Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Complexity Dynamic Element Matching DAC for Direct Digital Synthesis

This paper presents and analyzes a new dynamic element matching technique for low-harmonic distortion digital-to-analog conversion. The benefit of this technique over the prior art is a significantly reduced hardware complexity with no reduction in performance. It is particularly appropriate for applications such as direct digital synthesis (DDS) in wireless communications systems, where low hardware complexity and low harmonic distortion are essential.
2016 Ieee International Conference on Big Data (big Data) Point of Interest Recommendation with Social and Geographical Influence

—Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly.
Efficient Architecture for Spike Sorting in Reconfigurable Hardware

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.
Good Vibrations: A Modal Analysis Approach for Sequential Non-rigid Structure from Motion

We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis. Our method models non-rigid deformations as a linear combination of some mode shapes obtained using modal analysis from continuum mechanics. The shape is first discretized into linear elastic triangles, modelled by means of finite elements, which are used to pose the force balance equations for an undamped free vibrations model. The shape basis computation comes down to solving an eigenvalue problem, without the requirement of a learning step. The camera pose and time varying weights that define the shape at each frame are then estimated on the fly, in an online fashion, using bundle adjustment over a sliding window of image frames. The result is a low computational cost method that can run sequentially in real-time. We show experimental results on synthetic sequences with ground truth 3D data and real videos for different scenarios ranging from sparse to dense scenes. Our system exhibits a good trade-off between accuracy and computational budget, it can handle missing data and performs favourably compared to competing methods.
Turbo decoding on tailored OpenCL processor

Turbo coding is commonly used in the current wireless standards such as 3G and 4G. However, due to the high computational requirements, its software-defined implementation is challenging. This paper proposes a static multi-issue exposed datapath processor design tailored for turbo decoding. In order to utilize the parallel processor datapath efficiently without resorting to low level assembly programming, the turbo decoder is implemented using OpenCL, a parallel programming standard for heterogeneous devices. The proposed implementation includes only a small set of Turbo-specific custom operations to accelerate the most critical parts of the algorithm. Most of the computation is performed using general-purpose integer operations. Thus, the processor design can be used as a general-purpose OpenCL accelerator for arbitrary integer workloads as well. The proposed processor design was evaluated both by implementing it using a Xilinx Virtex 6 FPGA and by ASIC synthesis using 130 nm and 40 nm technology libraries. The implementation achieves over 63 Mbps Turbo decoding throughput on a single low-power core. According to the ASIC synthesis, the maximum operating clock frequency is 344 MHz/1 050 MHz (130 nm/40 nm).
Scheduling jobs on computational grid using differential evolution algorithm

Large scale computation is frequently limited to the performance of computer hardware or associated cost. Due to the development of information and network technologies, idle computers all over the world can be utilized and organized to enhance overall computation performance. Grid environments facilitate distributed computation. Hence the scheduling of grid jobs should be considered as an important issue. This paper introduces a novel approach based on Differential Evolution algorithm for scheduling jobs on computational grid. The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time.
Approximation-Aware Dependency Parsing by Belief Propagation

We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O(n3) runtime. It outputs the parse with maximum expected recall—but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as others have done for loopy CRFs (Domke, 2010; Stoyanov et al., 2011; Domke, 2011; Stoyanov and Eisner, 2012). The resulting parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.
Lithuanian Dependency Parsing with Rich Morphological Features

We present the first statistical dependency parsing results for Lithuanian, a morphologically rich language in the Baltic branch of the Indo-European family. Using a greedy transition-based parser, we obtain a labeled attachment score of 74.7 with gold morphology and 68.1 with predicted morphology (77.8 and 72.8 unlabeled). We investigate the usefulness of different features and find that rich morphological features improve parsing accuracy significantly, by 7.5 percentage points with gold features and 5.6 points with predicted features. As expected, CASE is the single most important morphological feature, but virtually all available features bring some improvement, especially under the gold condition.
Human Neural Stem Cells: Isolation, Expansion and Transplantation

