Miracle-dz/UniFAR-FTUs huggingface dataset:
Packet multiplexing transmission technique in wireless ad-hoc networks

This paper proposes a packet multiplexing transmission technique for wireless ad-hoc networks. Furthermore, we present and discuss our network and architecture concept. Preliminary numerical estimations show that the proposed multiplexing technique may not only increase the throughput, but also suppress the average end-to-end delay degradation in a multi-hop environment
Rapid development of an English/Farsi speech-to-speech translation system.

Significant advances have been achieved in Speech-to-Speech (S2S) translation systems in recent years. However, rapid configuration of S2S systems for low-resource language pairs and domains remains a challenging problem due to lack of human translated bilingual training data. In this paper, we report on an effort to port our existing English/Iraqi S2S system to the English/Farsi language pair in just 90 days, using only a small amount of training data. This effort included developing acoustic models for Farsi, domain-relevant language models for English and Farsi, and translation models for English-to-Farsi and Farsi-to-English. As part of this work, we developed two novel techniques for expanding the training data, including the reuse of data from different language pairs, and directed collection o f new data. In an independent evaluation, the resulting system achieved the highest performance of all systems.
SIMPRESS: A Simulator Generation Environment for System-on-Chip Exploration

of the Thesis SIMPRESS: A Simulator Generation Environment for System-on-Chip Exploration by Asheesh Khare Master of Science in Information and Computer Science University of California, Irvine, 1999 Professor Nikil D. Dutt, Chair Designing Systems-On-Chip (SOC) involves designing embedded applications using customizable embedded processor cores, novel on-chip/o -chip memory hierarchies and highly customizable blocks. Customizable processor cores and other customizable parts imply that the accompanying tool-kit (compiler, simulator, debugger etc.) be easily customizable too. A customizable tool-kit generation approach produces high quality tools from an architecture speci cation. Customizable tool-kits also enable early Design space exploration, improved time-tomarket. In this work we describe SIMPRESS, an environment for generation of simulators for processor-memory architectures. It forms the core component of V{SAT (Visual Speci cation and Analysis Tool), a tool for performing design space exploration of System-On-Chip (SOC) architectures. The other component of V{SAT is a processor-architecture speci cation language, EXPRESSION. In addition to providing early design space exploration in the V{SAT environment, the structural, Cycle-accurate simulators generated from SIMPRESS can be used viii for detailed design of SOC architectures, for application development through code analysis and for testing and evaluating retargetable compilers. Unlike other approaches, SIMPRESS can be used to generate processor simulators for diverse classes of processors such as RISC, VLIW, superscalar etc. We give a detailed description of SIMPRESS and an overview of the V{SAT system (including EXPRESSION, and a GUI for evaluation/analysis) within which it can be integrated. We also present a design space exploration scenario using an example DLX architecture,demonstrating the usefulness of SIMPRESS in exploration for an embedded SOC codesign ow by specifying and evaluating several modi cations to the pipeline structure of the processor. We have used SIMPRESS to model simulators for the DLX, C62X and an almost true size version of the R10000 processors, demonstrating its use in modeling a variety of processor classes. ix CHAPTER
SIPS: Solar Irradiance Prediction System

There is high interest in up-scaling capacities of renewable energy sources such as wind and solar. However, variability and uncertainty in power output is a major concern and forecasting is, therefore, a top priority. Advancements in forecasting can potentially limit the impact of fluctuations in solar power generation, specifically in cloudy days when the variability and dynamics are the largest. We propose SIPS, Solar Irradiance Prediction System, a novel sensing infrastructure using wireless sensor networks (WSNs) to enable sensing of solar irradiance for solar power generation forecasting. In this paper, we report the findings of a deployment of a hierarchical WSN system consisting of 19 TelosB nodes equipped with solar irradiance sensors, and 5 MicaZ nodes equipped with GPS boards, deployed in the vicinity of a 1 MW solar array. We evaluate different irradiance sensor types and the performance of different novel prediction methods using SIPS' data and show that the spatial-temporal cross-correlations between sensor node readings and solar array output power exists and can be exploited to improve prediction accuracy. Using this data for short-term solar forecasting for cloudy days with very high dynamics in solar output power generation - the worst case scenario for prediction-, we get an average of 97.24% accuracy in our prediction for short time horizon forecasting and 240% reduction of predicted normalized root mean square error (NRMSE) compared to state-of-the-art methods that do not use SIPS data.
Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection

Chandrasekaran, Parrilo, and Willsky (2012) proposed a convex optimization problem for graphical model selection in the presence of unobserved variables. This convex optimization problem aims to estimate an inverse covariance matrix that can be decomposed into a sparse matrix minus a low-rank matrix from sample data. Solving this convex optimization problem is very challenging, especially for large problems. In this letter, we propose two alternating direction methods for solving this problem. The first method is to apply the classic alternating direction method of multipliers to solve the problem as a consensus problem. The second method is a proximal gradient-based alternating-direction method of multipliers. Our methods take advantage of the special structure of the problem and thus can solve large problems very efficiently. A global convergence result is established for the proposed methods. Numerical results on both synthetic data and gene expression data show that our methods usually solve problems with 1 million variables in 1 to 2 minutes and are usually 5 to 35 times faster than a state-of-the-art Newton-CG proximal point algorithm.
Improvement Of PFC Boost Converter Energy Performance Using Silicon Carbide Diode

This paper discusses the possible benefits of the application of silicon carbide devices in power electronic circuits. The theoretical considerations are illustrated with the measurement results of a 500 W power factor correction (PFC) boost converter. The energy performance of the original converter has been improved owing to the application of silicon carbide Schottky barrier diode (SBD). As expected from the theory, the SiC boost diode allowed the decrease of power losses, thus improving converter efficiency. The ultimate measure of the converter energy performance used by the authors throughout this paper was the product of the energy efficiency and the power factor
Plasma Lp(a) levels correlate with number, severity, and length-extension of coronary lesions in male patients undergoing coronary arteriography for clinically suspected coronary atherosclerosis.

The relation between lipoprotein(a) [Lp(a)] as an independent risk factor for coronary atherosclerosis and the severity and extension of angiographically detectable coronary atherosclerotic lesions has not been systematically evaluated. In 118 male patients (54.3 +/- 7.4 years) with suspected coronary artery disease and without a history of myocardial infarction undergoing coronary angiography, the relation between plasma Lp(a) levels and other lipoproteins and the severity and extension of coronary lesions was studied. The coronary angiograms were evaluated in a blinded manner according to three scores: vessel score (0 to 3 points for 0 to 3 vessels with stenoses > or = 70%), stenosis score (0 to 32 points; number and severity of coronary stenoses or lesions), and extent score (0 to 100 points; length-extension of all coronary lesions in relation to the total coronary vessel length). The score values obtained were analyzed for correlations with age and levels of total cholesterol (6.08 +/- 1.26 mmol/L; mean +/- SD), high-density lipoprotein cholesterol (1.04 +/- 0.33 mmol/L), low-density lipoprotein cholesterol (4.18 +/- 1.15 mmol/L), triglycerides (1.88 +/- 1.37 mmol/L), and Lp(a) in plasma (19.5 +/- 22.6 mg/dL). Bivariate correlation analysis resulted in positive correlations between Lp(a) and vessel score (P < .01), stenosis score (P < .01), and extent score (P < .05). With multivariate analyses, besides Lp(a) plasma level (nl), only patient age showed a significant correlation to all three scores used, whereas none of the lipid parameters correlated significantly with all three scores.
A 1.06 μW smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring

A smart wearable electrocardiographic (ECG) processor is presented for secure ECG-based biometric authentication and cardiac monitoring, including arrhythmia and anomaly detection. Data-driven Lasso regression and low-precision techniques are developed to compress the neural networks by 24.4X. The prototype chip fabricated in 65 nm LP CMOS consumes 1.06 μW at 0.55 V for real-time ECG authentication. Equal error rates of 0.74% and 1.7% are achieved on ECG-ID database and in-house 645-subject database, respectively.
A reciprocal relationship between cutaneous nerves and repairing skin wounds in the developing chick embryo.

Various studies have suggested that the rate of adult skin healing may be in some way dependent on signals emanating from cutaneous nerves. Further, it appears that adult wounds become hyperinnervated by sensory nerves during the process of healing. In order to investigate this reciprocal relationship further, we have used a simple embryonic model to look at the effect of wounds on nerves, and conversely, the effect of nerves on wounds. We find that wounds made to the dorsum of the chick wing bud, at a stage prior to normal innervation (at E4), or soon after the normal establishment of cutaneous innervation (at E7), subtly alter the pattern of branching by perturbing developmental guidance cues, but do not cause hyperinnervation, whereas wounding at E14 does cause hyperinnervation. By creating chicks with nerveless wings, we show that from E7, wound healing in the absence of nerves is significantly impaired. These observations suggest that, from the earliest stages of skin innervation, the presence of nerves is beneficial to the healing process, but that, in contrast to neonatal and adult tissues, wound healing in the embryo and early foetus does not trigger hyperinnervation.
Virtual data center networks embedding through Software Defined Networking

