementation.

Sustainable energy policies are of growing importance in all urban centers.Climate — and climate change — will play increasingly important roles in these policies.Climate zones defined by the California Energy Commissionhave long been influential in energy management.For example, recently a two-zone division of Los Angeles(defined by historical temperature averages) was introduced for electricity rate restructuring.The importance of climate zones has been enormous,and climate change could make them still more important. AI can provide improvements on the ways climate zones are derived and managed.This paper reports on analysis of aggregate household electricity consumption (EC) data from local utilities in Los Angeles,seeking possible improvements in energy management. In this analysis we noticed that EC data permits identificationof interesting geographical zones  — regions having EC patterns that are characteristically different from surrounding regions.We believe these zones could be useful in a variety of urban models.