Neural stem cells, with the capacity to self renew and produce the major cell types of the brain, exist in the developing and adult rodent central nervous system (CNS). Their exact function and distribution is currently being assessed, but they represent an interesting cell population, which may be used to study factors important for the differentiation of neurons, astrocytes and oligodendrocytes. Recent evidence suggests that neural stem cells may also exist in both the developing and adult human CNS. These cells can be grown in vitro for long periods of time while retaining the potential to differentiate into nervous tissue. Significantly, many neurons can be produced from a limited number of starting cells, raising the possibility of cell replacement therapy for a wide range of neurological disorders. This review summarises this fascinating and growing field of neurobiology, with a particular focus on human tissues.
Tutorial on Connective Morphology

Morphological operators may be taken up from the two entries of dilation and of connection. This tutorial focuses on the second entry, and leads to optimum partitionings of the images under study. Five notions, which derive from each other, are successively explored. The “set connection,” which generalizes the usual connectivities, opens the series, and yields the “connective segmentation,” which associates connection with maximum partition. Then the “connected operators” allow to construct “hierarchies of partitions” whose the laws of “optimal cuts” are given.
Feedback-controlled robotics-assisted treadmill exercise to assess and influence aerobic capacity early after stroke: a proof-of-concept study

Abstract Purpose: The majority of post-stroke individuals suffer from low exercise capacity as a secondary reaction to immobility. The aim of this study was to prove the concept of feedback-controlled robotics-assisted treadmill exercise (RATE) to assess aerobic capacity and guide cardiovascular exercise in severely impaired individuals early after stroke. Method: Subjects underwent constant load and incremental exercise testing using a human-in-the-loop feedback system within a robotics-assisted exoskeleton (Lokomat, Hocoma AG, CH). Inclusion criteria were: stroke onset ≤8 weeks, stable medical condition, non-ambulatory status, moderate motor control of the lower limbs and appropriate cognitive function. Outcome measures included oxygen uptake kinetics, peak oxygen uptake (VO2peak), gas exchange threshold (GET), peak heart rate (HRpeak), peak work rate (Ppeak) and accuracy of reaching target work rate (P-RMSE). Results: Three subjects (18–42 d post-stroke) were included. Oxygen uptake kinetics during constant load ranged from 42.0 to 60.2 s. Incremental exercise testing showed: VO2peak range 19.7–28.8 ml/min/kg, GET range 11.6–12.7 ml/min/kg, and HRpeak range 115–161 bpm. Ppeak range was 55.2–110.9 W and P-RMSE range was 3.8–7.5 W. Conclusions: The concept of feedback-controlled RATE for assessment of aerobic capacity and guidance of cardiovascular exercise is feasible. Further research is warranted to validate the method on a larger scale. Implications for Rehabilitation Aerobic capacity is seriously reduced in post-stroke individuals as a secondary reaction to immobility. Robotics-assisted walking devices may have substantial clinical relevance regarding assessment and improvement of aerobic capacity early after stroke. Feedback-controlled robotics-assisted treadmill exercise represents a new concept for cardiovascular assessment and intervention protocols for severely impaired individuals.
Effective Fair Pricing of International Mutual Funds

We propose a new methodology to provide fair prices of international mutual funds by adjusting prices at the individual security level using a comprehensive and economically relevant information set. Stepwise regressions are used to endogenously determine the stock-specific optimal set of factors. Using 16 synthetic funds whose characteristics are extracted from 16 corresponding actual US-based Japanese mutual funds, we demonstrate that our method estimates fund prices significantly more accurately than existing methods. Although existing fair-pricing methods provide an improvement over the current practice of simply using Japanese market closing prices, they are still highly vulnerable to exploitation by market-timers. By contrast, our method is the most successful in preventing such strategic exploitation since no competing method can profit from our stated prices.
Adaptive CUSUM for Anomaly Detection and Its Application to Detect Shared Congestion