Software Defined Networking (SDN) has opened new ways to design, deploy, and operate networks with new abstractions and programmability at network control and data planes. In this paper, we approach SDN to embed virtual data center networks employing the Network-as-a-Service model. The proposed architecture is built upon abstractions to create a virtual topology using BGP configurations that allow an efficient mapping to a physical network of OpenFlow 1.3 switches. In the control plane, an algorithm is designed to perform efficient allocation of network resources to virtual paths based on the data plane state, such as allocated virtual networks and resource utilization metrics. Requirements such as bandwidth and resilience are used to define the tenants policies and construct the virtual topology graph mappings. The experimental evaluation on an emulated testbed shows that the proposed algorithm performs efficient load balancing and altogether yields better utilization of the physical resources under different tenant traffic patterns.
Genetic programming for simultaneous feature selection and classifier design

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.
Minimum-Energy Filtering for Attitude Estimation

In this work, we study minimum-energy filtering for attitude kinematics with vectorial measurements using Mortensen's approach. The exact form of a minimum-energy attitude observer is derived and is shown to depend on the Hessian of the value function of an associated optimal control problem. A suitably chosen matrix representation of the Hessian operator leads to a Riccati equation that approximates a minimum-energy attitude filter. An extended version of the proposed approximate filter is included for a situation where there is slowly time-varying bias in the gyro measurements. A unit quaternion version of the proposed filter is derived and shown to outperform the multiplicative extended Kalman filter (MEKF) for situations with large initialization errors or large measurement errors.
Fast algorithms for differential equations in positive characteristic

We address complexity issues for linear differential equations in characteristic <i>p</i> >;0: resolution and computation of the <i>p</i>-curvature. For these tasks, our main focus is on algorithms whose complexity behaves well with respect to <i>p</i>. We prove bounds linear in <i>p</i> on the degree of polynomial solutions and propose algorithms for testing the existence of polynomial solutions in sublinear time <i>Õ</i>(<i>p</i><sup>1/2</sup>), and for determining a whole basis of the solution space in quasi-linear time <i>Õ</i>(<i>p</i>); the <i>Õ</i> notation indicates that we hide logarithmic factors. We show that for equations of arbitrary order, the <i>p</i>-curvature can be computed in subquadratic time <i>Õ</i>(<i>p</i><sup>1.79</sup>), and that this can be improved to <i>O</i>(log(<i>p</i>)) for first order equations and to <i>Õ</i>(<i>p</i>) for classes of second order equations.
Genetic Variation, Structure, and Gene Flow in a Sloth Bear (Melursus ursinus) Meta-Population in the Satpura-Maikal Landscape of Central India

Sloth bears (Melursus ursinus) are endemic to the Indian subcontinent. As a result of continued habitat loss and degradation over the past century, sloth bear populations have been in steady decline and now exist only in isolated or fragmented habitat across the entire range. We investigated the genetic connectivity of the sloth bear meta-population in five tiger reserves in the Satpura-Maikal landscape of central India. We used noninvasively collected fecal and hair samples to obtain genotypic information using a panel of seven polymorphic loci. Out of 194 field collected samples, we identified 55 individuals in this meta-population. We found that this meta-population has moderate genetic variation, and is subdivided into two genetic clusters. Further, we identified five first-generation migrants and signatures of contemporary gene flow. We found evidence of sloth bears in the corridor between the Kanha and Pench Tiger Reserves, and our results suggest that habitat connectivity and corridors play an important role in maintaining gene flow in this meta-population. These corridors face several anthropogenic and infrastructure development threats that have the potential to sever ongoing gene flow, if policies to protect them are not put into action immediately.
Comparing usage of a large high-resolution display to single or dual desktop displays for daily work

With the ever increasing amount of digital information, users desire more screen real estate to process their daily computing work, and might well benefit from using a wall-size large high-resolution display instead of a desktop one. Unfortunately, we know very little about users' behaviors when using such a display for daily computing. We present a week-long study that investigates large display use in a personal desktop computing context by comparing it with single and dual desktop monitor use. Results show users' unanimous preference for using a large display: it facilitates multi-window and rich information tasks, enhances users' awareness of peripheral applications, and offers a more"immersive experience. Further, the data reveals distinct usage patterns in partitioning screen real estate and managing windows on a large display. Detailed analysis of these results provides insights into designing interaction techniques and window management systems more suited to a large display.
Establishing Interaction between Machine and Medaka using Deep Q-Network

