Miracle-dz/UniFAR-FTUs huggingface dataset:
Chinese developmental dyslexia:audio-visual integration ERP study

ObjectiveTo explore the neuropsychological feature of developmental dyslexia, especially in audio-visual integration. MethodsThe combinations of lateral visual stimuli with pure tones auditory stimuli were presented and the scalp potentials were recorded by ERP analyses system. ResultsFirstly, the significant main effect exits between two children in auditory-related brain potential such as P_ 60, N160.Secondly, the main effect of latency of P 110 is significant. The latency of dyslexia is more delayed markedly than that of normal. Thirdly, the accuracy of dyslexia is lower than that of normal but the response time doesn't reach the significant level. ConclusionThe processing of audio-visual integration of development dyslexia is in according with the model of interaction. The significant delay of latency and the marked descent of amplitude perhaps are the main neuropsychological character in dyslexia. On the other hand, the behavior results of dyslexia differ from single modal. Those results elucidate dyslexia may have limited mental resource that they couldn’t deal with so much signals.
Flexible nonuniform filter banks using allpass transformation of multiple order

This paper deals with allpass frequency transformations of uniform filter banks to achieve nonuniform band-widths. The known transformation with an allpass of first order [1] [4] [5] [6] is extended to an allpass transformation of order K. Thus the flexibility of the filter bank design can be increased significantly.
Bamboo-Dominated Forests of the Southwest Amazon: Detection, Spatial Extent, Life Cycle Length and Flowering Waves

We map the extent, infer the life-cycle length and describe spatial and temporal patterns of flowering of sarmentose bamboos (Guadua spp) in upland forests of the southwest Amazon. We first examine the spectra and the spectral separation of forests with different bamboo life stages. False-color composites from orbital sensors going back to 1975 are capable of distinguishing life stages. These woody bamboos flower produce massive quantities of seeds and then die. Life stage is synchronized, forming a single cohort within each population. Bamboo dominates at least 161,500 km2 of forest, coincident with an area of recent or ongoing tectonic uplift, rapid mechanical erosion and poorly drained soils rich in exchangeable cations. Each bamboo population is confined to a single spatially continuous patch or to a core patch with small outliers. Using spatial congruence between pairs of mature-stage maps from different years, we estimate an average life cycle of 27–28 y. It is now possible to predict exactly where and approximately when new bamboo mortality events will occur. We also map 74 bamboo populations that flowered between 2001 and 2008 over the entire domain of bamboo-dominated forest. Population size averaged 330 km2. Flowering events of these populations are temporally and/or spatially separated, restricting or preventing gene exchange. Nonetheless, adjacent populations flower closer in time than expected by chance, forming flowering waves. This may be a consequence of allochronic divergence from fewer ancestral populations and suggests a long history of widespread bamboo in the southwest Amazon.
An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns

Finger-vein, as a secure and convenient biometric characteristic in nature, has been widely studied for authentication in recent years. In this paper, we propose an efficient finger-vein extraction algorithm based on random forest training and regression with efficient local binary pattern feature. By integrating with a vein pattern matching method which is robust to finger misalignment, we achieved state-of-the-art finger-vein recognition. Thorough experiments have been conducted on two popular databases to prove the effectiveness and robustness of the proposed method.
Image smoothing using a metric tensor for an affine invariant scale space

This paper proposes a new image smoothing method using a metric tensor for affine invariant scale space. In the field of image processing and recognition, Gaussian filtering is a common procedure for image smoothing. For example, scale space construction based on Gaussian filtering is sometimes used as a preprocessing of various image processing tasks. However Gaussian filtering is not affine invariant. This paper proposes a new method for image smoothing that is invariant under such affine transformation that does not change the area of any region in the image. It is shown that a scale space representation can be constructed collaterally with the image smoothing. Experimental results show that the proposed method is almost never affected by affine transformation different from usual Gaussian filtering. In the proposed method, processing results are expected to be not affected much by variation of the viewpoint.
Speech Emotion Recognition Using Hybrid Generative and Discriminative Models

In this paper, we use Sequential Forward Selection to select 8 dimensional frame-level features from the total 69 dimensional features, and we reduce the dimensions of utterance-level eigenvectors from 63 to 12 by fisher discriminant. Then, two kinds of GMM multidimensional likelihoods are proposed for hybrid generative and discriminative models. Experimental results on Berlin emotional speech databases show that the GMM-MAP/SVM series hybrid model is the optimal Hybrid Generative and Discriminative Models, with the recognition rate up to 85.1%.
Under Consideration for Publication in Knowledge and Information Systems Estimating Robustness in Large Social Graphs

