Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature transformation applied to the detection of discontinuities in concatenated speech

The quality of concatenated speech depends on the degree of mismatch between successive units. Defining a perceptually salient join cost to represent the degree of mismatch has proven to be a difficult task. Such a join cost is critical in unit selection synthesis to ensure that the optimum sequence of speech units is selected from the units available in the speech inventory. In this study the problem of defining a join cost is extended to include a feature transformation stage. Two feature transformations are considered, principal component analysis and a neural networkbased approach. Each transformation was investigated for its ability to improve the detection of discontinuities in concatenated speech for a given feature set. The results indicate that a feature transformation combining principal component analysis as a preprocessing stage to a neural network-based transformation can increase the rate of detection of discontinuities. The neural network was trained using perceptual data obtained from a subjective listening test indicating if a join is continuous or discontinuous. The highest scoring measure based on this strategy provided a correlation with perceptual results of 0.8859 compared with a value of 0.7576 over the baseline MFCC measure on the same test data set.
Estimation of Imageable Dip Range of Target Structures in Interferometric Salt Flank Imaging with Limited Illumination

When applying seismic interferometry to image sub-vertical salt flanks or structures with large dips from vertical or deviated wells, we are often confronted with poor target illumination. Strong directionalillumination footprints caused by non-ideal placement of sources at the surface degrade the interferometric image quality and may prevent the retrieval of particular dips in the image. The effect of the well's orientation on the interferometric image is investigated. Moreover, a method is presented to estimate the imageable dips in an interferometric image, which can be used to design a more favorable shooting geometry and to gain additional knowledge about the target structures.
DES MILIEUX BIOLOGIQUES OPTICAL AND ACOUSTICAL IMAGING OF BIOLOGICAL MEDIA 3 D acousto-optic modulated-speckle imaging in biological tissues

A parallel, 64K-channel implementation of the acousto-optic method of imaging optical contrasts buried in organs is presented. This implementation also associates this method with echography in a single apparatus. This allows us to prove that sensitivity to purely optical contrast is achieved. Moreover, this enables any potential user to distinguish easily optical contrast, acoustic contrast, and mixtures of both. We plan to improve this prototype with a view to future use by physicians in a hospital environment.  2001 Académie des sciences/Éditions scientifiques et médicales Elsevier SAS scattering media / biological tissues / ultrasonic modulation / parallel lock-in detection / echography Imagerie acousto-optique 3D par modulation de speckle dans les tissus
Self-Calibration Algorithm for the Amplitude and Phase Error of the Multiple Beam Antenna

When we use the multiple beam antenna to estimate the Direction of Arrival (DOA) by using the multiple signal classification (MUSIC) algorithm, the uncertainty element's amplitude and phase will effect the DOA estimation performance of the beam antenna. So a universal technique for self-calibration based on the minimize cost function is introduced. By minimize the cost function it can get the array covariance matrix and compensate the array uncertainty without estimation. Therefore, the proposed method is advantage over the source calibration algorithm that resorts to computing the sample minimize cost function. The performance of the proposed method is demonstrated by using the actual parameter of multiple beam antenna, and the computer simulations show that the proposed method provide comparable performance that can not only self- calibrate its sensitivity to the array uncertainty with reduce complexity compute with resource, but also further increase its precision.
A New Image Encryption Approach using the Integration of a Shifting Technique and the AES Algorithm

In this paper, a new image encryption technique is proposed based on the integration of shifted image blocks and basic AES, where the shifted algorithm technique is used to divide the image into blocks. Each block consists of number of pixels, and these blocks are shuffled by using a shift technique that moves the rows and columns of the original image in such a way to produce a shifted image. This shifted image is then used as an input image to the AES algorithm to encrypt the pixels of the shifted image. In order to evaluate the performance, the proposed integration technique and AES algorithm were measured through a series of tests. These tests included a histogram analysis, information entropy, correlation analysis, differential analysis. Experimental results showed that the new integration technique has satisfactory security and is more efficient than using the AES algorithm alone without the shifting algorithm which makes it a good technique for the encryption of multimedia data. The results showed that the histogram of an encrypted image produced a uniform distribution, which is very different from the histogram of the plain image, and the correlation among image pixels was significantly decreased by using the integration technique and a higher entropy was achieved.
Multi-Label Transfer Learning With Sparse Representation