It is a major challenge for a detection algorithm to maintain high detection probability and low false alarms simultaneously. In this paper, we propose an adaptive CUSUM algorithm (ACS) to robustly detect an anomaly, which is defined as system behavior that deviates from its expected values. By embedding a sliding model control (SMC) controller into a CUSUM detector, ACS effectively prevents unlimited build-up of the accumulator, i.e., the yn variable in CUSUM, during the anomaly period. This way, ACS effectively detects change points at on-set and termination of an anomaly period, while satisfying the requirements of detection and false alarm time. For general performance evaluation, we found that ACS is more stable than the Neyman-Pearson detector, and also overcomes the tardy detection of anomaly termination of the CUSUM algorithm. We further reduce the problem of shared congestion to that of anomaly detection, where shared congestion is regarded as anomaly and independent congestion as normality, using the delay correlation of two probing streams as metric. Existing solutions employed accumulated samples to detect shared con- gestion. We proposed a sliding window scheme to capture the change of congestion states. The simulation results showed that window scheme with ACS can detect the switch of the shared vs. independent congestion for each detection instance.
Hemodynamic and Neuroendocrine Responses to Acute and Chronic Alpha‐adrenergic Blockade with Prazosin and Phenoxybenzamine

We investigated the relevance of the selective a1-adrenergic receptor blockade produced by prazosin to its blood pressure-lowering efficacy in man. The hemodynamic and neuroendocrine responses to the acute and chronic oral administration of prazosin and phenoxybenzamine were compared in a randomized, double-blind, placebo-controlled, crossover study of 11 patients with essential hypertension. These responses were also evaluated during lower body negative pressure and dynamic bicycle exercise, which produce potent but diversified activation of the sympathetic nervous system. In the acute studies, arterial blood pressure decreased to similar levels with prazosin or phenoxybenzamine; however, hemodynamic and neuroendocrine responses differed both before and during sympathetic nervous system activation. Prazosin lowered arterial blood pressure by reducing total peripheral resistance (p < 0.05). In contrast, phenoxybenzamine produced a modest reduction in cardiac output (8%, p < 0.05) with little change in total peripheral resistance, forearm vascular resistance or forearm blood flow. Additionally, plasma norepinephrine concentration and heart rate rose to significantly higher levels with prazosin (p < 0.02) than with phenoxybenzamine, a difference that was most evident with lower body negative pressure or dynamic exercise. Baroreceptor control of arterial pressure homeostasis was preserved with both agents, except during marked degrees of cardiovascular stress. With chronic therapy, the circulatory responses adapted to the a-adrenergic antagonists, and both drugs produced similar hemodynamic and neuroendocrine profiles. The differences with acute administration may be the result of a more rapid onset of action and a more marked degree of a-adrenergic blockade with prazosin than with phenoxybenzamine therapy, rather than to any difference in their a1- and a2-adrenergic receptor blocking properties. Moreover, the findings of the present study suggest that the prejunctional a2-receptor, autoinhibitory to sympathetic neuronal norepinephrine release, is of no functional significance in patients with essential hypertension.
A User Configurable Metric for Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are comprised of thousands of nodes that are embedded with limited energy resources. Clustering is a well-known technique that can be used to extend the lifetime of such a network. However, user adaption is one criterion that is not taken into account by current clustering algorithms. Here, the term “user” refers to application developer who will adjust their preferences based on the application specific requirements of the service they provide to application users. In this paper, we introduce a novel metric named Communication Distance (ComD), which can be used in clustering algorithms to measure the relative distance between sensors in WSNs. It is tailored by user configuration and its value is computed from real time data. These features allow clustering algorithms based on ComD to adapt to user preferences and dynamic environments. Through experimental and theoretical studies, we seek to deduce a series of formulas to calculate ComD from Time of Flight (ToF), Radio Signal Strength Indicator (RSSI), node density and hop count according to some user profile.
Generic Environment for Full Automation of Benchmarking

Regression testing is an important part of software quality assurance. We work to extend regression testing to include regression benchmarking, which applies benchmarking to detect regressions in performance. Given the specific requirements of regression benchmarking, many contemporary benchmarks are not directly usable in regression benchmarking. To overcome this, we present a case for designing a generic benchmarking environment that will facilitate the use of contemporary benchmarks in regression benchmarking, analyze the requirements and propose architecture of such an environment.
Dry storage of spent nuclear fuel

The idea of dry storage of spent nuclear fuel has been welcomed because of concern that pool storage beyond 20 years will lead to corrosion and cracking of fuel cladding. Modes for dry storage include air-cooled vaults, dry wells, silos, and storage casks. Advantages and disadvantages of the dry storage method are discussed. (JMT)
State of the art in off-line writer identification of handwritten text and survey of writer identification of Arabic text

