Miracle-dz/UniFAR-FTUs huggingface dataset:
Control and Stabilization of the Korteweg-de Vries Equation on a Periodic Domain

In [38], Russell and Zhang showed that the Korteweg-de Vries equation posed on a periodic domain 𝕋 with an internal control is locally exactly controllable and locally exponentially stabilizable when the control acts on an arbitrary nonempty subdomain of 𝕋. In this paper, we show that the system is in fact globally exactly controllable and globally exponentially stabilizable. The global exponential stabilizability is established with the aid of certain properties of propagation of compactness and regularity in Bourgain spaces for the solutions of the associated linear system. With Slemrod's feedback law, the resulting closed-loop system is shown to be locally exponentially stable with an arbitrarily large decay rate. A time-varying feedback law is further designed to ensure a global exponential stability with an arbitrarily large decay rate.
jsCoq: Towards Hybrid Theorem Proving Interfaces

We describe jsCcoq, a new platform and user environment for the Coq interactive proof assistant. The jsCoq system targets the HTML5-ECMAScript 2015 specification, and it is typically run inside a standards-compliant browser, without the need of external servers or services. Targeting educational use, jsCoq allows the user to start interaction with proof scripts right away, thanks to its self-contained nature. Indeed, a full Coq environment is packed along the proof scripts, easing distribution and installation. Starting to use jsCoq is as easy as clicking on a link. The current release ships more than 10 popular Coq libraries, and supports popular books such as Software Foundations or Certified Programming with Dependent Types. The new target platform has opened up new interaction and display possibilities. It has also fostered the development of some new Coq-related technology. In particular, we have implemented a new serialization-based protocol for interaction with the proof assistant, as well as a new package format for library distribution.
Scene text detection with superpixels and hierarchical model

Scene text detection is a challenging task for the text-based information extraction systems. We present a novel scene text detection method for this task. The images are over-segmented into meaningful perceptron superpixels, and candidate connected components (CCs)are extracted by combining local contrast and color consistency. The non-text components are then pruned by a hierarchical model consisting of three stages in cascade. Experimental results show that our approach is better than other state-of-the-art methods.
Analysis and Design of Cyclic Switched-Capacitor DC–DC Converters

We introduce the cyclic switched-capacitor dc–dc converter for single-input multiple-output power delivery. We focus on applications in stacked voltage domain (SVD)-based systems, and show that the cyclic architecture is robust to load current mismatches. We analyze such converters and give intuition for their power losses. We give experimental results from an SVD based power management integrated circuit (PMIC), designed and fabricated in a 130nm CMOS technology. The PMIC is a combination of a 2/3 stage reconfigurable cyclic SC dc–dc converter and a linear regulator. It can drive $3\times 3$ mA loads with the input supply voltage varying from 2.35–3.65V.
A new miniaturised multi-axis force/torque sensors based on optoelectronic technology and simply-supported beam

This paper presents a methodology for the development of a multi-axis force/torque sensor based on optoelectronic technology. The advantages of using this sensing principle are the low manufacturing costs, the simple fabrication, and the immunity to electrical noise. The force/ torque sensor makes use of six optical sensors: each sensor measures the displacement of a reflective surface that moves integrally with a simply-supported beam. The proposed mechanical structure allows for a variety of shapes on the mechanical structure to be easily adaptable to many robot applications. In this paper, we present a five-axis force/torque sensor based on this optoelectronic principle. To measure force/torque components, two identical three-DoF force/torque sensor structures (comprised of three beams) are mounted on top of each other. Photo sensors and mirrors are fixed inside the structure to measure the six beam deflections. In this paper, we describe the sensor structure, design, fabrication, calibration, and verify our sensor development methodology.
Fast-handoff support in IEEE 802.11 wireless networks

With the advance of wireless local area network (WLAN) technology, handoff support has become one of the most important issues in IEEE 802.11 WLANs. However, the current IEEE 802.11 specification does not provide the fast handoff required for real-time multimedia applications. To support fast handoff in IEEE 802.11 networks, a number of fast-handoff schemes have been proposed in the literature. In this article we review these fast-handoff schemes and analyze their advantages and disadvantages qualitatively. After that, important design considerations for mobility support in future IEEE 802.11 networks are suggested. Also, we introduce a fast-handoff framework which adaptively meets different application requirements via a cross-layer approach.
Heart murmur detection and configuration estimation using wavelet transform and linear fit

