Miracle-dz/UniFAR-FTUs huggingface dataset:
BL-Database: A French audiovisual database for speech driven lip animation systems

The lack of publicly available annotated databases is a major limitation to research advances in speech processing. We describe in this paper an audiovisual speech database which is being made available to the research community. Our database, called BL-database (Blue Lips-database), consists of 238 utterances spoken by 17 speakers. The recordings have been performed during two sessions. The data of the first session can be used to analyze the 2D movements of the mouth while the data collected by the second session is dedicated to 3D analysis. The audio signal has been phonetically segmented and labeled. Such data is expected to be of great interest to all research groups working on multimodal automatic speech recognition, audio/visual synchronization or speech-driven lip animation.
A layout-based approach for Multiple Event Transient analysis

With the emerging nanoscale CMOS technology, Multiple Event Transients (METs) originated from radiation strikes are expected to become more frequent than Single Event Transients (SETs). In this paper, a fast and accurate layout-based Soft Error Rate (SER) estimation technique with consideration of both SET and MET fault models is proposed. Unlike previous techniques in which the adjacent MET sites are obtained from logic-level netlist, we perform a comprehensive layout analysis to extract MET adjacent cells. It is shown that layout-based technique is the only effective solution for identification of adjacent cells as netlist-based techniques significantly underestimate the overall SER.
A model driven 3D lane detection system using stereovision

This paper presents a new method for the detection and 3D reconstruction of the road lane using onboard stereovision. The proposed algorithm makes it possible to overcome the assumptions commonly used in most of the detection systems using monocular vision such as: flat road, constant pitch angle or absence of roll angle. The proposed method of detection and 3D reconstruction is based on two modules. The first one is aimed at detecting the road markings of the lane in each image of the stereoscopic pair. It is based on a recognition algorithm driven by a statistical model of the road lane: the initial state of the model is obtained after a learning stage; it is updated using the results of a feature extraction stage. Our model takes into account the intrinsic links between the projections of the road in the two images of the stereoscopic pair, so that its update from a feature extracted in one of two images drives the detection of the features in the other image. No disparity map is required since the matching of the road features is directly obtained as the result of the model update. The second module is aimed at 3D reconstruction and relative localization of the vehicle with respect to its lane. The parameters of a 3D surface including the horizontal and vertical profiles of the road lane are estimated from the road borders previously detected. The robustness of the algorithm is evaluated from synthetic and real images.
On the Step-Size Bounds of Frequency-Domain Block LMS Adaptive Filters

Least-mean-square (LMS) and block LMS (BLMS) adaptive filters are generally believed to have similar step-size bounds for convergence. Similarly, convergence analyses of frequency-domain block LMS (FBLMS) adaptive filters have suggested that they have very restrictive convergence bounds. In this letter, we revisit Feuer's work and reveal a much larger convergence bound for BLMS adaptive filters. We then analyze the convergence properties of the FBLMS adaptive filter. The new step-size bound for the FBLMS adaptive filter, regardless of whether the input is white or colored, is not that restrictive as generally assumed for the block algorithms in the literature. Extensive simulation results are included to support the analyses.
New Paradigms for Colonoscopic Management of Diminutive Colorectal Polyps: Predict, Resect, and Discard or Do Not Resect?

The possibility to predict in vivo the histology of colorectal polyps by advanced endoscopic imaging has resulted in the implementation of a more conservative management for diminutive lesions detected at colonoscopy. In detail, a predict-and-do-not-resect strategy has been proposed for diminutive lesions located in the rectosigmoid tract, whilst a predict-resect-and-discard policy has been advocated for nonrectosigmoid diminutive polyps. Recently, the American Society for Gastrointestinal Endoscopy set required thresholds to be met, before allowing the adoption of these policies in the clinical field. The ability of current endoscopic imaging in reaching these thresholds would depend on a complex interaction among the accuracy of advanced endoscopic imaging in differentiating between adenomatous and hyperplastic lesions, the prevalence of (advanced) neoplasia within diminutive lesions, and the type of surveillance intervals recommended. Aim of this review is to summarize the data supporting the application of both a predict-and-do-not-resect and a predict-resect-and-discard policies, also addressing the potential pitfalls associated with these strategies.
The DUAL Cognitive Architecture: A Hybrid Multi-Agent Approach

