Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of predictable pain using fentanyl pectin nasal spray in patients undergoing radiotherapy

Background Studies report the need for improved pain management in the radiation oncology setting. Many patients with well controlled background pain experience breakthrough pain in cancer (BTPc) that can interrupt their treatment schedule with a potentially negative impact on outcomes. BTPc can be unpredictable and predictable; both types of pain can be managed with fast-acting analgesics, but predictable pain lends itself to anticipatory management. Methods Five consecutive cases are described in which fentanyl pectin nasal spray (FPNS) was used to manage BTPc, with an emphasis on the anticipatory management of predictable pain in cancer patients receiving radiotherapy. Results Patients (four men, one woman), age range 32–84 years, were diagnosed with various cancers. All patients were receiving opioid treatment for chronic pain, and experienced predictable pain with radiotherapy which included pain associated with lying on a treatment table for a sustained time during an average of 29 radiotherapy treatments; pain associated with radiation simulation and radiotherapy; pain associated with odynophagia related to increasing mucositis during treatment, resulting in decreased nutritional intake; pain associated with the customized immobilization mask for head and neck cancer patients; and pain associated with defecation. Some patients also reported pain awakening them randomly at night (eg, sleep interruption). All patients attained lower pain intensity scores (2/10 to 3/10), reduced from approximately 7/10, when they were treated with FPNS 20 minutes before a predictable pain event. No patient experienced any pain-related interruptions to their course of radiotherapy. The average number of radiotherapy sessions was 29 per patient, excluding one short-course treatment for one patient. Conclusion FPNS offers a good solution to the management of BTPc because its fast onset of action makes it very suitable for the anticipatory treatment of predictable pain, which is likely to minimize interruptions to the radiotherapy schedule.
Center for Embedded Computer Systems University of California , Irvine Using Result Oriented Modeling for Fast yet Accurate TLMs

Communication modeling is a critical part during SoC design and exploration. In particular, it is needed for accurately predicting the timing behavior of the system. Fa st simulation capabilities are a key in this environment, for coping with the complex design choices during the specifi cat on process. Recently, Transaction Level Models have been proposed to speedup communication simulation at t he cost of accuracy. This paper proposes a new modeling style: Result Oriented Mo eling (ROM). Using ROM yields fast executing models, that still are 100% accurate. ROM utilizes t he fact, that internal state changes of the model are not observable by the caller. Hence, it omits the interna l st tes and optimistically predicts the end result . Retroactively, the outcome is checked and if necessary, cor rective measures are taken to maintain accuracy of the model. The paper reports how Result Oriented Modeling can be applie d to Transaction Level Modeling for a communication system. It shows its application to two differen t bus systems: the AMBA AHB and the CAN bus. The initial results shown in this paper are very promising. Both implemented ROMs exhibit a performance in the same order of magnitude as the transaction level model, yet t hey retain the accuracy of the bus functional model. This clearly indicates that the proposed Result Oriented Mo eling approach is a very effective modeling style for transaction level models.
Emotions Recognition Using EEG Signals: A Survey

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.
Stochastic Pronunciation Modeling for Out-of-Vocabulary Spoken Term Detection

Spoken term detection (STD) is the name given to the task of searching large amounts of audio for occurrences of spoken terms, which are typically single words or short phrases. One reason that STD is a hard task is that search terms tend to contain a disproportionate number of out-of-vocabulary (OOV) words. The most common approach to STD uses subword units. This, in conjunction with some method for predicting pronunciations of OOVs from their written form, enables the detection of OOV terms but performance is considerably worse than for in-vocabulary terms. This performance differential can be largely attributed to the special properties of OOVs. One such property is the high degree of uncertainty in the pronunciation of OOVs. We present a stochastic pronunciation model (SPM) which explicitly deals with this uncertainty. The key insight is to search for all possible pronunciations when detecting an OOV term, explicitly capturing the uncertainty in pronunciation. This requires a probabilistic model of pronunciation, able to estimate a distribution over all possible pronunciations. We use a joint-multigram model (JMM) for this and compare the JMM-based SPM with the conventional soft match approach. Experiments using speech from the meetings domain demonstrate that the SPM performs better than soft match in most operating regions, especially at low false alarm probabilities. Furthermore, SPM and soft match are found to be complementary: their combination provides further performance gains.
A load balancing model for grid environment