This paper describes a signal processing procedure that identifies the first and second heart sounds (Si and S2) from repeated cardiac cycles; separates the systole from the diastole; isolates suspected murmurs found within; and delineates murmur configuration in regards to its shape. The identification of heart sounds was facilitated by performing the wavelet transform (WT) based decomposition and approximation using Daubechies (db6) wavelet, and Si and S2 were correctly identified without referring to the ECG waveform. We extended our previous studies on heart murmur features extraction and emphasized in this study on delineating heart murmur configuration either as crescendo, decrescendo, crescendo-decrescendo, or plateau. The said task was accomplished by first segmenting and computing the energy index measure and the averaged absolute amplitude of the suspected murmur, and then estimating the slope(s) of the averaged murmur signal energy and the averaged absolute amplitude through a linear fit. The procedure outlined was evaluated using both emulated systolic murmurs and clinically recorded heart murmurs. The evaluation has shown satisfactory and promising performance for future expansion.
Medicare hospital outpatient services and costs: Implications for prospective payment

Medicare expenditures of hospital outpatient department (HOPD) services are growing rapidly, prompting congressional interest in a prospective payment system. In this article, the authors identify frequently provided services and examine service volume and charges in the HOPD. Relatively few services drive Medicare HOPD spending, and volume is dominated by visits, imaging and laboratory tests, whereas surgery accounts for a large proportion of charges. Hospital-level variations in charges, costs, case mix, and outliers are also explored. There is substantial variation in charges and costs across hospital types. However, after case-mix adjustment, all hospital types have average costs within 6 percent of the national average.
Using a Prediction Model to Manage Cyber Security Threats

Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.
Towards a neural model of bonding in self-attachment

We build on a previous neural model of bonding circuitry, in which the orbitofrontal cortex mediates between facilitative and stress reactivity to social stimuli, via the dorsomedial and paraventricular nucleus of the hypothalamus. We integrate recent neuroscientific findings, and consider how the introduction of additional reward could drive a further, counter-conditioning based re-balancing mechanism between activation of these networks, via increasing prefrontal-driven inhibition of the central nucleus of the amygdala. We simulate our model computationally, and hypothesise that such a process may be involved in a particular phase of self-bonding in the newly introduced self-attachment psychotherapy.
The impact of sport, urbanicity, gender, and demographics on high school coaches' perceptions of no pass, no play in Educational Service Center, Region 20, Texas

The Impact of Sport, Urbanicity, Gender, and Demographics on High School Coaches’ Perceptions of No Pass, No Play in Educational Service Center, Region 20, Texas. (August 2007) Jennifer Johnson Kennedy, B.S. Texas A&M University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Mario S. Torres The major purpose of this study was to determine how no pass, no play has impacted the perceptions of academic player eligibility as perceived by high school coaches in Educational Service Center, Region 20, Texas. Variables such as coach characteristics, school characteristics, and community characteristics were researched. In addition, the study examined the influence gender and ethnicity of the coach had on their perceptions of no pass, no play. The study focused on the perceptions of coaches to no pass, no play relating to (1) student motivation, (2) instructional issues, (3) ethnicity specific variables, (4) student suspension variables. The relationship between poverty status in the district, annual household income, the type of sport, and demographic variables such as the gender, experience level, and ethnicity of the coach were also examined. Respondents’ answers were dependent upon a number of variables. The gender of the coach was a variable that reappeared as significant throughout the study. The
Link Status Monitoring Using Network Coding

In network tomography, we seek to infer link status parameters (such as delay) inside a network through end-to-end measurements at (external) boundary nodes. As can be expected, such approaches generically suffer from identifiability problems; i.e., even with the maximum amount of data obtainable from such end-to-end probes, status of links in a large number of network topologies is not identifiable. A basic result characterizing network topologies which are not identifiable under end-to-end probe sending is first derived. Then, we introduce an innovative approach based on linear network coding that overcomes this problem. We provide sufficient conditions on network coding coefficients and training sequence under which any logical network is guaranteed to be identifiable. In addition, we show that it is possible to locate any congested link inside a network during an arbitrary amount of time by increasing size of transmitted packets, leading to raise in complexity of the method. Further, given an appropriate choice of training sequence, a probability of success is provided for a random network. OPNET is used to implement the concept and confirm the validity of the claims simulation results show that the network coding method correctly detects the congested link, while probing based algorithms fail.
Probabilistic Resurvey Mission Planning