A hybrid (symbolic/connectionist) cognitive architecture, DUAL, is proposed. It is a multi-agent system which consist of a large number of non-cognitive , relatively simple agents, and which behaviour emerges from the behaviour of these simple agents and the interactions between them. The agents within this architecture have no internal knowledge base and goals. They are both computational devices and representational elements. They have internal (local) memory and hard- wired processes that they can run. DUAL is hybrid at the micro level (i.e. it consists of hybrid agents) rather than at the macro level (i.e. it does not consist of separate symbolic and connectionist modules). From the symbolic perspective each agent represents a piece of world knowledge and performs some specific task while from the connectionist perspective it computes simply the activation level of the agent which reflects its relevance to the context. In this way symbolism and connectionism are considered as dualistic aspects of human cognition: the former representing the world knowledge and the latter its current relevance. The connectionist aspect of the architecture continuously "restructures" the knowledge base of the cognitive system represented by the symbolic aspect thus controlling the set of possible inferences at any moment. This makes the knowledge base dynamic and context-sensitive. The use of the DUAL cognitive architecture in modelling similarity judgements, analogical and deductive reasoning is described.
E-Novation Customer Relationship Management

In the early 1990's, marketing theory experienced a paradigm shift from a transactional approach focusing on sales to a relationship one. This shift was due to several limits of traditional mix marketing based on the "four P's" and to the change in the market business models. In fact, the growing role of branding in mass markets, the development of services marketing, and the importance of network and human interactions in business are some of the main reasons behind relationship marketing emergence. Relationship marketing is then aimed at developing and maintaining mutually profitable relationships with customers and even stakeholders. In the era of technology evolution and Internet, customer relationship management (CRM) is moving forward to better manage, drive, and keep value-added relationships. However, CRM is, first of all, a company strategy and a shared vision that involves organization, people, and processes in satisfying and retaining customers. This chapter deals with the concept of customer centricity and its development, customer lifecycle with acquisition and retention, and finally the issue of CRM implementation.
A DIBR method based on inverse mapping and depth-aided image inpainting

Depth-image-based rendering (DIBR) has attracted more and more attention as a method of virtual view rendering in three-dimensional video (3DV) applications. Many efforts have been dedicated to realize high-quality rendering, ranging from pre-process of the reference depth images to post-process of the virtual texture images. A DIBR method based on inverse mapping and depth-aided image inpainting technique is proposed in this paper. In the proposed method, inverse mapping rendering technique is adopted to realize the 3D warping instead of traditional forward mapping; and a modified image inpainting technique in which depth information is considered is employed to inpaint the disocclusion regions. Besides, morphological processing is performed on the reference depth image to avoid the artifacts along the disocclusion areas. Experimental results demonstrate that the proposed method can obtain higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in comparison to the previous methods, while producing satisfactory visual effect.
Eyes and Apps on the Streets: From Surveillance to Sousveillance Using Smartphones

This article explores the concept of surveillance by assessing the nature of data gathered by users of a smartphone-based tool (app) developed in Sweden to assist citizens in reporting incidents in public spaces. This article first illustrates spatial and temporal patterns of records gathered over 9 months in Stockholm County using Geographic Information Systems (GIS) to exemplify the process of sousveillance via app. Then, the experiences of user group members, collected using an app-based survey, are analyzed. Findings show that the incident reporting app is more often used to report an incident and less often to prevent it. Preexistent social networks in neighborhoods are fundamental for widespread adoption of the app, often used as a tool in Neighborhood Watch schemes in high-crime areas. Although the potentialities of using app data are open, these results call for more in-depth evaluations of smartphone data for safety interventions.
Broadband 31–65 GHz inductorless active balun with 12.4 dB gain in 0.13μm SiGe:C BiCMOS technology

An inductorless single-ended input to differential output active balun is presented. The balun has a 12.4dB gain at 50GHz and a 3-dB bandwidth from 31 to 65GHz. Measured maximum amplitude and phase errors between the two outputs are 0.5 dB and 2.5°, respectively. Measured input and output reflection are below −15dB and −10dB respectively and the reverse isolation is better than 38 dB. Time domain measurements indicate 1.4 ps and 187 fs peak-to-peak and rms jitter, respectively. The balun is designed in a 0.13 μm SiGe:C BiCMOS technology, dissipates 278mW and occupies 0.47 mm2.
A Systematic Approach to Jointly Optimize Rate and Power Consumption for OFDM Systems

