Miracle-dz/UniFAR-FTUs huggingface dataset:
Backtracking matching pursuit with supplement set of arbitrary size

The idea of backtracking has been incorporated into the matching pursuit algorithms in sparse recovery, for example, subspace pursuit (SP) and compressive sampling matching pursuit (CoSaMP), to improve the recovery performance. In each iteration, a supplement set of size K or 2K is added to the candidate set to re-evaluate their reliability and then discard the unreliable indices, where K is the sparsity level of the original sparse signal. Yet the optimal choice of the size of the supplement set is still unclear. This paper aims to provide comprehensive analysis on the optimal choice of the size. The optimality is twofold: performance guarantees and computational complexity. By two theorems,we provide theoretical guarantees for the supplement set of arbitrary size, and computational complexity needed for perfect recovery. Numerical simulations demonstrate that a moderate size, such as 0.25K, results in computational efficiency without loss of recovery quality.
Nonlinear Interference Alignment in a One-dimensional Space

Real interference alignment is efficient in breaking-up a one-dimensional space over time-invariant channels into fractional dimensions. As such, multiple symbols can be simultaneously transmitted with fractional degrees-of-freedom (DoF). Of particular interest is when the one dimensional space is partitioned into two fractional dimensions. In such scenario, the interfering signals are confined to one sub-space and the intended signal is confined to the other sub-space. Existing real interference alignment schemes achieve near-capacity performance at high SNR for time-invariant channels. However, such techniques yield poor achievable rate at finite SNR, which is of interest from a practical point of view. In this paper, we propose a radically novel nonlinear interference alignment technique, which we refer to as Interference Dissolution (ID). ID allows to break-up a one-dimensional space into two fractional dimensions while achieving near-capacity performance for the entire SNR range. This is achieved by aligning signals by signals, as opposed to aligning signals by the channel. We introduce ID by considering a time-invariant MISO channel. This channel has a one-dimensional space and offers one DoF. We show that, by breaking-up the one dimensional space into two sub-spaces, ID achieves a rate of two symbols per channel use while providing $\frac{1}{2}$ DoF for each symbol. We analyze the performance of the proposed ID scheme in terms of the achievable rate and the symbol error rate. In characterizing the achievable rate of ID for the entire SNR range, we prove that, assuming Gaussian signals, the sum achievable rate is at most one bit away from the capacity. We present numerical examples to validate the theoretical analysis. We also compare the performance of ID in terms of the achievable rate performance to that of existing schemes and demonstrate ID's superiority.
A joint decode-and-forward strategy for physical network coding based on factor graphs

We consider physical network coding on a two-way relay channel. Two source nodes send their packets simultaneously over time-varying MIMO channels to a relay node. Assuming that the relay employs the joint decode-and-forward strategy, the relay must be able to decode the individual source packets. This task requires a robust estimation of the source-to-relay channels. We design a message passing algorithm based on factor-graphs to solve this problem efficiently.
Location-Based Adaptation for DASH in Vehicular Environment

In dynamic adaptive streaming over HTTP (DASH), a video file is encoded at different bitrates and segmented into small chunks. Each time, the most appropriate bitrate will be selected for a video chunk based on the network condition observed by streaming client. This may lead to video freezing or not efficiently using the network capacity due to unexpected bandwidth changes especially in fast moving environment. In this research we take advantage of geographical knowledge of bandwidth to predict the network behavior in next moments. Using simulation driven by real-world 3G bandwidth and vehicular mobility traces we achieved higher QoS when geographical throughput history was considered.
Resolution Enhancement by Incorporating Segmentation-based Optical Flow Estimation

In this paper, the problem of recovering a high-resolution frame from a sequence of low-resolution frames is considered. High-resolution reconstruction process highly depends on image registration step. Typical resolution enhancement techniques use global motion estimation technique. However, in general, video frames cannot be related through global motion due to the arbitrary individual pixel movement between frame pairs. To overcome this problem, we propose to employ segmentation-based optical flow estimation technique for motion estimation with a modified model for frame alignment. To do that, we incorporate the segmentation with the optical flow estimation in two-stage optical flow estimation. In the first stage, a reference image is segmented into homogeneous regions. In the second stage, the optical flow is estimated for each region rather than pixels or blocks. Then, the frame alignment is accomplished by optimizing the cost function that consists of L1-norm of the difference between the interpolated low-resolution (LR) frames and the simulated LR frames. The experimental results demonstrate that using segmentation-based optical flow estimation in motion estimation step with the modified alignment model works better than other motion models such as affine, and conventional optical flow motion models.
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.
From Real Cities to Virtual Habitats