Problems in which autonomous agents perform simultaneous localisation and mapping (SLAM) are well explored in the literature. A related problem is rediscovery, in which the agent attempts to refine estimates of feature locations derived from previous SLAM surveys. However, most works in rediscovery assume high density of features. The major contribution of this work is a formulation of the sparse rediscovery problem. The applicability of current techniques in planning under uncertainty are subsequently considered with respect to the identified characteristics, specifically the high dimensionality of the problem in general and the low density of features in the surveys in the sparse problem. A solution to the sparse rediscovery problem is proposed and empirical results are presented and discussed.
Virtual Utilization and VoIP Capacity of WLANs Supporting a Mix of Data Rates

Today’s WLANs offer high data rates of up to 11 and 54 Mbps for IEEE 802.11b respective 802.11a/g making this technology very attractive for real-time applications like VoIP calls. However, only a certain part of communication can take place at high data rates. Additionally, a certain amount of overhead is introduced by the CSMA/CA-based medium access scheme. Thus, challenging questions remain how the load situation of a WLAN cell can be evaluated adequately and which costs arise if some users transmit only at low rates. In this work, we motivate a ”virtual” utilization metric for the evaluation of the current situation in a WLAN cell and investigate simulatively the influence of low-rate transmissions in a WLAN network used for pure VoIP traffic . The virtual utilization is based on the physical and virtual occupancy duration for each transmission. Since the occupancy duration is measured at the AP, which is the bottleneck of a WLAN cell, we are able to identify the resources that every user consumes for his transmissions—including the effects of transmission rates as well as technology dependent overheads. For the VoIP simulation studies, our metric is the number of calls that is servable at acceptable quality for every single user. ”Acceptable” thereby means that certain VoIP QoS constraints are not violated. For each single data rate, the VoIP capacity, i.e., the maximum number of calls, is determined at first. Afterwards, the influence of a single low-rate user is investigated and set in relation to the capacity results, which leads to the identification of costs invoked by the low-rate transmissions.
Confidence metrics for speaker identification

This paper presents a concept and experimental results for a novel confidence metric for speaker identification (SID). The concept is based on three major audio components: duration, signal-to-noise ratio, and model quality. This concept has been successfully applied to two benchmark closed-set SID systems which demonstrates the broad applicability of the technique. A subset of the TIMIT database was used for this investigation. Derivation and validation of the confidence performance are reported in this paper. We also investigate a fusion technique for the composite multi-component confidence metric. Reliability of the confidence metric has been determined to be 94.6% accurate on average.
A broadcast-based test scheme for reducing test size and application time

We present efficient method for reducing test application time by broadcasting test configuration. We compare our method based on single, multiple, 1-1 in-order mapping, even distribution, nearest signal probability matching, and in-order pseudo-exhaustive method. The results of our experiments indicate that our method reducing the test pattern number and the test application time by running the ATPG tool provided by SIS
Socio-Cultural Aspects of Language Acquisition Through Conversation

The paper focuses on the role of the conversation between parents/caregivers and monolingual children from Russia and bilingual Roma children from Bulgaria. Although the two cultures differ, the parents and adults who communicate with children use the same strategies in order to help them to learn the language from a very early age. In Roma culture, more oral folklore is used, and in Russian culture more children’s books and art; these are employed as vessels to introduce the language. In both cultures the conversation and the use of complex sentences is an important factor in language acquisition. The process of language learning is considered in the light of Vygotsky’s cultural theory. The monolingual and bilingual children learn their mother tongue through the communication with parents, community members, siblings, extended family members and teachers. The mothers and fathers, the teachers in kindergartens and preschools, create situations for language learning according to their culture and traditions. The conversation with the young language learners, the stimuli provided by adults, asking complex questions, discussing fairy tales or poems by a famous author are all strategies for communication. Being an oral culture, Roma culture has its values and they are used in the process of teaching the language to the children. Roma children generally have no books at home in Romani. Russian language and culture, as a written language, has different values and rules. However, in both cultures conversation plays an important role in the learning of the grammar of the mother tongue from an early age.
Periodicity-Based Computation of Optical Flow

