Miracle-dz/UniFAR-FTUs huggingface dataset:
Low bit rate high-quality MDCT audio coding of the 3GPP EVS standard

This paper presents a low bit-rate MDCT coder, which is adopted as a part of the recently standardized codec for Enhanced Voice Services. To maximize codec performance for NB to SWB input signals for low bit-rates (7.2 to 16.4 kbps), new adaptive bit-allocation and spectrum quantization schemes, which emphasize perceptually important spectrum while efficiently coding full spectrum, was introduced into the low bit-rate MDCT coder. Further, small symbol switched Huffman coding is exploited for reducing the bits consumption for quantizing band energies of the spectrum. Finally, the performance of the coder is illustrated with some listening test results.
Combined effect of NBTI and channel hot carrier effects in pMOSFETs

This work shows that the channel hot carrier (CHC) degradation for a p-MOSFET consists of two different regimes. At low V/sub g/, the degradation is dominated by hot electrons produced by impact ionization. The hot electrons are responsible for the creation of both interface traps and electron traps within the oxide. At high V/sub g/, a NBT-induced hot carrier effect is evidenced as well as an anomalous CHC effect. This work should help understanding the CHC degradation of pMOSFET as well as determining the worst case degradation.
Quantitative [Fe]MRI of PSMA-targeted SPIONs specifically discriminates among prostate tumor cell types based on their PSMA expression levels

We report the development, experimental verification, and application of a general theory called [Fe]MRI (pronounced fem-ree) for the non-invasive, quantitative molecular magnetic resonance imaging (MRI) of added magnetic nanoparticles or other magnetic contrast agents in biological tissues and other sites. [Fe]MRI can easily be implemented on any MRI instrument, requiring only measurements of the background nuclear magnetic relaxation times (T1, T2) of the tissue of interest, injection of the magnetic particles, and the subsequent acquisition of a pair of T1-weighted and T2-weighted images. These images, converted into contrast images, are subtracted to yield a contrast difference image proportional to the absolute nanoparticle, iron concentration, ([Fe]) image. [Fe]MRI was validated with the samples of superparamagnetic iron oxide nanoparticles (SPIONs) both in agarose gels and bound to human prostate tumor cells. The [Fe]MRI measurement of the binding of anti-prostate specific membrane antigen (PSMA) conjugated SPIONs to PSMA-positive LNCaP and PSMA-negative DU145 cells in vitro allowed a facile discrimination among prostate tumor cell types based on their PSMA expression level. The low [Fe] detection limit of ~2 μM for SPIONs allows sensitive MRI of added iron at concentrations considerably below the US Food and Drug Administration’s human iron dosage guidelines (<90 μM, 5 mg/kg).
If You Can ’ t Take the Heat , Get Out of the Region : Spatial Competition and Strategic Firm Relocation

Firms compete spatially for customers who have some degree of brand loyalty. The number and attractiveness of the alternatives available to these customers dictates the level of competition facing firms. In this study, a model of spatial pricing in the presence of heterogeneous customer preferences is used to specify an empirical analysis of spatial competition. Results, using spatial autoregressive techniques and ticket pricing data from professional sports, suggest that sports franchises spatially compete when pricing their tickets, and that the level of spatial competition has partially influenced the relocation of teams over time.
High throughput hardware architectures for asymmetric numeral systems entropy coding

In this paper two new hardware-based entropy coding architectures for asymmetric numeral systems are introduced, as entropy encoding is one of the major phases in a compression algorithm. The proposed architectures are based on tabled asymmetric numeral systems (tANS). The tabled asymmetric numeral systems combines the speed advantage of table based approaches (e.g. Huffman encoding) with the higher compression rate advantage of arithmetic encoding. Both proposed architectures have been synthesized to a state-of-the-art FPGA, and the synthesis results show high encoding throughput. The architectures are capable of encoding one symbol per clock cycle. The performance of the architectures depends on the number of symbols in the alphabet and may vary from 146 up to 290 Mega symbols per second (Msps).
Significant Event Detection in Sports Video Using Audio Cues

This work presents audio cue based approaches for event detection from sports video for construction of sports video summary. The approach has been proved effective applied to soccer and cricket videos. The approach extracts the event based on audio level, the sports matches always have mass spectators who show there response in the form of loud cheering, applause and referees signal also. These audio symbols can be detected by measure audio level and pattern which is normally higher than regular audio level. The co-relation between audio and video frames will help to extract frames and summarize these audio samples to combine event, all such events expected together can be used for summary. Proposed Methodology detects the peak values of the audio samples present in the sports video streams and looks for the video frame corresponding to the audio peak detected. The video event detection is generated as AVI streams using the video frames around the audio peaks detected. The experimental results found to match the manual examination and ground truth of the various sports videos.
Temporal variation of elemental carbon in Guangzhou, China, in summer 2006