Workload and resource management are two essential functions provided at the service level of the Grid software. To improve the global throughput of these environments, effective and efficient load balancing algorithms are fundamentally important. Although load balancing problem in classical distributed systems has been intensively studied, new challenges in Grid computing still make it an interesting topic, and many research projects are under way. This is due to the Grid characteristics and to the complex nature of the problem. This paper presents a task load balancing model in Grid environment. First we propose a tree-based model to represent Grid architecture in order to manage workload. This model is characterized by three main features: (i) it is hierarchical; (ii) it supports heterogeneity and scalability; and, (iii) it is totally independent from any Grid physical architecture. Second, we develop a hierarchical load balancing strategy to balance tasks among Grid resources. The main characteristics of the proposed strategy are: (i) it uses a task-level load balancing; (ii) it privileges local tasks transfer to reduce communication cost; and, (iii) it is a distributed strategy with local decision making.
BACH: BYU Address Collection Hardware, The Collection of Complete Traces

Trace driven simulation is an important tool for computer systems performance analysis and prediction, but its accuracy decreases when incomplete or inaccurate traces are used for input. Nevertheless, many memory hierarchy simulation studies have been published which rely on such traces. In this paper we describe BACH, a hardware monitor developed to capture long, accurate, and complete traces on a variety of hardware and software platforms. BACH traces are long | traces containing over 200 million contiguous references have been collected to date. BACH traces are accurate | in contrast to other techniques such as inlining they contain almost no time and space dilation e ects. BACH traces are complete | they contain all references generated by the CPU during tracing, including prefetches and demand fetches from user code, system calls, exceptions, interrupts, and other system code. Finally, the traces produced using BACH are available to members of the general research community. In addition, we demonstrate the usefulness of the traces acquired using BACH through a cache simulation study. The miss rates obtained using BACH traces are shown to be as much as 50 times higher than those obtained using other traces. It is also shown that with a memory access time of 30, predictions of e ective access time can be o by as much as a factor of 2 when using inaccurate or incomplete trace data.
Grammar, uncertainty and sentence processing

Toward a probabilistic theory of human sentence processing, this dissertation proposes a definition of computational work done in the course of analyzing sentences generated by formal grammars. It applies the idea of entropy from information theory to the set of derivations compatible with an initial substring of a sentence. Given a probabilistic grammar, this permits the set of such compatible derivations to be viewed as a random variable, and the change in uncertainty about the outcomes to be calculated. This definition of computational work is examined as a cognitive model of human sentence processing difficulty. To apply the model, a variety of existing syntactic proposals for English sentences are cast as probabilistic Generalized Phrase Structure Grammars (Gazdar et al., 1985) and probabilistic Minimalist Grammars (Stabler, 1997). It is shown that the amount of predicted processing effort in relative clauses correlates with the Accessibility Hierarchy of relativized grammatical relations (Keenan and Comrie, 1977) on a Kaynian (1994) view of relative clause structure. Results from three new on-line sentence reading experiments suggest that while genitivity has the role suggested by the Accessibility Hierarchy, extraction from oblique does not. Evidence is also found for a direct object/indirect object processing asymmetry, which can be derived from the proposed cognitive model under the assumption of a lexicalized probabilistic grammar.
Compressive Parameter Estimation in AWGN

Compressed sensing is by now well-established as an effective tool for extracting sparsely distributed information, where sparsity is a discrete concept, referring to the number of dominant nonzero signal components in some basis for the signal space. In this paper, we establish a framework for estimation of continuous-valued parameters based on compressive measurements on a signal corrupted by additive white Gaussian noise (AWGN). While standard compressed sensing based on naive discretization has been shown to suffer from performance loss due to basis mismatch, we demonstrate that this is not an inherent property of compressive measurements. Our contributions are summarized as follows: (a) We identify the isometries required to preserve fundamental estimation-theoretic quantities such as the Ziv-Zakai bound (ZZB) and the Cramér-Rao bound (CRB). Under such isometries, compressive projections can be interpreted simply as a reduction in “effective SNR. ” (b) We show that the threshold behavior of the ZZB provides a criterion for determining the minimum number of measurements for “accurate” parameter estimation. (c) We provide detailed computations of the number of measurements needed for the isometries in (a) to hold for the problem of frequency estimation in a mixture of sinusoids. We show via simulations that the design criterion in (b) is accurate for estimating the frequency of a single sinusoid.
MEMS based Optical Image Stabilization