Due to the visually polysemous barrier, videos and images may be annotated by multiple tags. Discovering the correlations among different tags can significantly help predicting precise labels for videos and images. Many of recent studies toward multi-label learning construct a linear subspace embedding with encoded multi-label information, such that data points sharing many common labels tend to be close to each other in the embedded subspace. Motivated by the advances of compressive sensing research, a sparse representation that selects a compact subset to describe the input data can be more discriminative. In this paper, we propose a sparse multi-label learning method to circumvent the visually polysemous barrier of multiple tags. Our approach learns a multi-label encoded sparse linear embedding space from a related dataset, and maps the target data into the learned new representation space to achieve better annotation performance. Instead of using l1-norm penalty (lasso) to induce sparse representation, we propose to formulate the multi-label learning as a penalized least squares optimization problem with elastic-net penalty. By casting the video concept detection and image annotation tasks into a sparse multi-label transfer learning framework in this paper, ridge regression, lasso, elastic net, and the multi-label extended sparse discriminant analysis methods are, respectively, well explored and compared.
Patients with persistent pain after enucleation studied by MRI dynamic color mapping and histopathology.

PURPOSE To study possible causes of persistent pain in patients who underwent enucleation of the globe and in whom all other noninvasively detectable causes of pain had been ruled out. METHODS Twenty patients were studied, 10 with intractable pain (score >5 on a 0-to-9 self-reporting pain scale) persisting for more than 6 months after enucleation (for various reasons) and 10 without pain (score <4) at least 6 months after enucleation. Magnetic resonance imaging (MRI) with dynamic color mapping (MRI-DCM) was used to quantify the motion of the optic nerve in millimeters per degree of gaze, 2 to 3 mm behind the implant. Histopathologic study of biopsy specimens was used to verify imaging findings. RESULTS The optic nerve was attached to the implant in almost all (19/20) patients. On average, the motion was significantly less in patients with persistent intractable pain (0.04 mm/deg) than in patients without pain (0.08 mm/deg; normal orbit, 0.13 mm/deg). A biopsy specimen was available in 5 of 10 patients with persistent pain, and in 4 of those 5, microscopic neuroma was found close to the optic nerve-implant junction. CONCLUSIONS In the enucleated orbit, the optic nerve is usually attached to the implant and soft tissue motion is decreased. In patients who have persistent pain after enucleation, motion is decreased even more, and a high percentage of microscopic amputation neuromas are found. Increased stiffness of orbital soft tissue and optic nerve attachment after enucleation are detectable using MRI-DCM, and may play a role in susceptible patients in the development of microscopic amputation neuroma and pain.
Augmenting the Efficacy of Immunotoxins and Other Targeted Protein Toxins by Endosomal Escape Enhancers

The toxic moiety of almost all protein-based targeted toxins must enter the cytosol of the target cell to mediate its fatal effect. Although more than 500 targeted toxins have been investigated in the past decades, no antibody-targeted protein toxin has been approved for tumor therapeutic applications by the authorities to date. Missing efficacy can be attributed in many cases to insufficient endosomal escape and therefore subsequent lysosomal degradation of the endocytosed toxins. To overcome this drawback, many strategies have been described to weaken the membrane integrity of endosomes. This comprises the use of lysosomotropic amines, carboxylic ionophores, calcium channel antagonists, various cell-penetrating peptides of viral, bacterial, plant, animal, human and synthetic origin, other organic molecules and light-induced techniques. Although the efficacy of the targeted toxins was typically augmented in cell culture hundred or thousand fold, in exceptional cases more than million fold, the combination of several substances harbors new problems including additional side effects, loss of target specificity, difficulties to determine the therapeutic window and cell type-dependent variations. This review critically scrutinizes the chances and challenges of endosomal escape enhancers and their potential role in future developments.
Positron Emission Tomography (PET) in Oncology

Since its introduction in the early nineties as a promising functional imaging technique in the management of neoplastic disorders, FDG-PET, and subsequently FDG-PET/CT, has become a cornerstone in several oncologic procedures such as tumor staging and restaging, treatment efficacy assessment during or after treatment end and radiotherapy planning. Moreover, the continuous technological progress of image generation and the introduction of sophisticated software to use PET scan as a biomarker paved the way to calculate new prognostic markers such as the metabolic tumor volume (MTV) and the total amount of tumor glycolysis (TLG). FDG-PET/CT proved more sensitive than contrast-enhanced CT scan in staging of several type of lymphoma or in detecting widespread tumor dissemination in several solid cancers, such as breast, lung, colon, ovary and head and neck carcinoma. As a consequence the stage of patients was upgraded, with a change of treatment in 10%–15% of them. One of the most evident advantages of FDG-PET was its ability to detect, very early during treatment, significant changes in glucose metabolism or even complete shutoff of the neoplastic cell metabolism as a surrogate of tumor chemosensitivity assessment. This could enable clinicians to detect much earlier the effectiveness of a given antineoplastic treatment, as compared to the traditional radiological detection of tumor shrinkage, which usually takes time and occurs much later.
Cryptanalysis on Improved Chou et al.'s ID-Based Deniable Authentication Protocol