Given a large social graph, what can we say about its robustness? Broadly speaking, the property of robustness is crucial in real graphs, since it is related to the structural behavior of graphs to retain their connectivity properties after losing a portion of their edges/nodes. Can we estimate a robustness index for a graph quickly? Additionally, if the graph evolves over time, how this property changes? In this work, we are trying to answer the above questions studying the expansion properties of large social graphs. First, we present a measure which characterizes the robustness properties of a graph, and also serves as global measure of the community structure (or lack thereof). We show how to compute this measure efficiently by exploiting the special spectral properties of real-world networks. We apply our method on several diverse real networks with millions of nodes, and we observe interesting properties for both static and time evolving social graphs. As an application example, we show how to spot outliers and anomalies in graphs over time. Finally, we examine how graph generating models that mimic several properties of real-world graphs, behave in terms of robustness dynamics.
The Crew Workload Manager

This paper presents an open loop adaptive system intended to address workload imbalances in future, high-workload flight decks. Air traffic in Europe is expected to more than double by 2020. New technologies being proposed will significantly add to pilot roles and responsibilities, and has the potential to add further periods of high workload to pilot operations. The CAMMI (Cognitive Adaptive Man Machine Interface) program addresses human factors priorities in the aviation domain by developing concepts that balance operator workload, support added future operator roles and responsibilities and resulting new task and information requirements, while allowing operators to focus on the most safety critical tasks. The Crew Workload Manager (CWLM) is a research prototype that objectively measures, compares, and displays the workload between pilots, and can recommend task sharing or automate lower order tasks as necessary. It is expected that the CWLM will minimize the time pilots spend in unbalanced workload conditions, and thereby reduce errors and pilot fatigue, and improve crew resource management. An evaluation plan is outlined that utilizes the novel Shared Aviation Task Battery.
An Eclipse-based tool framework for software model management

Software development involves the use of many models and Eclipse provides an ideal infrastructure for building tools to support the use of models. While there is a large selection of tools available for working with individual models, there is less support for working with collections of models, as for example, when a collection of models from different sources must be merged. We have identified the problem of working with collections of related models in software development as the Software Model Management (SMM) problem - a close cousin of the Model Management problem in the area of metadata management. In the course of building SMM tools to address particular scenarios, we have observed that they share common foundations both at the theoretical and implementation levels. In this paper, we describe the vision and initial development of a framework that implements these common foundations in order to facilitate and accelerate the development of Eclipse-based SMM tools.
Reliable data delivery in sparse WSNs with multiple Mobile Sinks: An experimental analysis

Urban sensing is emerging as a significant Wireless Sensor Networks (WSNs) application. In such a scenario, static sensors are sparsely deployed in an urban area to collect environmental information. Sensed data are opportunistically collected by Mobile Sinks (MSs), which can be other sensor nodes attached to cars or buses, or carried by people while they move around the city. Since the contacts between the MSs and the static sensors are infrequent and short, reliable and energy efficient data collection is a primary concern of such applications. To this end, we exploit a hybrid data delivery scheme based on both Erasure Coding (EC) and feedback by the MSs. We provide an optimized implementation, and show by extensive experiments in a real testbed that the proposed approach is feasible, despite the very limited storage and processing resources of commercially available sensor platforms.
A 12b ENOB, 2.5MHz-BW, 4.8mW VCO-based 0–1 MASH ADC with direct digital background nonlinearity calibration

A direct digital background calibration technique to correct nonlinearity errors in VCO-based 0-1 MASH ΣΔ ADCs is presented. The proposed technique altogether corrects VCO gain error, nonlinearity, and capacitor mismatch of the residue generating DAC. It improves SNDR of the prototype ADC from 60dB to 73.4dB in 2.5MHz signal bandwidth. The ADC consumes 4.8mW from 1.8V supply in 180nm CMOS. The measured convergence time is only 64ms.
Deniable Storage Encryption for Mobile Devices

Smartphones, and other mobile computing devices, are being widely adopted globally as the de-facto personal computing platform. Given the amount of sensitive information accumulated by these devices, there are serious privacy and security implications for both personal use and enterprise deployment. Confidentiality of data-at-rest can be effectively preserved through storage encryption. All major mobile OSes now incorporate some form of storage encryption. In certain situations, this is inadequate, as users may be coerced into disclosing their decryption keys. In this case, the data must be hidden so that its very existence can be denied. Steganographic techniques and deniable encryption algorithms have been devised to address this specific problem. This dissertation explores the feasibility and efficacy of deniable storage encryption for mobile devices. A feature that allows the user to feign compliance with a coercive adversary, by decrypting plausible and innocuous decoy data, while maintaining the secrecy of their sensitive or contentious hidden data. A deniable storage encryption system, Mobiflage, was designed and implemented for the Android OS, the first such application for mobile devices. Current mobile encryption mechanisms all rely, in some way, on a user secret. Users notoriously choose weak passwords that are easily guessed/cracked. This thesis offers a new password scheme for use with storage encryption. The goal is to create passwords that are suitably strong for protection of encryption keys, easier to input on mobile devices, and build on memorability research in cognitive psychology for a better user experience than current password guidelines.
Dual Tree Complex Wavelet Transform and Robust Organizing Feature Map in Medical Image Fusion Technique