The thought of taking a perfect image gives way to a plethora of ideas which work towards improving the image quality using Image Stabilization. Currently, the methods used for Optical Image Stabilization(OIS) use Motion sensors and detect vibration and correct for any jitter using VCM (Voice coil motors). The concept of the proposed work is to simulate a MEMS gyroscope and monitor the system to detect change in rotation (or any vibrations). These vibrations in turn are used to trigger a voltage pulse which is conditioned and fed into structures called comb drives which are micro structures inturn actuate on application of optimal voltages. This comb drive arrangement is made to move a lens or a sensor (depending on which type of OIS is suitable for the application) and adjust the lens such that, even if the camera shakes, the image obtained is not blurred because the setup inside the camera which captures the image also shakes, but in the opposite direction.Hence, a clear image can be obtained in a novel method using MEMS gyroscope coupled with a MEMS comb-drive actuator set-up.
Mentor-shape: exploring the mentoring relationships of Black women in doctoral programs

Abstract Using a Black Feminist Thought framework, this phenomenological study focused on lived experiences with mentoring relationships among 38 Black women in doctoral programs in multiple disciplines across universities in the United States. Although emerging studies on Black women in academia acknowledge the significance of mentorship and the reality of discrimination at the intersection of race and gender, the literature lacks empirical support pertaining to the formation and outcomes of mentoring relationships, specifically for Black women in doctoral education. Findings from this study suggested four central themes in the mentoring relationships among Black women in doctoral programs: (a) organic evolvement, (b) shared identity/mutual interest, (c) personal transformation and (d) increase in access and opportunity. Implications for future research, policy, practice and education across disciplines are offered.
Recognizing Touch Gestures for Social Human-Robot Interaction

In this study, we performed touch gesture recognition on two sets of data provided by "Recognition of Social Touch Gestures Challenge 2015". For the first dataset, dubbed Corpus of Social Touch (CoST), touch is performed on a mannequin arm, whereas for the second dataset (Human-Animal Affective Robot Touch---HAART) touch is performed in a human-pet interaction setting. CoST includes 14 gestures and HAART includes 7 gestures. We used the pressure data, image features, Hurst exponent, Hjorth parameters and autoregressive model coefficients as features, and performed feature selection using sequential forward floating search. We obtained classification results around 60%--70% for the HAART dataset. For the CoST dataset, the results range from 26% to 95% depending on the selection of the training/test sets.
A Common Meta-Model for Data Analysis based on DSM

Realization and usage of advanced decision support systems are cost intensive. They require expert users during data collection and analysis task. Implementing these systems is time consuming and thus costly. This leads to the problem that SMEs (Small and Medium-sized Enterprises) often cannot afford these systems. In this paper we describe our aim of creating a DSM (Domain Specific Modeling) based top-down approach to generate advanced decision support systems. This approach is based on a family of DSLs (Domain Specific Language) that share a common meta-model. With this we aim to establish a faster and more affordable process for data analysis which better fits for SMEs.
Finding a needle in a haystack: pinpointing significant BGP routing changes in an IP network

The performance of a backbone network is vulnerable to interdomain routing changes that affect how traffic travels to destinations in other Autonomous Systems (ASes). Despite having poor visibility into these routing changes, operators often need to react quickly by tuning the network configuration to alleviate congestion or by notifying other ASes about serious reachability problems. Fortunately, operators can improve their visibility by monitoring the Border Gateway Protocol (BGP) decisions of the routers at the periphery of their AS. However, the volume of measurement data is very large and extracting the important information is challenging. In this paper, we present the design and evaluation of an online system that converts millions of BGP update messages a day into a few dozen actionable reports about significant routing disruptions. We apply our tool to two months of BGP and traffic data collected from a Tier-1 ISP backbone and discover several network problems previously unknown to the operators. Validation using other data sources confirms the accuracy of our algorithms and the tool's additional value in detecting routing disruptions.
Critical Layers in Shear Flows