A deniable authentication protocol enables the protocol participants to authenticate their respective peers, while able to deny their participation after the protocol execution. This protocol can be extremely useful in some practical applications such as online negotiation, online shopping and electronic voting. Recently, we have improved a deniable authentication scheme proposed by Chou et al. due to its vulnerability to the key compromise impersonation attack in our previous report. However, we have later discovered that our previous enhanced protocol is vulnerable to the insider key compromise impersonation attack and key replicating attack. In this paper, we will again secure this protocol against these attacks and demonstrate its heuristic security analysis.
Algorithmic thermodynamics

Algorithmic entropy can be viewed as a special case of the entropy studied in statistical mechanics. This viewpoint allows us to apply many techniques developed for use in thermodynamics to the subject of algorithmic information theory. In particular, suppose we fix a universal prefix-free Turing machine and let X be the set of programs that halt for this machine. Then we can regard X as a set of ‘microstates’, and treat any function on X as an ‘observable’. For any collection of observables, we can study the Gibbs ensemble that maximises entropy subject to constraints on the expected values of these observables. We illustrate this by taking the log runtime, length and output of a program as observables analogous to the energy E, volume V and number of molecules N in a container of gas. The conjugate variables of these observables allow us to define quantities we call the ‘algorithmic temperature’ T, ‘algorithmic pressure’ P and ‘algorithmic potential’ μ, since they are analogous to the temperature, pressure and chemical potential. We derive an analogue of the fundamental thermodynamic relation dE = TdS − PdV + μdN, and use it to study thermodynamic cycles analogous to those for heat engines. We also investigate the values of T, P and μ for which the partition function converges. At some points on the boundary of this domain of convergence, the partition function becomes uncomputable – indeed, at these points the partition function itself has non-trivial algorithmic entropy.
Nearest neighbor based collection OCR

Conventional optical character recognition (OCR) systems operate on individual characters and words, and do not normally exploit document or collection context. We describe a Collection OCR which takes advantage of the fact that multiple examples of the same word (often in the same font) may occur in a document or collection. The idea here is that an OCR or a reCAPTCHA like process generates a partial set of recognized words. In the second stage, a nearest neighbor algorithm compares the remaining word-images to those already recognized and propagates labels from the nearest neighbors. It is shown that by using an approximate fast nearest neighbor algorithm based on Hierarchical K-Means (HKM), we can do this accurately and efficiently. It is also shown that profile based features perform much better than SIFT and Pyramid Histogram of Gradient (PHOG) features. We believe that this is because profile features are more robust to word degradations (common in our documents). This approach is applied to a collection of Telugu books - a language for which no commercial OCR exists. We show from a selection of 33 Telugu books that starting with OCR labels for only 30% of the collection we can recognize the remaining 70% of the words in the collection with 70% accuracy using this approach. Since the approach makes no language specific assumptions, it should be applicable to a large number of languages. In particular we are interested in its applicability to Indic languages and scripts.
Live User-Guided Intrinsic Video for Static Scenes

We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection. We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance.
Feasibility Study on the Pyrolysis Production for Hydrogen-Riched Fuel Gas from the Wet Sewage Sludge

The feasibility of producing hydrogen-riched fuel gas from wet sewage sludge was assessed in this paper, which consist in carrying out the pyrolysis of dewatered residual activated sludge as it is produced in the wastewater treatment plant with a moisture content of above 70%, without subjecting it to any previous thermal drying process. In order to establish the most favorable conditions for the production of hydrogen-riched fuel gas, the effects of moisture content, heating rate and temperature were analyzed. It was found that the natural moisture of the sludge was converted into steam at high temperature, which can take part in the partial gasification of the sludge, leading to an increase in the gas production and the yield of hydrogen. In addition, pyrolyzing wet sludge at high temperature with high heating rates could maximize the gas yield productivity and raise its quality as a fuel and as a source of hydrogen or syngas as well. In this process, the drying, pyrolysis and gasification of wet sewage sludge were completed in a single thermal process, and resulting in a considerable amount of gases with potential value as fuels due to the relatively high calorific values they contain.
A driving force: an analysis of strategic planning in the Canadian automotive industry

