An Information-Theoretic Test for Dependence with an Application to the Temporal Structure of Stock Returns

Information theory provides ideas for conceptualising information and measuring relationships between objects. It has found wide application in the sciences, but economics and finance have made surprisingly little use of it. We show that time series data can usefully be studied as information -- by noting the relationship between statistical redundancy and dependence, we are able to use the results of information theory to construct a test for joint dependence of random variables. The test is in the same spirit of those developed by Ryabko and Astola (2005, 2006b,a), but differs from these in that we add extra randomness to the original stochatic process. It uses data compression to estimate the entropy rate of a stochastic process, which allows it to measure dependence among sets of random variables, as opposed to the existing econometric literature that uses entropy and finds itself restricted to pairwise tests of dependence. We show how serial dependence may be detected in S&P500 and PSI20 stock returns over different sample periods and frequencies. We apply the test to synthetic data to judge its ability to recover known temporal dependence structures.Comment: 22 pages, 7 figure

Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector

A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video

LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN

High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model impedes real-time processing, and (2) inapplicable to legacy SDR content with more degradation types. Therefore, we propose a lightweight DNN-based method trained to tackle legacy SDR. For better design, we reform the problem modeling and emphasize degradation model. Experiments show that our method reached appealing performance with minimal computational cost compared with others.Comment: Accepted in ACCV202

Advanced Visual Computing for Image Saliency Detection

Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks

Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.

A rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.Engineering and Physical Sciences Research Council through the CSIT 2 Project under Grant EP/N508664/1

Image recognition technique of road tax sticker in Malaysia

Plate Recognition became significant in daily life because of the unlimited increase of transportation systems which make it impossible to be fully managed and monitored by humans, examples are so many like traffic monitoring, tracking stolen cars, managing parking toll, red-light violation enforcement, border, toll gates and customs check points. This paper will propose a new image recognition technique for inspecting the validity of car Road Tax information in Malaysia based on Neural Network. The development of vehicle Road Tax Recognition (RTR) System will result in greater efficiency for vehicle monitoring system at Toll Gates in Malaysia. In Malaysia, the usage of recognition system is limited to the vehicle plates. It means that the system is unable to detect Road Tax stickers. Therefore, The Implementing of the Image Recognition of The Road Tax and Monitoring the License Plate Number Identification system helps to automatically detect the Road Tax sticker information and plate number. Previously, the police used to observe the expiry date of the Road Tax sticker and matched it with the car plate number manually. So this paper aimed to propose a technique to monitor the vehicle by automatically capturing and extracting the Road Tax sticker image

An AI-Resilient Text Rendering Technique for Reading and Skimming Documents

Readers find text difficult to consume for many reasons. Summarization can address some of these difficulties, but introduce others, such as omitting, misrepresenting, or hallucinating information, which can be hard for a reader to notice. One approach to addressing this problem is to instead modify how the original text is rendered to make important information more salient. We introduce Grammar-Preserving Text Saliency Modulation (GP-TSM), a text rendering method with a novel means of identifying what to de-emphasize. Specifically, GP-TSM uses a recursive sentence compression method to identify successive levels of detail beyond the core meaning of a passage, which are de-emphasized by rendering words in successively lighter but still legible gray text. In a lab study (n=18), participants preferred GP-TSM over pre-existing word-level text rendering methods and were able to answer GRE reading comprehension questions more efficiently.Comment: Conditionally accepted to CHI 202