R. L. Verma, L. K. Sahu, Y. Kondo, N. Takegawa, S. Han, J. S. Jung, Y. J. Kim, S. Fan, N. Sugimoto, M. H. Shammaa, Y. H. Zhang, and Y. Zhao Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan Advanced Environmental Monitoring Research Center, Department of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea Institute of Environmental Meteorology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China Atmospheric Remote Sensing Section, National Institute for Environmental Studies, Tsukuba, Japan State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Science and Engineering, Peking University, Beijing, China Air Quality Research Center, University of California, Davis, USA
Low Birth Weight Prediction Based on Maternal and Fetal Characteristics

Newborn size is an important indicator of infant survival and childhood morbidity [1] and appears to be related to subsequent risk of type 2 diabetes, hypertension, cardiovascular disease, and other disorders [2], [3]. Therefore, many studies have attempted to identify sources of variation in newborn size. The purpose of this study is to determine whether accurate prediction of term birth weight is possible based on maternal and fetal characteristics routinely measured remote from term. Multiple linear regressions is deployed to define which combinations of these variables are significant using real data collected in a maternity clinical and birth weight prediction equations are developed. The models are then used to predict the delivery weight for the Low Birth Weight (LBW) babies. The efficacy of the predication models are assessed and compared based on their mean and standard error of the predicted weights. The paper proposes two regression models based on some measurable characteristics of both mother and fetal. The models can explain 62.9% and 59.4% of the delivery weight variation for the low birth weight babies. The proposed models were then used to estimate the recorded weights together with their corresponding 95% confidence and predication intervals for the LBW babies. The results indicate that the most significant factors for the reduced regression model are head circumference, gestation age, and fetal length. The reduced model can explain 59.4% of the delivery weight variation for the low birth weight babies. While the regression model based on the above predictors as well as mother hemoglobin level, chest circumference and mother height and BMI can explain 62.9% of the delivery weight variation for the low birth weight babies.
Triplifying Wikipedia's Tables

We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called "Wikitables"). We present a survey of Wikitables and their content in a recent dump of Wikipedia. We then discuss some ongoing work on using DBpedia to mine novel RDF triples from these tables: we present methods that automatically extract 24.4 million raw triples from the Wikitables at an estimated precision of 52.2%. We believe this precision can be (greatly) improved through machine learning methods and sketch ideas for features that should help classify (in)correct triples.
Data Quality Assessment via Robust Clustering

Although data mining is popularly used in business and industry to improve the quality of decision making, data quality is long time ignored in many practices so that the analytical results derived by data mining methods are usually questionable and unreliable to represent useful knowledge and aid decision making. This paper proposed a generic framework for data quality assessment in nonhomogeneous environments based on robust clustering analysis. In particular, trimmed clustering methods are proposed to robustly characterize groups of similar observations and trimmed observations are then evaluated to assess outlying-ness based on their distance with the cluster profiles. Simulation studies have shown the eectiveness of the proposed framework.
Design and Implementation of Controller for a Nonlinear Spherical Tank System using Soft computing Techniques

In this work, Particle Swarm Optimization (PSO) based intelligent controller design technique is attempted for a nonlinear spherical tank process. The search process is guided by an objective function, which considers the minimization of some key parameters such as Integral Square Error (ISE), Integral Absolute Error (IAE), and settling time (ts) of the process under study. The proposed controller tuning procedure is initially tested on the mathematical model of the spherical tank system with a chosen operating region. A detailed simulation study is also performed with the chosen operating regions in order to validate the performance of proposed controller design technique and the results are validated with fuzzy controller. The simulation results evident that, the PSO based PI controller offers better steady state response and performance indices for the considered operating region. Finally, the PSO tuned PI controller is implemented and tested on a laboratory scale real time nonlinear spherical tank system. The real-time responses evident that, proposed controller offers enhanced result for both reference tracking and disturbance rejection operations with an improved performance measure values.
A Text Image Segmentation Method Based on Spectral Clustering

