Understanding Notional Machines through Traditional Teaching with Conceptual Contraposition and Program Memory Tracing

A correct understanding about how computers run code is mandatory in order to effectively learn to program. Lectures have historically been used in programming courses to teach how computers execute code, and students are assessed through traditional evaluation methods, such as exams. Constructivism learning theory objects to students’ passiveness during lessons, and traditional quantitative methods for evaluating a complex cognitive process such as understanding. Constructivism proposes complimentary techniques, such as conceptual contraposition and colloquies. We enriched lectures of a “Programming II” (CS2) course combining conceptual contraposition with program memory tracing, then we evaluated students’ understanding of programming concepts through colloquies. Results revealed that these techniques applied to the lecture are insufficient to help students develop satisfactory mental models of the C++ notional machine, and colloquies behaved as the most comprehensive traditional evaluations conducted in the course.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Text segmentation using a cache memory

International audience

RGS14 (414)-mediated prevention of an episodic memory loss: a study of molecular mechanism

A large proportion of human populations suffer memory impairments either caused by normal aging or afflicted by diverse neurological and neurodegenerative diseases. Memory enhancers and other drugs tested so far against memory loss have failed to produce therapeutic efficacy in clinical trials and thus, there is a need to find remedy for this mental disorder. In search for cure of memory loss, our laboratory discovered a robust memory enhancer called RGS14(414). A treatment in brain with its gene produces an enduring effect on memory that lasts for lifetime of rats. Therefore, current thesis work was designed to investigate whether RGS14(414) treatment can prevent memory loss and furthermore, explore through biological processes responsible for RGS-mediated memory enhancement. We found that RGS14(414) gene treatment prevented episodic memory loss in rodent models of normal aging and Alzheimer´s disease. A memory loss was observed in normal rats at 18 months of age; however, when they were treated with RGS14(414) gene at 3 months of age, they abrogated this deficit and their memory remained intact till the age of 22 months. In addition to normal aging rats, effect of memory enhancer treatment in mice model of Alzheimer´s disease (AD-mice) produced a similar effect. AD-mice subjected to treatment with RGS14(414) gene at the age of 2 months, a period when memory was intact, showed not only a prevention in memory loss observed at 4 months of age but also they were able to maintain normal memory after 6 months of the treatment. We posit that long-lasting effect on memory enhancement and prevention of memory loss mediated through RGS14(414) might be due to a permanent structural change caused by a surge in neuronal connections and enhanced neuronal remodeling, key processes for long-term memory formation. A neuronal arborization analysis of both pyramidal and non-pyramidal neurons in brain of RGS14(414)-treated rats exhibited robust rise in neurites outgrowth of both kind of cells, and an increment in number of branching from the apical dendrite of pyramidal neurons, reaching to almost three times of the control animals. To further understand of underlying mechanism by which RGS14(414) induces neuronal arborization, we investigated into neurotrophic factors. We observed that RGS14 treatment induces a selective increase in BDNF. Role of BDNF in neuronal arborization, as well as its implication in learning and memory processes is well described. In addition, our results showing a dynamic expression pattern of BDNF during ORM processing that overlapped with memory consolidation further support the idea of the implication of this neurotrophin in formation of long-term memory in RGS-animals. On the other hand, in studies of expression profiling of RGS-treated animals, we have demonstrated that 14-3-3ζ protein displays a coherent relationship to RGS-mediated ORM enhancement. Recent studies have demonstrated that the interaction of receptor for activated protein kinase 1 (RACK1) with 14-3-3ζ is essential for its nuclear translocation, where RACK1-14-3-3ζ complex binds at promotor IV region of BDNF and promotes an increase in BDNF gene transcription. These observations suggest that 14-3-3ζ might regulate the elevated level of BDNF seen in RGS14(414) gene treated animals. Therefore, it seems that RGS-mediated surge in 14-3-3ζ causes elevated BDNF synthesis needed for neuronal arborization and enhanced ORM. The prevention of memory loss might be mediated through a restoration in BDNF and 14-3-3ζ protein levels, which are significantly decreased in aging and Alzheimer’s disease. Additionally, our results demonstrate that RGS14(414) treatment could be a viable strategy against episodic memory loss.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

Memory for public events in mild cognitive impairment and Alzheimer’s disease : the importance of rehearsal

Ribot’s law refers to the better preservation of remote memories compared with recent ones that presumably characterizes retrograde amnesia. Even if Ribot-type temporal gradient has been extensively studied in retrograde amnesia, particularly in Alzheimer’s disease (AD), this pattern has not been consistently found. One explanation for these results may be that rehearsal frequency rather than remoteness accounts for the better preservation of these memories. Thus, the aim of present study was to address this question by studying retrograde semantic memory in subjects with amnestic mild cognitive impairment (aMCI) (n = 20), mild AD (n = 20) and in healthy older controls (HC; n = 19). In order to evaluate the impact of repetition as well as the impact of remoteness, we used a test assessing memory for enduring and transient public events that occurred in the recent and remote past. Results show no clear temporal gradient across time periods (1960–1975; 1976–1990; 1991–2005; 2006–2011), but a better performance was observed in all three groups for enduring compared with transient events. Moreover, although deficits were globally found in both patients groups compared with HC, more specific analyses revealed that aMCI patients were only impaired on transient events while AD patients were impaired on both transient and enduring events. Exploratory analyses also revealed a tendency suggesting preservation of remote transient events in aMCI. These findings are discussed with regards to memory consolidation models.