In this paper, we adopt a multiobjective optimization for the bit and power allocation problem in order to meet the requirements of emerging wireless systems, i.e., achieving higher throughput without considerably increasing the transmit power. More specifically, we propose to simultaneously maximize the throughput and minimize the transmit power of an OFDM system subject to average bit error rate (BER), power budget, and maximum allocated number of bits per subcarrier constraints. The formulated optimization problem is not convex and we use an evolutionary algorithm, i.e., genetic algorithm, in order to obtain the solution. We study the structure of the problem and the obtained solution and notice that the constraint on the average BER can be replaced by a BER per subcarrier constraint. As such, we propose an approximate non-convex optimization problem. We further exploit the structure of the approximate optimization problem and notice that the BER constraint per subcarrier (i.e., the source of the non-convexity) must be satisfied with an equality sign and can be substituted; this leads to an equivalent convex optimization problem where the global optimality of the Pareto solutions is guaranteed. Closed-form expressions are obtained for the bit and power allocations with reduced complexity. Simulation results show that the proposed multiobjective optimization approach provides significant performance improvements over single objective optimization techniques presented in the literature, without incurring additional complexity.
Spatial modeling of Scalable Spatially-correlated Log-normal distributed traffic inhomogeneity and energy-efficient network planning

This paper explores the influence of the inhomogeneous spatial traffic distribution on the performance of energy efficiency and proposes an energy-efficient heterogeneous network planning scheme for cellular networks. We first provide a spatial modeling method of Scalable, Spatially correlated, and Log-normally distributed Traffic (SSLT). Together with the spatial modeling, the measure of inhomogeneity of spatial traffic distributions is introduced which is related to the energy efficiency performance of network planning. Then we propose a network planning scheme which deploys both micro and macro base stations (BSes) adapting to the traffic inhomogeneity. Numerical results show that when the spatial traffic distribution is more inhomogeneous, the proposed scheme improves energy efficiency by deploying more micro BSes. We also found that proportional relationships exist among three values, i.e. the ratio of the number of micro and macro BSes, energy efficiency, and the inhomogeneity.
Sampling Theorems for Multivariate Shift

Regular and irregular sampling theorems for multivariate shift invariant subspaces are studied. We give a characterization of regular points and an irregular sampling theorem, which covers many known results, e.g., Kadec’s 1/4-theorem. We show that some subspaces may not have a regular point. We also present a reconstruction algorithm which is slightly dierent from the known one but is more ecient. We study the aliasing error and prove that every smooth square integrable function can be approximated by its sampling series.
Predictive head tracking for virtual reality

In virtual reality (VR), head movement is tracked through inertial and optical sensors. Computation and communication times result in delays between measurements and updating of the new frame in the head mounted display(HMD). These delays result in problems, including motion sickness. We use recurrent and time delay neural networks to predict the head location and use it to calculate the new frame. A predictability analysis is used in designing the prediction system.
Efficient Service Composition Using Zero-Suppressed Reduced Ordered Binary Decision Diagrams

Recent algorithms for automated service composition issue many complex queries to service directories. As service directories are shared resources, they may become performance bottlenecks. In order to increase scalability, we introduce a compact directory digest, which is distributed to clients and includes all information needed for automated service composition. Therefore, complex directory queries during service composition can be avoided. We encode a directory digest as a zero-suppressed reduced ordered binary decision diagram (ZDD). In several steps, we refine a simple service composition algorithm in order to leverage the ZDD representation. Introducing specialized ZDD operations, we achieve a service composition algorithm that scales very well with an increasing size of the directory digest
Interhospital Transport System for Critically Ill Patients: Mobile Extracorporeal Membrane Oxygenation without a Ventilator

Background Extracorporeal membrane oxygenation (ECMO) has been successfully used as a method for the interhospital transportation of critically ill patients. In South Korea, a well-established ECMO interhospital transport system is lacking due to limited resources. We developed a simplified ECMO transport system without mechanical ventilation for use by public emergency medical services. Methods Eighteen patients utilized our ECMO transport system from December 2011 to September 2015. We retrospectively analyzed the indications for ECMO, the patient status during transport, and the patient outcomes. Results All transport was conducted on the ground by ambulance. The distances covered ranged from 26 to 408 km (mean, 65.9±88.1 km) and the average transport time was 56.1±57.3 minutes (range, 30 to 280 minutes). All patients were transported without adverse events. After transport, 4 patients (22.2%) underwent lung transplantation because of interstitial lung disease. Eight patients who had severe acute respiratory distress syndrome showed recovery of heart and lung function after ECMO therapy. A total of 13 patients (70.6%) were successfully taken off ECMO, and 11 patients (61.1%) survived. Conclusion Our ECMO transport system without mechanical ventilation can be considered a safe and useful method for interhospital transport and could be a good alternative option for ECMO transport in Korean hospitals with limited resources.
Breaking New Ground: McHenry County College Developmental Mathematics Approach