In this paper we present the state of the art in writer identification and verification of handwritten text. In addition, a special and extensive survey of writer identification and verification of Arabic handwritten text is also included. Different feature extraction techniques are addressed showing the different research groups’ efforts as well as individual efforts. The different classification approaches, e.g. minimum distance classifiers and statistical classifiers, used for identification by writer and verification by different groups and individuals are presented. Identification results of surveyed publications are investigated and tabulated for ease of reference. Examples of writer identification and verification of others languages are addressed. An extensive survey of databases used in writer identification and verification for Latin and Arabic text is presented. Conclusions relevant to writer identification of Arabic text are discussed and future directions stated. Key words: Writer identification, writer verification, handwritten database, feature extraction, handwriting analysis, distance measures, literature survey.
LIFT: LIKELIHOOD-FREQUENCY-TIME ANALYSIS FOR PARTIAL TRACKING AND AUTOMATIC TRANSCRIPTION OF MUSIC

We propose a new method for analysing the time-frequency domain, called LiFT. It is especially designed for partial tracking in a polyphonic automatic transcription model. After the signal passes through a Q-constant filter bank, composed of twenty four quartertone filters, it is analysed thanks to a generalized maximum likelihood approach. Two hypotheses are tested: the first one is that the output signal of a filter is a cosine plus noise, the second one is that it corresponds to colored noise. This likelihood analysis is developed in two ways: temporally treating the samples and frequentially treating the short time Fourier transform of the signal. For these two approaches, we have tested the robustness to noise and the cosine detection power.
FGF6 mediated expansion of a resident subset of cells with SP phenotype in the C2C12 myogenic line

Fibroblast growth factor 6 (FGF6) is selectively expressed during muscle development and regeneration. We examined its effect on muscle precursor cells (mpc) by forcing stable FGF6 expression in C2C12 cells in vitro. FGF6 produced in genetically engineered mpc was active, inducing strong morphological changes, altering cell adhesion and compromising their ability to differentiate into myotubes. Expression of MyoD and myogenin, but not of Myf5, was abrogated in FGF6 engineered mpc. These effects were reversed by FGF inhibitors. Ectopic expression of MyoD also restored fiber formation indicating that FGF6 interferes with the myogenic differentiation pathway upstream of MyoD. We also report that in the presence of FGF6, the minor (0.5–2%) subpopulation of cells actively excluding Hoechst 33342 in a verapamil‐dependent manner (SP phenotype) was increased to 15–20% and the expression of the mdr1a gene (but not mdr1b) was upregulated by 400‐fold. Our data establish a previously undescribed link between FGF6—a muscle specific growth factor—and a multidrug resistance gene expressed in stem cells, and suggest a role for FGF6 in the maintenance of a reserve pool of progenitor cells in the skeletal muscle. © 2004 Wiley‐Liss, Inc.
Dilated canine hearts: A specimen for teaching cardiac anatomy

Dilated canine hearts were used to teach undergraduate students internal and external cardiac anatomy. The specimens were dilated using hydrostatic pressure and then fixed using 5% formalin. These specimens provided the students with an alternative to prepackaged embalmed hearts and anatomical models for studying the external and internal cardiac anatomy. In addition, the dilated hearts are more similar to the organ they encounter within the thoracic cavity of their dissected animal. This gives the students a better chance of developing a three‐dimensional understanding of the heart. Anat Sci Ed 1:207–211, 2008. © 2008 American Association of Anatomists.
Deformation Characterization of Mouse Oocyte Cell Using Inverse Finite Element and Levenberg–Marquardt Optimization Algorithm in Needle Injection Experiment

In order to better understand the mechanical properties of biological cells, characterization and investigation of their material behavior is necessary. In this paper hyperelastic Neo-Hookean material is used to characterize the mechanical properties of mouse oocyte cell. It has been assumed that the cell behavior is continues, isotropic, nonlinear and homogenous material. Then, by matching the experimental data with finite element (FE) simulation result and using the Levenberg–Marquardt optimization algorithm, the nonlinear hyperelastic model parameters have been extracted. Experimental data of mouse oocyte captured from literatures. Advantage of the developed model is that it can be used to calculate accurate reaction force on surgical instrument or it can be used to compute deformation or force in virtual reality based medical simulations.Copyright © 2012 by ASME