Abstract-Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) techniques in medical image has revolutionized for medical diagnosis. From CT coordinate system, Intensity based image registration is used in MRI image. Image fusion is an advanced tool in medical imaging. To make a medical diagnosis easier and accurate, image fusion combines MRI and CT images into a single image, which contains the relevant information from the original source images. Image fusion technique is based on Dual Tree Complex Wavelet Transform (DT-CWT) and Robust Self Organizing Feature Mapping (Robust SOFM) neural network. DT_CWT decomposes the images in a multi-scale and multi-direction to extract important features. The Robust SOFM neural network is utilized to recognize the complementary features. These features are integrated using a criteria based on activity level. Fused of both MR, CT images are made from a fused feature set. Finally the fused image is attained by executing Inverse Dual Tree Complex Wavelet Transform (IDTCWT). The experimental results show that the proposed algorithm can significantly outperform image fusion technique and has a better Peak Signal to Noise Ratio (PSNR) value as compared with other transforms, DWT, FDCT, NSCT, and DTCWT.
Cortical state and natural movie responses in cat visual cortex

Abstract How does cortical state affect neural responses to naturalistic stimuli, and is it analogous between anesthetized and awake animals? We recorded spikes and local field potential (LFP) from all layers of isoflurane-anesthetized cat primary visual cortex (V1) while repeatedly presenting wide-field natural scene movie clips. Spiking responses of single units were remarkably precise, reliable and sparse, with lognormally distributed mean firing rates. Many units had distinct barcode-like firing patterns, with features as little as 10 ms wide. LFP-derived cortical state switched spontaneously between synchronized (1/f) and desynchronized (broadband). Surprisingly, responses were more precise, reliable and sparse during the synchronized than desynchronized state. Because the desynchronized state under anesthesia is thought to correspond to attending periods in awake animals, during which responses are enhanced, our results complicate the analogy between cortical states in anesthetized and awake animals. The presenceof orientation maps in cat V1 may explain contrary reports in anesthetized rodents, and predicts a similar result in anesthetized ferret and primate V1. Significance Statement Global brain activity changes spontaneously over time and can be characterized along a spectrum from slow synchronized activity, to fast desynchronized activity. This spectrum is similar in awake, asleep and anesthetized animals, but is its effect on neural responses the same in all cases? Here we show that neural responses to natural movies in anesthetized cat visual cortex are more precise during synchronized activity. This is contrary to reports in anesthetized rodents, which we suggest may be due to greater columnar organization in cat visual cortex. Since this is also contrary to enhanced responses and behavioural performance during attention, when activity is desynchronized, our results suggest that similar brain states in awake and anesthetized animals may not be functionally analogous.
Rapid-response machine translation for unexpected languages

Statistical techniques for machine translation offer promise for rapid development in response to unexpected requirements, but realizing that potential requires rapid acquisition of required resources as well. This paper reports the results of experiments with resources collected in ten days; about 1.3 million words of parallel text from five types of sources and a bilingual term list with about 20,000 term pairs. Systems were trained with resources individually and in combination, using an approach based on alignment templates. The use of all available resources was found to yield the best results in an automatic evaluation using the BLEU measure, but a single resource (the Bible) coupled with a small amount of in-domain manual translation (less than 6,000 words) achieved more than 85% of that upper baseline. With a concerted effort, such a system could be built in a single day.
Distributed index mechanism based on Hadoop

Recent years, MapReduce has aroused much attention. However, MapReduce has its own weakness- require an entire block scan as it cannot precisely locate the query result. Currently, there are already some researches that have built index on Hadoop, but some of them could only deal with full-text search, which cannot support dataset with certain schema. There's not yet a general distributed unstructured data index system optimized from MapReduce that could handle multi-schema dataset and support query well no matter with index or without index. So in this paper, we proposed a distributed index mechanism and set up this index mechinism on MapReduce which can reduce its query time and map task number in some context. Moreover, this distributed index mechanism could support multi-schema dataset, has a good scalability and is customizable. From our experiment, we find our distributed index mechanism could save up to 30% query time, and 90% map task number in some context compared to the query performance of original MapReduce framework, and the advantage grows as the dataset expands.
Convergence Proofs of Least Squares Policy Iteration Algorithm for High-Dimensional Inflnite Horizon Markov Decision Process Problems