This paper outlines the workflow from terrestrial and aerial images of urban areas to the reconstructed virtual models and the corresponding visualization. We combine new ideas as well as known methods from computer vision and computer graphics to accomplish this challenging task. The reconstruction and modeling steps within our workflow aim at the automatic content creation for virtual environments. The result of these steps is a virtual copy of an existing urban habitat. This set of data is further processed to obtain a scene database suitable for realtime visualization. One major component of the scene database is the LOD-tree, which allows efficient culling techniques to be applied and provides hierarchical levels of detail.
An Extension of an Ontology-Based Land Cover Designation Approach for Fuzzy Rules

Satellite image interpretation requires the assignment of sets of objects (or pixels) that share certain attribute values to object categories. This procedure requires expert intervention and knowledge. An approach has been developed that formalizes expert knowledge in the image interpretation procedure with ontologies. Ontologies provide a definition of object categories and associated attribute values that are known to represent these object categories. A classic ontology has the limitation that the definitions of object categories and their properties need to be crisp, i.e. not overlapping. Practical tests showed that less rigid definitions of class properties make the ontology-based approach more flexible and adaptable to different study areas and satellite images. This paper presents the extension of the ontology-based approach with fuzzy rules and discusses the advantages of this extension.
Gender-number dissociations in sentence production in spanish: 2710

In this study, we present evidence from, various sources to support the claim that gender and number have different processing loci in Spanish. After a short review of current linguistic accounts of the representation of gender and number in Spanish, we argue for a dissociation in the processing of gender and number information during sentence production, on the assumption that gender information is lexically specified whereas number information is retrieved and used in structure-building processes. Drawing on a taxonomy proposed for gender in Spanish nouns and adjectives, we report an analysis of speech error patterns and a study of word exchanges by instruction that provide converging evidence for our claims on dissociation. We also discuss the implications of our results for linguistic accounts of the representation of gender and number and for current psycholinguistic models of sentence production. *
Belatacept-Based Immunosuppression in De Novo Liver Transplant Recipients: 1-Year Experience From a Phase II Randomized Study

This exploratory phase II study evaluated the safety and efficacy of belatacept in de novo adult liver transplant recipients. Patients were randomized (N = 260) to one of the following immunosuppressive regimens: (i) basiliximab + belatacept high dose [HD] + mycophenolate mofetil (MMF), (ii) belatacept HD + MMF, (iii) belatacept low dose [LD] + MMF, (iv) tacrolimus + MMF, or (v) tacrolimus alone. All received corticosteroids. Demographic characteristics were similar among groups. The proportion of patients who met the primary end point (composite of acute rejection, graft loss, death by month 6) was higher in the belatacept groups (42–48%) versus tacrolimus groups (15–38%), with the highest number of deaths and grafts losses in the belatacept LD group. By month 12, the proportion surviving with a functioning graft was higher with tacrolimus + MMF (93%) and lower with belatacept LD (67%) versus other groups (90%: basiliximab + belatacept HD; 83%: belatacept HD; 88%: tacrolimus). Mean calculated GFR was 15–34 mL/min higher in belatacept‐treated patients at 1 year. Two cases of posttransplant lymphoproliferative disease and one case of progressive multifocal leukoencephalopathy occurred in belatacept‐treated patients. Follow‐up beyond month 12 revealed an increase in death and graft loss in another belatacept group (belatacept HD), after which the study was terminated.
Realizing Real-Time Centroid Detection of Multiple Objects with Marching Pixels Algorithms