The standard Brightness Constancy Equation states spatiotemporal shift invariance of the input data along a local velocity of optical flow. In its turn, the shift invariance leads to a periodic function of a real argument. This allows application of a known test for periodicity to computation of optical flow at random locations. The approach is valid also for higher dimensions: for example, it applies to a sequence of 3D tomography images. The proposed method has a reasonably high accuracy for continuous flow and is noise tolerant. Special attention is paid to weak signal input. It is shown that a drastic reduction in the signal strength worsens the accuracy of estimates insignificantly. For a possible application to tomography, this would lead to an unprecedented diminution of harmful radiation exposure.
Extraction from Live Captured Image with Diversified Background using Edge Based & K-Means Clustering

The proposed system highlights a novel approach of extracting a text from image using K-Means Clustering. Text Extraction from image is concerned with extracting the relevant text data from a collection of images. Recent studies in the field of image processing show a great amount of interest in content retrieval from images and videos. This content can be in the form of objects, color, texture, shape as well as the relationships between them. As the commercial usage of digital contents are on rise, the requirement of an efficient and error free indexing text along with text localization and extraction is of high importance. The proposed system has broader scale of consideration of input image with much complicated backgrounds along with consideration of sliding windows. For much accuracy, morphological operation is included to accurately distinguish the text and non-text area for better text localization and extraction. The experimental result was compared with all the prior significant work in text extraction where the results show a much robust, efficient, and much accurate text extraction technique. Keyword: Text Extraction, Discrete Wavelet Transform, K-Means Clustering, Morphological Operations
State-dependent mixture tying with variable codebook size for accented speech recognition

In this paper, we propose a state-dependent tied mixture (SDTM) models with variable codebook size to improve the model robustness for accented phonetic variations while maintaining model discriminative ability. State tying and mixture tying are combined to generate SDTM models. Compared to a pure mixture tying system, the SDTM model uses state tying to reserve the state identity; compared to the sole state tying system, such model uses a small set of parameters to discard the overlapping mixture distributions for robust model estimation. The codebook size of SDTM model is varied according to the confusion probability of states. The more confusable a state is, the larger its codebook size gets for a higher degree of model resolution. The codebook size is governed by state level variation probability of accented phonetic confusions which can be automatically extracted by frame-to-state alignment based on the local model mismatch. The effectiveness of this approach is evaluated on Mandarin accented speech. Our method yields a significant 2.1%, 9.5% and 3.5% absolute word error rate reduction compared with state tying, mixture tying and state-based phonetic tied mixtures, respectively.
An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge

Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE) methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure.
Connectionist Models of Reading: Incorporating Semantics

There now exist direct route connectionist models of reading aloud (i.e. text to phoneme conversion) that learn to perform on their training data and unseen words with accuracy comparable to that of humans. They also exhibit a number of developmental, reaction time and brain damage effects that are observed experimentally. However, various deficiencies (such as their failure to perform accurate lexical decision and to show the pseudohomophone effect) indicate the need to incorporate some form of lexical/semantic system into these models. In this paper we present a preliminary investigation of this problem. A general framework is outlined that models network activation flow between orthography, phonology and semantics. Explicit small scale connectionist simulations show how such an approach can account for many aspects of reading (and related tasks) that are not possible without the influence of the semantic system. Further possible refinements of this approach are discussed.
"Analytic" approximation of Doppler ultrasound blood flow signals

Spectral resolution may be enhanced by complex signal representation ("analytic" signal) of the signals processed. This paper addresses the impact on statistical and computational performance of the autoregressive modified covariance spectral estimator on a clinical scenario, while using, or not, discrete "analytic" signals. The statistical performance results prove that an "analytic" signal representation should be obtained prior to the estimation process. A novel algorithm to compute the complex modified covariance matrix is used to achieve increased computational performance. This approach reduces the estimation error and the execution time by 1% and 30% respectively