Stress-Responsive Nano- and Microcomposites Featuring Mechanophore Units

abstract: The problem of catastrophic damage purveys in any material application, and minimizing its occurrence is paramount for general health and safety. Thus, novel damage detection schemes are required that can sense the precursors to damage. Mechanochemistry is the area of research that involves the use of mechanical force to induce a chemical change, with recent study focusing on directing the mechanical force to embedded mechanophore units for a targeted chemical response. Mechanophores are molecular units that provide a measureable signal in response to an applied force, often in the form of a visible color change or fluorescent emission, and their application to thermoset network polymers has been limited. Following preliminary work on polymer blends of cyclobutane-based mechanophores and epoxy, dimeric 9-anthracene carboxylic acid (Di-AC)-based mechanophore particles were synthesized and employed to form stress sensitive particle reinforced epoxy matrix composites. Under an applied stress, the cyclooctane-rings in the Di-AC particles revert back to their fluorescent anthracene form, which linearly enhances the overall fluorescence of the composite in response to the applied strain. The fluorescent signal further allows for stress sensing in the elastic region of the stress-strain curve, which is considered to be a form of damage precursor detection. This behavior was further analyzed at the molecular scale with corresponding molecular dynamics simulations. Following the successful application of Di-AC to an epoxy matrix, the mechanophore particles were incorporated into a polyurethane matrix to show the universal nature of Di-AC as a stress-sensitive particle filler. Interestingly, in polyurethane Di-AC could successfully detect damage with less applied strain compared to the epoxy system. While mechanophores of varying chemistries have been covalently incorporated into elastomeric and thermoplastic polymer systems, they have not yet been covalently incorporated a thermoset network polymer. Thus, following the study of mechanophore particles as stress-sensitive fillers, two routes of grafting mechanophore units into an epoxy system to form a self-sensing nanocomposite were explored. These involved the mechanophore precursor and mechanophore, cinnamamide and di-cinnamamide, respectively. With both molecules, the free amine groups can directly bond to epoxy resin to covalently incorporate themselves within the thermoset network to form a self-sensing nanocomposite.Dissertation/ThesisDoctoral Dissertation Chemical Engineering 201

Diffraction Studies Of Deformation In Shape Memory Alloys And Selected Engineering Components

Deformation phenomena in shape memory alloys involve stress-, temperature-induced phase transformations and crystallographic variant conversion or reorientation, equivalent to a twinning operation. In near equiatomic NiTi, Ti rich compositions can exist near room temperature as a monoclinic B19\u27 martensitic phase, which when deformed undergoes twinning resulting in strains as large as 8%. Upon heating, the martensite transforms to a cubic B2 austenitic phase, thereby recovering the strain and exhibiting the shape memory effect. Ni rich compositions on the other hand can exist near room temperature in the austenitic phase and undergo a reversible martensitic transformation on application of stress. Associated with this reversible martensitic transformation are macroscopic strains, again as large as 8%, which are also recovered and resulting in superelasticity. This work primarily focuses on neutron diffraction measurements during loading at the Los Alamos Neutron Science Center at Los Alamos National Laboratory. Three phenomena were investigated: First, the phenomena of hysteresis reduction and increase in linearity with increasing plastic deformation in superelastic NiTi. There is usually a hysteresis associated with the forward and reverse transformations in superelastic NiTi which translates to a hysteresis in the stress-strain curve during loading and unloading. This hysteresis is reduced in cold-worked NiTi and the macroscopic stress-strain response is more linear. This work reports on measurements during loading and unloading in plastically deformed (up to 11%) and cycled NiTi. Second, the tension-compression stress-strain asymmetry in martensitic NiTi. This work reports on measurements during tensile and compressive loading of polycrystalline shape-memory martensitic NiTi with no starting texture. Third, a heterogeneous stress-induced phase transformation in superelastic NiTi. Measurements were performed on a NiTi disc specimen loaded laterally in compression and associated with a macroscopically heterogeneous stress state. For the case of superelastic NiTi, the experiments related the macroscopic stress-strain behavior (from an extensometer or an analytical approach) with the texture, phase volume fraction and strain evolution (from neutron diffraction spectra). For the case of shape memory NiTi, the macroscopic connection was made with the texture and strain evolution due to twinning and elastic deformation in martensitic NiTi. In all cases, this work provided for the first time insight into atomic-scale phenomena such as mismatch accommodation and martensite variant selection. The aforementioned technique of neutron diffraction for mechanical characterization was also extended to engineering components and focused mainly on the determination of residual strains. Two samples were investigated and presented in this work; first, a welded INCONEL 718 NASA space shuttle flow liner was studied at 135 K and second, Ti-6Al-4V turbine blade components were investigated for Siemens Westinghouse Power Corporation. Lastly, also reported in this dissertation is a refinement of the methodology established in the author\u27s masters thesis at UCF that used synchrotron x-ray diffraction during loading to study superelastic NiTi. The Los Alamos Neutron Science Center is a national user facility funded by the United States Department of Energy, Office of Basic Energy Sciences, under Contract No. W-7405-ENG-36. The work reported here was made possible by grants to UCF from NASA (NAG3-2751), NSF CAREER (DMR-0239512), Siemens Westinghouse Power Corporation and the Space Research Initiative