In this paper we present a class of emergent algorithms called Marching Pixels which can be used for real time image processing in smart camera chips. Marching Pixels are based on hardware agents which are virtually crawling in a pixel grid image to find attributes like centroid, rotation and size of an arbitrary number of objects given in an image. Due to the distributed and local processing scheme of MPs reply times in milliseconds can be fulfilled. This means in that time is determined where pre-known objects are located and how they are oriented to the main axes of the image. We present an example Marching Pixels algorithm and corresponding application-specific and programmable architectures designedf or the use in embedded systems. The strengths and weaknesses of those architectures concerning the realization as FPGAs and ASICs are discussed by means of hardware synthesis results.
Towards service-oriented continuous queries in pervasive systems

Pervasive information systems give an overview of what digital environments should look like in the future. From a data-centric point of view, traditional databases have to be used alongside with non-conventional data sources like data streams, services and events. In this paper, we tackle the definition of continuous queries combining standard relations, data streams and services in a declarative language extending SQL. We first define virtual tables with binding patterns as a way to get a unified view of the pervasive environment. Relations, data streams and services can be homogeneously queried using a SQL-like language, on top of which query optimization can be performed. We also introduce a new clause defining the optimizing criteria to dynamically choose the best way to handle each event.
EnergyMap: Energy-efficient embedding of MapReduce-based virtual networks and controlling incast queuing delay

Energy consumption is a major concern for today's data centers. MapReduce is a cloud computing paradigm that is widely deployed in many data centers. In this paper, we propose EnergyMap, a novel energy-efficient embedding method that maps heterogeneous MapReduce-based virtual networks onto a heterogeneous data center network. EnergyMap also tries to constrain the incast queuing delay that may happen in Virtualized Data Centers (VDCs). Simulation results show that EnergyMap significantly reduces a data center's energy consumption and effectively controls the queuing delay caused by incast traffic.
Optimization for Timing-Robust Asynchronous Circuits Based on Eager Evaluation

As process, temperature and voltage variations become sign ifica t in deep submicron design, timing closure becomes a critical ch lenge using synchronous CAD flows. One attractive alternative is t o use robust asynchronous circuits which gracefully accommodate t iming discrepancies. However, such timing-robust asynchronous cir cuits typically suffer from high area and latency overhead. In this pap er, an optimization algorithm is presented which significantly re duces the area and delay of these circuits by relaxing their overly-re st ictive style. The proposed approach systematically transforms th e circuit which may contain arbitrarily complex blocks, allowing sel ected local nodes to perform eager evaluation, while still preserving t he overall timing-robustness of the circuit. The algorithm was implem ented and experiments performed on high-speed arithmetic circuits. U ing the block-level approach, on average, 38.4% of the blocks could be implemented in an eager manner, with area improvement of 27.2% , and delay improvement of 13.1% for the critical path. In the best case, 48.4% of the blocks could be implemented in an eager manner (1 6bit unoptimized multiplier) with area improvement of 49.7% (16-bit Kogge-Stone adder) and delay improvement of 25.5% (8-bit Ko gge-
An improved reduced support vector machine

The reduced support vector machine (RSVM) was proposed to overcome the computational difficulties as well as to reduce the model complexity in generating a nonlinear separating surface for a massive data set. However, it selects ‘support vectors’ randomly from the training set, this will effect the result. To overcome this shortcoming of RSVM, an improved RSVM algorithm is presented in this paper. First of all, we calculate the average of relative distance for each sample point in every class; and then use percentile to deal with unbalanced sample and remove the outliers form margin vectors, so the representative vectors as ‘support vectors’ were extracted; finally, we apply the RSVM on these representative vectors. Because we reduce the effect of unbalanced sample and outliers, and apply the representative vectors as ‘support vectors’, so the new algorithm improves the ability of RSVM to classify and the training speed of C-SVM .
Malicious code detection method over IPv4/IPv6 tunneling using Naive Bayesian classifier

As IPv4/IPv6 transition mechanism, there are a Dual Stack, Tunneling, and Translation. Among them, tunneling may be misused in order for an malicious code to avoid a firewall system or intrusion detection system. In this study, the methodology which classifies the normal traffic and malicious traffic in IPv4/IPv6 tunneling environment, by using 'Naive Bayes Classifier' which shows an excellent performance for a text categorization is discussed. In general, Internet worms or remote attack scripts include the certain features, that is, signature or machine instructions. Network packet can be supposed as one of general document. Accordingly, 'Naive Bayes Classifier' can be utilized for the network traffic analysis. This study indicates the method which can detects the new form's malicious code in IPv4/IPv6 transition environment by applying 'Naive Bayes Classifier', and it can be effectively applied to an encapsulated packet.
A Batch Algorithm for Estimating Trajectories of Point Targets Using Expectation Maximization