ABSTRACT Focusing on the Developmental Mathematics program installed by McHenry County College (MCC), this case study examines an integrative approach to reducing student dependency on developmental math. Facing the challenge of bridging the gap between K-12 and college readiness, MCC established a summer bridge program to help alleviate summer brain drain and help students acclimated themselves to the college classroom environment. College and high school faculty collaborated to create a more fluid transition for students by aligning coursework and targeting specific needs of students. With an articulated mission of preparing students for future success in postsecondary education this program tremendously benefits students by saving time and cost by skipping developmental math courses. Especially for institutions that are experiencing developmental education reform and implementing the completion agenda, this case study provides practical implications to ensure student success and institutional effectiveness.
Examining multiple potential models in end-user interactive concept learning

End-user interactive concept learning is a technique for interacting with large unstructured datasets, requiring insights from both human-computer interaction and machine learning. This note re-examines an assumption implicit in prior interactive machine learning research, that interaction should focus on the question "what class is this object? ". We broaden interaction to include examination of multiple potential models while training a machine learning system. We evaluate this approach and find that people naturally adopt revision in the interactive machine learning process and that this improves the quality of their resulting models for difficult concepts.
Detection of tRNA Genes with Introns from DNA Sequences of Archaea

With the progress of genome projects, a vast amount of nucleotide sequence data is now available, which makes it possible to study the species-specific gene expression systems involving transcription and translation for a wide range of organisms. To understand species-specific translation system, it is important for comprehending the whole set of tRNAs for individual species. We have been developing a procedure for detecting tRNA genes from DNA sequences [3]. Some of the tRNA genes in Archaea and Eukaryotes have introns [1]. In the present study, we have improved the procedure to find them out.
Another lesson from the factor V Leiden mouse: thrombin generation drives arterial disease.

The discovery of activated protein C (APC) resistance by Dahlback in 19931 was a milestone in thrombophilia research that led to major advances in our understanding of the biochemistry, genetics, and clinical manifestations of hypercoagulability. APC resistance was quickly demonstrated to be caused by a single point mutation (1691G→A) in the coding region of the factor V gene.2 This mutation, factor V Leiden, is now known to be the most prevalent risk factor for venous thromboembolism (VTE) in people of European descent, occurring in 3% to 15% of the general population in Europe and North America.3 These observations ushered in a new era in the clinical evaluation of thromboembolism. For the first time, it became possible to diagnose a genetic defect (with a defined biochemical mechanism) in a substantial fraction of patients with venous thrombosis. Before the discovery of factor V Leiden, hereditary risk factors could be identified in <5% of patients presenting with VTE, even when a strong family history of thrombosis was obtained. In the current era, with widespread availability of genetic testing for factor V Leiden and another common hereditary risk factor, prothrombin 20210G→A,4 it is now possible to identify a genetic thrombophilic factor in 10% to 20% of unselected patients with VTE and up to 50% of patients with familial thromboembolism.5 See p 1822 The factor V Leiden protein contains glutamine instead of arginine at amino acid 506, which is a key site of cleavage by APC. This substitution confers resistance to APC and leads to increased thrombin generation through 2 mechanisms: (1) decreased APC-mediated inactivation of the procoagulant form of factor V (factor Va), and (2) decreased APC-mediated conversion of factor V to an anticoagulant form (factor Vac) that functions as a …
DRAM Caching

This paper presents methods to reduce memory latency in the main memory subsystem below the board-level cache. We consider conventional page-mode DRAMs and cached DRAMs. Evaluation is performed via trace-driven simulation of a suite of nine benchmarks. In the case of page-mode DRAMs we show that it can be detrimental to use page-mode naively. We propose two enhancements that reduce overall memory latency in this case: one is the remapping of address bits and the other is selective usage of page-mode under the control of the memory controller. In the case of cached DRAM we quantify the improvements that can be attained by introducing some SRAM cache on the DRAM chip. We evaluate various design alternatives for the line size and associativity of the SRAM cache.
The Relationship of Music to the Melody of Speech and to Syntactic Processing Disorders in Aphasia

Abstract: Two new empirical studies address the relationship between music and language. The first focuses on melody and uses research in phonetics to investigate the long‐held notion that instrumental music reflects speech patterns in a composer's native language. The second focuses on syntax and addresses the relationship between musical and linguistic syntactic processing via the study of aphasia, an approach that has been explored very little. The results of these two studies add to a growing body of evidence linking music and language with regard to structural patterns and brain processing.