Social interaction is the basic ability for animals to survive. It is difficult for a machine to interact with human or other animals because it is not clear how the machine should interact. This paper examines whether an artificial dot controlled by a machine can interact with a medaka and induce a desired behavior. The dot is displayed on a monitor. We use deep Q network (DQN) to learn how to move the dot. As a result, the DQN could learn some basic elements to interact with the medaka and the desired behavior could be induced.
Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking

In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD) scheme. We describe a new ASLT algorithm based on a particle filtering (PF) approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.
New Adaptive Algorithms for Online Classification

We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any generic loss function. The mistake bounds can be specialized for the hinge loss, allowing to recover and improve the bounds of known online classification algorithms. By optimizing the general bound we derive a new online classification algorithm, called NAROW, that hybridly uses adaptive- and fixed- second order information. We analyze the properties of the algorithm and illustrate its performance using synthetic dataset.
Uptake of ant-derived nitrogen in the myrmecophytic orchid Caularthron bilamellatum.

BACKGROUND AND AIMS Mutualistic ant-plant associations are common in a variety of plant families. Some myrmecophytic plants, such as the epiphytic orchid Caularthron bilamellatum, actively form hollow structures that provide nesting space for ants (myrmecodomatia), despite a substantial loss of water-storage tissue. This study aimed at assessing the ability of the orchid to take up nitrogen from ant-inhabited domatia as possible trade-off for the sacrifice of potential water storage capacity. METHODS Nitrogen uptake capabilities and uptake kinetics of (15)N-labelled compounds (NH(4)(+), urea and l -glutamine) were studied in field-grown Caularthron bilamellatum plants in a tropical moist forest in Panama. Plants were either labelled directly, by injecting substrates into the hollow pseudobulbs or indirectly, by labelling of the associated ants in situ. KEY RESULTS Caularthron bilamellatum plants were able to take up all tested inorganic and organic nitrogen forms through the inner surface of the pseudobulbs. Uptake of NH(4)(+) and glutamine followed Michaelis-Menten kinetics, but urea uptake was not saturable up to 2 mm. (15)N-labelled compounds were rapidly translocated and incorporated into vegetative and reproductive structures. By labelling ants with (15)N in situ, we were able to prove that ants transfer N to the plants under field conditions. CONCLUSIONS Based on (15)N labelling experiments we were able to demonstrate, for the first time, that a myrmecophytic orchid is capable of actively acquiring different forms of nitrogen from its domatia and that nutrient flux from ants to plants does indeed occur under natural conditions. This suggests that beyond anti-herbivore protection host plants benefit from ants by taking up nitrogen derived from ant debris.
Learning rhythmic movements by demonstration using nonlinear oscillators

ATR Human Information Science Laboratories, Kyoto, JapanEmail: ijspeert@usc.edu, jun@his.atr. co.jp, sschaal@usc.eduAbstractThis paper presents a new approach to the generationof rhythmic movement patterns with nonlinear dy-namical systems. Starting from a canonical limit cy-cle oscillator with well-de ned stability properties, wemodify the attractor landscape of the canonical sys-temby meansof statisticallearning methods toembedarbitrary smooth target patterns, however, withoutlosing the stability properties of the canonical sys-tem. In contrast to non-autonomous movement rep-resentations like splines, the learned pattern gener-ators remain autonomous dynamical systems whichrobustly cope with external perturbations that disruptthe time ow of the original pattern, and which canalso be modi ed on-line by additional perceptual vari-ables. A simple extension allows to cope with mul-tiple degrees-of-freedom (DOF) patterns, where allDOFs share the same fundamental frequency but,otherwise, can move in arbitrary phase and ampli-tude o sets to each other. We evaluate our meth-ods in learning from demonstration with an actual30 DOF humanoid robot. Figure-8 and drummingmovements are demonstrated by a human, recordedin joint angle space with an exoskeleton, and em-bedded in multi-dimensional rhythmic pattern gener-ators. The learned patterns can be used by the robotin various workspace locations and from arbitraryinitial conditions. Spatial and temporal invarianceof the pattern generators allow easy amplitude andspeed scaling without losing the qualitative signatureof a movement. This novel way of creating rhyth-mic patterns could tremendously facilitate rhythmicmovement generation, in particular in locomotion ofrobots and neural prosthetics in clinical applications.