We present a novel approach for solving the text segmentation problem in natural scene images. The proposed algorithm uses the normalized graph cut(Ncut) as the measure for spectral clustering, and the weighted matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. Thus, the proposed algorithm requires much smaller spatial costs and much lower computation complexity. Experiments show the superior performance of the proposed method compared to the typical thresholding algorithms.
Foxo3 circular RNA retards cell cycle progression via forming ternary complexes with p21 and CDK2

Most RNAs generated by the human genome have no protein-coding ability and are termed non-coding RNAs. Among these include circular RNAs, which include exonic circular RNAs (circRNA), mainly found in the cytoplasm, and intronic RNAs (ciRNA), predominantly detected in the nucleus. The biological functions of circular RNAs remain largely unknown, although ciRNAs have been reported to promote gene transcription, while circRNAs may function as microRNA sponges. We demonstrate that the circular RNA circ-Foxo3 was highly expressed in non-cancer cells and were associated with cell cycle progression. Silencing endogenous circ-Foxo3 promoted cell proliferation. Ectopic expression of circ-Foxo3 repressed cell cycle progression by binding to the cell cycle proteins cyclin-dependent kinase 2 (also known as cell division protein kinase 2 or CDK2) and cyclin-dependent kinase inhibitor 1 (or p21), resulting in the formation of a ternary complex. Normally, CDK2 interacts with cyclin A and cyclin E to facilitate cell cycle entry, while p21works to inhibit these interactions and arrest cell cycle progression. The formation of this circ-Foxo3-p21-CDK2 ternary complex arrested the function of CDK2 and blocked cell cycle progression.
K-Modes Clustering Algorithm in Solving Data Mining Problems for Mushroom Dataset

The research work presents K-modes clustering algorithm in solving Data mining problems for agro-based dataset. Mushroom data set available from the UCI data repository were analyzed to identify different combinations of attributes that are significant in grouping the mushroom data as poisonous or edible. Clustering is unsupervised learning that aims at partitioning a data set into groups of similar items. The goal is to create clusters of data objects where the within-cluster similarity is maximized (intra-cluster similarity) and the between-cluster similarity is minimized (inter-cluster similarity). K-modes clustering algorithm was used to cluster mushroom datasets. The system was developed using Java programming language and Object Oriented Methodology (OOM) was applied because, OOM promotes simplicity, reusability, reliability and increase the development speed. The clustered result of mushroom dataset described hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota family. Each species was identified as definitely edible, definitely poisonous or of unknown edibility and not recommended. And we also find the most poisonous Trichoderma green that mould infection in edible basidiomycetes. It has been known to be able to cause a drastical decrease in production or even entire crops can be wiped out. The Nigerian Agri-Business is presently looking for researchers to partner in order to promote agro-based products free from any kind of defects, which the paper tried to achieve.
Yang Long-Term Synaptic Modification Contribution of Individual Spikes in Burst-Induced

[PDF] [Full Text] [Abstract] , September 23, 2009; 29 (38): 11891-11903. J. Neurosci. Alon Polsky, Bartlett Mel and Jackie Schiller Pyramidal Neurons Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 [PDF] [Full Text] [Abstract] , December 2, 2009; 29 (48): 15286-15298. J. Neurosci. Ignacio Sáez and Michael J. Friedlander Plasticity between Neuronal Pairs in Layer 4 of Visual Cortex Varies with Synapse State [PDF] [Full Text] [Abstract] , November 24, 2010; 30 (47): 15713-15725. J. Neurosci. Jary Y. Delgado, José F. Gómez-González and Niraj S. Desai Pyramidal Neuron Conductance State Gates Spike-Timing-Dependent Plasticity [PDF] [Full Text] [Abstract] , January 26, 2011; 31 (4): 1516-1527. J. Neurosci. Minoru Honda, Hidetoshi Urakubo, Keiko Tanaka and Shinya Kuroda Model with Spike Timing-Dependent Plasticity Analysis of Development of Direction Selectivity in Retinotectum by a Neural Circuit [PDF] [Full Text] [Abstract] , April , 2011; 105 (4): 1908-1917. J Neurophysiol Jonathan Y. Shih, Craig A. Atencio and Christoph E. Schreiner cortex Improved stimulus representation by short interspike intervals in primary auditory
Continuous Flash Suppression Modulates Cortical Activity in Early Visual Cortex