The normal mode approach to investigating the stability of a parallel shear flow involves the superposition of a small wavelike perturbation on the basic flow. Its evolution in space and/or time is then determined. In the linear inviscid theory, if ū(y) is the basic velocity profile, then a singularity occurs at critical points yc, where ū = c, the perturbation phase speed. This is plausible intuitively because energy can be exchanged most efficiently where the wave and mean flow are travelling at the same speed. The problem is of the singular perturbation type; when viscosity or nonlinearity, for example, are restored to the governing equations, the singularity is removed. In this lecture, the classical viscous theory is first outlined before presenting a newer perturbation approach using a nonlinear critical layer (i.e., nonlinear terms are restored within a thin layer). The application to the case of a density stratified shear flow is discussed and, finally, the results are compared qualitatively with radar observations and also with recent numerical simulations of the full equations. ∗Address for correspondence: Department of Mathematics and Statistics, McGill University, Montreal, QC, H3A 2K6, Canada. e-mail: maslowe@math.mcgill. ca
Digit recognition from pressure sensor data using Euler number and central moments

The work described in the paper deals with realization of a digit recognition system for the visually impaired persons. The input to the system is pressure sensor data obtained from a sensor pad with 256 capacitive pressure sensors. The objective of the system is to recognize correctly the 2-D shape of a digit which is placed on the above sensor pad. The problem is extremely challenging because the input data i) is very susceptible to noise and ii) may undergo rigid body transformation with respect to the reference library of the system. During the first step of the recognition process, the Euler number is used to achieve a preliminary classification. In the second step, 4th order central moment is applied to correctly recognize the input. A comparison of the performance of the proposed method with some other existing approaches indicates our recognition results to be highly promising.
Attention modulates responses to motion reversals in human visual cortex

Selective attention modulates brain responses in visual cortex. A common finding, using functional magnetic resonance imaging or event-related potentials, is that responses to attended relative to unattended stimuli are potentiated. We report an exceptional circumstance in a motion-processing paradigm. Participants viewed superimposed stationary and moving dots and were instructed to attend to one or the other subset. Changes in the direction of dot motion triggered an event-related potential over posterior scalp sites, with a prominent negative peak at 200 ms that was larger when attention was directed at the stationary dots. This effect was localized to extrastriate visual cortex and may be due to reflexive effects of attention orienting triggered by unattended peripheral motion.
A new method for calibration of range sensor and terrain classification

Laser range scanner is an important modality in robotics for the perception of an environment such as object classification, map generation, navigation, etc. In this paper, we first present a new method for calibration of Laser range scanner to generate the highly accurate 3D model of the environment. Later, we propose a robust segmentation method for objects and ground points separation of fused range data which is obtained from a Laser range scanner. The Laser range scanner gives the two range dataset from different orientation of the same terrain. After fusion of these two range data using ICP algorithm, the complete range data of terrain are obtained. In order to classify objects and ground points of terrain, we exploit the properties of statistical measures and local elevation of the range data. The proposed algorithm is independent from range data format and resolution, i.e., it works for point cloud and gridded data. The experimental results presented in the paper have shown robustness of the proposed method for explicit segmentation of objects from the ground points.
Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. The proposed model was compared with the original ANN model without data preprocessing and a nonlinear perturbation model (NLPM) based on ANN, i.e., the NLPM-ANN model. Eight watersheds were selected for calibrating and testing these models. Comparative study shows that the learning and training ability of ANN models can be improved by SSA and NLPM techniques significantly, and the performance of the SSA-ANN model is much better than the NLPM-ANN model, with high foresting accuracy. The SSA-ANN1 model, which only considers rainfall as model input, was compared with the SSA-ANN2 model, which considers both rainfall and previous runoff as model inputs. It is shown that the Nash-Sutcliffe criterion of the SSA-ANN2 model is much higher than that of the SSA-ANN1 model, which means that the proper selection of previous runoff data as rainfall-runoff model inputs can significantly improve model performance since they usually are highly auto-correlated.
The far-infrared–radio relationship at high and low redshift

We use the results of the SCUBA Local Universe Galaxy Survey, a submillimetre (submm) survey of galaxies in the nearby Universe, to investigate the relationship between the far-infrared (FIR)–submm and radio emission of galaxies at both low and high redshift. At low redshift we show that the correlation between radio and FIR emission is much stronger than the correlation between radio and submm emission, which is evidence that massive stars are the source of both the FIR and radio emission. At high redshift we show that the submm sources detected by SCUBA are brighter sources of radio emission than are predicted from the properties of galaxies in the local Universe. We discuss possible reasons for the cosmic evolution of the relationship between radio and FIR emission.