In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance.
A Complete Evaluation of the Chinese IP Geolocation Databases

Direct query to IP geolocation databases is a convenient and widespread way for determining geographic location where a host with corresponding IP address is. However, many databases available on the Internet may not provide accuracy data as they claim. In this paper, we focus on analyzing IP addresses of mainland China recorded in popular China-based IP geolocation databases. First, distributions and characteristics of IP addresses and blocks of mainland China at different geographic granularities are analyzed. Second, entries consistency comparisons among databases are implemented and those with better performances are recommended, we also use small amount of ground truth data to verify the recommended databases. Finally, clustering and classification analyses are conducted for IP blocks with different locations and preliminary conclusions are drawn.
On optimum fixed-rate causal scalar quantization design for causal video coding

Causal video coding is a coding paradigm where video source frames are encoded in a frame by frame manner, the encoder for each frame can use all previous frames and all previous encoded frames, and the corresponding decoder can use only all previous encoded frames. In this paper, the design of causal video coding is considered from an information theoretic perspective by modeling each frame as a stationary information source. We first put forth a concept called causal scalar quantization. By extending the classic Lloyd-Max algorithm for a single source to this multiple sources case, we then propose an algorithm for designing optimum fixed-rate causal scalar quantizers for causal video coding to minimize the total distortion among all sources. The proposed algorithm converges in the sense that the total distortion cost is monotonically decreasing until a stationary point is reached. Simulation results show that in comparison with fixed-rate predictive scalar quantization, fixed-rate causal scalar quantization offers as large as 16% quality improvement (distortion reduction).
Conceptualising interaction in live performance: reflections on 'encoded'

This paper presents a detailed examination of experiences of the creative team responsible for the direction, choreography, interaction design and performance of a dance and physical theatre work, Encoded. Interviews, observations and reflection on personal experience have made visible a range of different perspectives on the design, use and creative exploration of the interactive systems that were created for the work. The work itself, and in particular the use of interactive systems, was overall considered to be successful and coherent, even while participants' approaches and concerns were often markedly different. A trajectory of creative development in which exploratory improvisation and iterative design gradually became 'locked down' in preparation for final performance and touring is described.
Development of Anatomically Realistic Numerical Breast Phantoms With Accurate Dielectric Properties for Modeling Microwave Interactions With the Human Breast

Computational electromagnetics models of microwave interactions with the human breast serve as an invaluable tool for exploring the feasibility of new technologies and improving design concepts related to microwave breast cancer detection and treatment. In this paper, we report the development of a collection of anatomically realistic 3-D numerical breast phantoms of varying shape, size, and radiographic density which can readily be used in finite-difference time-domain computational electromagnetics models. The phantoms are derived from T1-weighted MRIs of prone patients. Each MRI is transformed into a uniform grid of dielectric properties using several steps. First, the structure of each phantom is identified by applying image processing techniques to the MRI. Next, the voxel intensities of the MRI are converted to frequency-dependent and tissue-dependent dielectric properties of normal breast tissues via a piecewise-linear map. The dielectric properties of normal breast tissue are taken from the recently completed large-scale experimental study of normal breast tissue dielectric properties conducted by the Universities of Wisconsin and Calgary. The comprehensive collection of numerical phantoms is made available to the scientific community through an online repository.
Effective Active Noise Control algorithm for gain and power constraints with limiting factor using adaptive filter

In this paper, a new algorithm with multi constraints that can be used in active noise control is presented. This can be used to maintain the particular system power budget and prevent over driving. The filter used for the constraints is frequency domain of least mean square algorithm. A limiting factor is used in the algorithm to reduce the effect of the filter response at other frequencies. The algorithm can be either used in gain or output power constraint. The algorithm shows the better convergence rate than the existing algorithms. The finding of correct convergence point can be done through the secondary path.