This paper investigated the impact of components and contextual elements of strategic planning on the effectiveness of the strategic planning process. Specifically, the characteristics of planning and the overall effectiveness of planning processes in the automotive industry were studied. A large‐scale survey revealed that certain aspects of these firms’ planning processes were vital for their continued effectiveness. Functional coverage was identified as one of the most important component of strategic planning and provision of sufficient planning resources and the most important contextual element. The practical implications of this research for firms in the automotive industry are discussed.
Emote toWin: Affective Interactions with a Computer Game Agent

Abstract: In this paper, we introduce a game interface that is based on affective interactions between a player and a computer pet. As opposed to many traditional computer games, users are not expected to manipulate control devices in a skillful manner to win the game. Instead the basic idea is to elicit certain reactions of the pet via appropriate emotive user behaviors. For improved accuracy of emotion recognition, we employ a combined analysis of signals from two emotive expression channels: affective speech and physiological reactions.
Controllability of Networked Dynamic Systems with Autonomous Subsystems

This paper extends results of [14] on the controllability of networked dynamic systems to situations in which some subsystems are not directly affected by external inputs. We establish some necessary and sufficient conditions for the system to be controllable. The topology is arbitrary, and every subsystem can have different dynamics. These conditions share the same properties of those in [14]. That is, they essentially depend on the parameters of every subsystem separately and the subsystem connection matrix. Based on these conditions, simple criteria are given for the controllability of networked systems with some special topological structures.
The Piriformospora indica effector PIIN_08944 promotes the mutualistic Sebacinalean symbiosis

Pathogenic and mutualistic microbes actively suppress plant defense by secreting effector proteins to manipulate the host responses for their own benefit. Current knowledge about fungal effectors has been mainly derived from biotrophic and hemibiotrophic plant pathogenic fungi and oomycetes with restricted host range. We studied colonization strategies of the root endophytic basidiomycete Piriformospora indica that colonizes a wide range of plant species thereby establishing long-term mutualistic relationships. The release of P. indica’s genome helped to identify hundreds of genes coding for candidate effectors and provides an opportunity to investigate the role of those proteins in a mutualistic symbiosis. We demonstrate that the candidate effector PIIN_08944 plays a crucial role during fungal colonization of Arabidopsis thaliana roots. PIIN_08944 expression was detected during chlamydospore germination, and fungal deletion mutants (PiΔ08944) showed delayed root colonization. Constitutive over-expression of PIIN_08944 in Arabidopsis rescued the delayed colonization phenotype of the deletion mutant. PIIN_08944-expressing Arabidopsis showed a reduced expression of flg22-induced marker genes of pattern-triggered immunity (PTI) and the salicylic acid (SA) defense pathway, and expression of PIIN_08944 in barley reduced the burst of reactive oxygen species (ROS) triggered by flg22 and chitin. These data suggest that PIIN_08944 contributes to root colonization by P. indica by interfering with SA-mediated basal immune responses of the host plant. Consistent with this, PIIN_08944-expressing Arabidopsis also supported the growth of the biotrophic oomycete Hyaloperonospora arabidopsidis while growth of the necrotrophic fungi Botrytis cinerea on Arabidopsis and Fusarium graminearum on barley was not affected.
Multiple-Module High-Gain High-Voltage DC–DC Transformers for Offshore Wind Energy Systems

Renewable energy sources, such as offshore wind farms, require high voltage gains in order to interface with power transmission networks. These conversions are normally made using bulky, complex, and costly transformers and high-voltage ac-dc converters with unnecessary bidirectional power flow capability. Multiple modules of single-switch single-inductor dc-dc converters can reach high gains without transformers in these applications due to low semiconductor conduction loss in high-power devices. This paper describes a new approach for high-gain high-voltage dc-dc converters using multiple modules of single-switch single-inductor transformerless converters. Results for low-voltage experimental prototypes show gains of up to 29 p.u. and demonstrate the potential of the approach as high-gain dc-dc converters for offshore wind farms. This paper then demonstrates the viability of multiple-module converters compared to a conventional high-voltage dc converter and a theoretical full-bridge converter due to fewer devices and valves, comparable isolation levels, and ease of interleaving for increased reliability.
Towards Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems Using an AOSE Methodology

Autonomic computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as policy-based self-management. We look at ways to achieve this, and in particular focus on agent-oriented software engineering. We propose utilizing an AOSE methodology for specifying autonomic and autonomous properties of the system independently, and later, by means of composition of these specifications, to construct a specification for the policy and its subsequent deployment