Most of the current theory for dynamic programming algorithms focuses on finite state, finite action Markov decision problems, with a paucity of theory for the convergence of approximation algorithms with continuous states. In this paper we propose a policy iteration algorithm for infinite-horizon Markov decision problems where the state and action spaces are continuous and the expectation cannot be computed exactly. We show that an appropriately designed least squares (LS) or recursive least squares (RLS) method is provably convergent under certain problem structure assumptions on value functions. In addition, we show that the LS/RLS approximate policy iteration algorithm converges in the mean, meaning that the mean error between the approximate policy value function and the optimal value function shrinks to zero as successive approximations become more accurate. Furthermore, the convergence results are extended to the more general case of unknown basis functions with orthogonal polynomials.
The Challenges for High Performance Embedded Systems

Consumer electronics devices traditionally rely on non-programmable circuits for their "streaming" part. Recent demands on flexibility moved the balance towards the use of programmable components. However, there is a major gap between the current programmable processors and the actual requirements of applications. To bridge this gap, it is necessary to use parallel architectures consisting of multiple, programmable compute blocks, specifically designed for efficient processing of data streams. Programming those architectures poses major challenges and requires appropriate tools. Managing the ever-increasing complexity of those embedded systems is certainly one of the most important challenges beside power consumption. Complexity will make systems unreliable and unpredictable. The new technology nodes (65nm, and below) will also bring their own additional challenges: the global interconnect delay that does not scale, the predominant leakage current and the increasing variability of components
MIMO evaluation platform for fast hardware baseband reconfiguration of advanced wireless networks

Communication systems will have to support an ever increasing number of transmission standards. In this context, the reconfiguration capability appears as a promising solution for designing multi-standard terminals with a limited impact on the size, cost and power consumption budgets. This paper describes a hardware realtime reconfigurable hardware platform, developed jointly by CEA-LETI and MERCE in the context of the CELTIC project WINNER+. This platform, implements a basic 3GPP/LTE system composed of a terminal and a base station. The terminal baseband architecture is based on the MAGALI ASIC developed by CEA-LETI. Based on a Network-on-Chip (NoC) technology, it allows the dynamic reconfiguration of the system. The base station relies on a more traditional FPGA prototyping platform. The reconfiguration is showcased by switching the transmission mode from a 3GPP/LTE SIMO (1×2) mode to a 3GPP/LTE compliant 2×2 MIMO mode. Consequently, the platform is used to measure the benefit of spatial transmission diversity scheme envisioned for the evolutions of 3GPP/LTE (LTE-A) system compared to basic transmission such as specified in the LTE standard.
Disseminating Ambient Assisted Living in Rural Areas

The smart home, ambient intelligence and ambient assisted living have been intensively researched for decades. Although rural areas are an important potential market, because they represent about 80% of the territory of the EU countries and around 125 million inhabitants, there is currently a lack of applicable AAL solutions. This paper discusses the theoretical foundations of AAL in rural areas. This discussion is underlined by the achievements of the empirical field study, Casa Vecchia, which has been carried out over a four-year period in a rural area in Austria. The major goal of Casa Vecchia was to evaluate the feasibility of a specific form of AAL for rural areas: bringing AAL technology to the homes of the elderly, rather than moving seniors to special-equipped care facilities. The Casa Vecchia project thoroughly investigated the possibilities, challenges and drawbacks of AAL related to this specific approach. The findings are promising and somewhat surprising and indicate that further technical, interactional and socio-psychological research is required to make AAL in rural areas reasonable in the future.
Visually lossless image compression extension for JPEG based on just-noticeable distortion evaluation

A visually lossless image encoding extension for JPEG is presented. Such extension enables an efficient implementation of perceptual coding by reusing existing widespread software libraries and hardware IP cores for JPEG. For any pixel in a decoded image, the proposed algorithm guarantees a maximum distortion bounded by the just-noticeable distortion (JND) threshold measured based on the input image. Perceptual coding is performed in three steps: (1) standard transform domain coding, (2) spatial domain distortion visibility analysis by JND model and (3) spatial domain residual coding. Such scheme has been implemented in this work as an extension for JPEG based on a low complexity JND model. The encoder determines if a pixel block in a standard JPEG output image contains distortions beyond the visibility threshold given by the JND model. If it is true then the locations and the values of such distortions are encoded as side information. Quantization step size for the distortion values, i.e. perceptual residuals, are chosen based on the visibility threshold. Experimental results show that in terms of compression efficiency, the proposed perceptual encoding extension outperforms the standard JPEG encoder by 50% for a visually lossless compression of images.