A salient visual stimulus can be rendered invisible by presenting it to one eye while flashing a mask to the other eye. This procedure, called continuous flash suppression (CFS), has been proposed as an ideal way of studying awareness as it can make a stimulus imperceptible for extended periods of time. Previous studies have reported robust suppression of cortical activity in higher visual areas during CFS, but the role of primary visual cortex (V1) is still controversial. In this study, we resolve this controversy on the role of V1 in CFS and also begin characterizing the computational processes underlying CFS. Early visual cortical activity was measured with functional magnetic resonance imaging while human subjects viewed stimuli composed of target and mask, presented to the same or different eyes. Functional MRI responses in early visual cortex were smaller when target and mask were in different eyes compared with the same eye, not only for the lowest contrast target rendered invisible by CFS, but also for higher contrast targets, which were visible even when presented to the eye opposite the mask. We infer that CFS is based on modulating the gain of neural responses, akin to reducing target contrast.
Workshop on Task Planning for Intelligent Robots in Service and Manufacturing

Automated task planning for robots is usually implemented on a motion primitive domain, where the focus is on constructing meaningful, general motion primitives. In this work we propose planning on the higher abstraction level of robot skills. In this context, skills are general, functional blocks that contain both sensing and action, have a welldefined expected outcome and set of preconditions, and are thus immediately useful for planning. By using a world model, which is focused on the skills, we show that this is in fact the case. We show automated task planning and execution, as a sequence of skills and their parameters, based on the desired goal state and the current state from the world model. Experiments show that the approach is immediately applicable, given a skill-equipped robot, and that inconsistencies between the world model and physical world are overcome simply by replanning.
Optimized tracking using VAIT for moving objects

This Automated object tracking and recognition system is very essential in terms of surveillance system and many more applications. Nevertheless correctness of these applications still remains the prior challenge. In this paper a simple procedure is proposed to develop a system that monitors a video and detect the objects that are moving and then recognize the objects comparing with our proposed method. Which is primarily targeted to stop stealing of vehicles from parking lot. This particular method can be further extended for many other applications. We use optical flow to detect moving objects and construct a data model of that particular object from different viewpoints. Applying VAIT on those certain regions that has got optical flow we try to figure out the matched object from our data models. Which boosts the performance and reduces the computation significantly. A moving vehicle can be in different angle at a given time. The tangible challenge for this paper is to recognition those objects.
Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or “social talk”) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called “out-of-domain” (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads.
Patterns in SNMP-Based Management

A lot of activity is currently going on to replace the SNMP management architecture with a solution better suited to managing modern IP networks and systems. New candidates include Management by delegation, active networks, and Web-based management. In this exercise, the management community runs the risk of throwing the baby out with the bath water by focusing too much on a few well-known problems exhibited by SNMP (e.g., its poor scalability) and neglecting most of its other characteristics, including those that contributed to its success (e.g., the reasons why it is simple). One way to avoid this is to explicitly capture the experience gained in the management of IP networks and systems with SNMP. In this paper, we make one step in this direction by studying the SNMP management architecture through a software engineer s eyes: we identify in SNMP some of the fundamental architectural and design patterns defined in the literature. Patterns are schematic, proven solutions to recurring problems. By characterizing the current management architecture in terms of patterns, we help retain the strengths of SNMP-based management in future management architectures. We also make it easier for new software engineers to move to network and systems management by characterizing this application domain in standard pattern terms, as opposed to using the jargon understood solely by the SNMP community.
Honeydew standing crop and production over 24 hours in Nothofagus solandri forest in Canterbury

2, all 740 active individual insects were mapped by their anal threads and honeydew production was recorded every three hours over 24 hours. Mean production of honeydew per insect over 24 hours was 0.169 µl, but ranged from zero (4% of all active insects) to 11.5 µl. Standing crop peaked just after dawn, and production was apparently higher at night. Removing drops every three hours and covering the quadrats to exclude animals had no significant effect on honeydew production. The greatest variation was between the 16 trees. Ultracoelostoma densities were higher on trees with higher daily honeydew production per insect. The honeydew production per unit area over 24 hours represented 3.2 to 23.0 times the standing crop, depending on what time of day the latter was measured; for the daylight samples, production was 11.5 times the mean standing crop.
Subchannel assignment in OFDM relay nodes

The paper considers a source-relay-destination link in which the relay node is allowed to reassign the input subchannels to different output subchannels. In particular, we consider a channel-aware relay node and compute the subchannel reassignment that optimizes a selected performance criterion. The numerical results suggest that optimized subchannel reassignment is particularly beneficial in frequency-selective channels and in channels where interference information is available at transmitter.