ATC: an Advanced Tucker Compression library for multidimensional data

We present ATC, a C++ library for advanced Tucker-based compression of multidimensional numerical data, based on the sequentially truncated higher-order singular value decomposition (ST-HOSVD) and bit plane truncation. Several techniques are proposed to improve compression rate, speed, memory usage and error control. First, a hybrid truncation scheme is described which combines Tucker rank truncation and TTHRESH quantization [Ballester-Ripoll et al., IEEE Trans. Visual. Comput. Graph., 2020]. We derive a novel expression to approximate the error of truncated Tucker decompositions in the case of core and factor perturbations. Furthermore, a Householder-reflector-based approach is proposed to compress the orthogonal Tucker factors. Certain key improvements to the quantization procedure are also discussed. Moreover, particular implementation aspects are described, such as ST-HOSVD procedure using only a single transposition. We also discuss several usability features of ATC, including the presence of multiple interfaces, extensive data type support and integrated downsampling of the decompressed data. Numerical results show that ATC maintains state-of-the-art Tucker compression rates, while providing average speed-ups of 2.6-3.6 and halving memory usage. Furthermore, our compressor provides precise error control, only deviating 1.4% from the requested error on average. Finally, ATC often achieves significantly higher compression than non-Tucker-based compressors in the high-error domain.Comment: The ATC software is publicly available at the following repository: https://gitlab.kuleuven.be/u0118878/at

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected

Incremental volume rendering using hierarchical compression

Includes bibliographical references.The research has been based on the thesis that efficient volume rendering of datasets, contained on the Internet, can be achieved on average personal workstations. We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. This dissertation covers the theory and practice of developing the octree based Shear-Warp algorithms, and then presents the results of extensive empirical testing. The results, using typical volume datasets, demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements

Reverse production effect: Children recognize novel words better when they are heard rather than produced

This is the peer reviewed version of the following article: Tania S. Zamuner, Stephanie Strahm, Elizabeth Morin-Lessard, and Michael P. A. Page, 'Reverse production effect: children recognize novel words better when they are heard rather than produced', Developmental Science, which has been published in final form at DOI 10.1111/desc.12636. Under embargo until 15 November 2018. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This research investigates the effect of production on 4.5- to 6-year-old children’s recognition of newly learned words. In Experiment 1, children were taught four novel words in a produced or heard training condition during a brief training phase. In Experiment 2, children were taught eight novel words, and this time training condition was in a blocked design. Immediately after training, children were tested on their recognition of the trained novel words using a preferential looking paradigm. In both experiments, children recognized novel words that were produced and heard during training, but demonstrated better recognition for items that were heard. These findings are opposite to previous results reported in the literature with adults and children. Our results show that benefits of speech production for word learning are dependent on factors such as task complexity and the developmental stage of the learner.Peer reviewedFinal Accepted Versio

Mapping Stream Programs into the Compressed Domain

Due to the high data rates involved in audio, video, and signalprocessing applications, it is imperative to compress the data todecrease the amount of storage used. Unfortunately, this implies thatany program operating on the data needs to be wrapped by adecompression and re-compression stage. Re-compression can incursignificant computational overhead, while decompression swamps theapplication with the original volume of data.In this paper, we present a program transformation that greatlyaccelerates the processing of compressible data. Given a program thatoperates on uncompressed data, we output an equivalent program thatoperates directly on the compressed format. Our transformationapplies to stream programs, a restricted but useful class ofapplications with regular communication and computation patterns. Ourformulation is based on LZ77, a lossless compression algorithm that isutilized by ZIP and fully encapsulates common formats such as AppleAnimation, Microsoft RLE, and Targa.We implemented a simple subset of our techniques in the StreamItcompiler, which emits executable plugins for two popular video editingtools: MEncoder and Blender. For common operations such as coloradjustment and video compositing, mapping into the compressed domainoffers a speedup roughly proportional to the overall compressionratio. For our benchmark suite of 12 videos in Apple Animationformat, speedups range from 1.1x to 471x, with a median of 15x

Music Synchronization, Audio Matching, Pattern Detection, and User Interfaces for a Digital Music Library System

Over the last two decades, growing efforts to digitize our cultural heritage could be observed. Most of these digitization initiatives pursuit either one or both of the following goals: to conserve the documents - especially those threatened by decay - and to provide remote access on a grand scale. For music documents these trends are observable as well, and by now several digital music libraries are in existence. An important characteristic of these music libraries is an inherent multimodality resulting from the large variety of available digital music representations, such as scanned score, symbolic score, audio recordings, and videos. In addition, for each piece of music there exists not only one document of each type, but many. Considering and exploiting this multimodality and multiplicity, the DFG-funded digital library initiative PROBADO MUSIC aimed at developing a novel user-friendly interface for content-based retrieval, document access, navigation, and browsing in large music collections. The implementation of such a front end requires the multimodal linking and indexing of the music documents during preprocessing. As the considered music collections can be very large, the automated or at least semi-automated calculation of these structures would be recommendable. The field of music information retrieval (MIR) is particularly concerned with the development of suitable procedures, and it was the goal of PROBADO MUSIC to include existing and newly developed MIR techniques to realize the envisioned digital music library system. In this context, the present thesis discusses the following three MIR tasks: music synchronization, audio matching, and pattern detection. We are going to identify particular issues in these fields and provide algorithmic solutions as well as prototypical implementations. In Music synchronization, for each position in one representation of a piece of music the corresponding position in another representation is calculated. This thesis focuses on the task of aligning scanned score pages of orchestral music with audio recordings. Here, a previously unconsidered piece of information is the textual specification of transposing instruments provided in the score. Our evaluations show that the neglect of such information can result in a measurable loss of synchronization accuracy. Therefore, we propose an OCR-based approach for detecting and interpreting the transposition information in orchestral scores. For a given audio snippet, audio matching methods automatically calculate all musically similar excerpts within a collection of audio recordings. In this context, subsequence dynamic time warping (SSDTW) is a well-established approach as it allows for local and global tempo variations between the query and the retrieved matches. Moving to real-life digital music libraries with larger audio collections, however, the quadratic runtime of SSDTW results in untenable response times. To improve on the response time, this thesis introduces a novel index-based approach to SSDTW-based audio matching. We combine the idea of inverted file lists introduced by Kurth and Müller (Efficient index-based audio matching, 2008) with the shingling techniques often used in the audio identification scenario. In pattern detection, all repeating patterns within one piece of music are determined. Usually, pattern detection operates on symbolic score documents and is often used in the context of computer-aided motivic analysis. Envisioned as a new feature of the PROBADO MUSIC system, this thesis proposes a string-based approach to pattern detection and a novel interactive front end for result visualization and analysis

Recasting a scene adaptive video coder for real time implementation

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 117-118).by Jonathan David Rosenberg.M.S