Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Latency Audio Pitch Tracking: A Multi-Modal Sensor-Assisted Approach

This paper presents a multi-modal approach to musical instrument pitch tracking combining audio and position sensor data. Finger location on a violin fingerboard is measured using resistive sensors, allowing rapid detection of approximate pitch. The initial pitch estimate is then used to restrict the search space of an audio pitch tracking algorithm. Most audio pitch tracking algorithms face a fundamental tradeoff between accuracy and latency, with longer analysis windows producing better pitch estimates at the cost of noticeable lag in a live performance environment. Conversely, sensor-only strategies struggle to achieve the fine pitch accuracy a human listener would expect. While this paper is violin centric, it demonstrates a more general concept for augmented instruments that by combining the two differing approaches, high accuracy and low latency pitch can be simultaneiously achieved.
ICASE Report No . 94-74 S EFFICIENT BULK-LOADING OF GRIDFILES

This paper considers the problem of bulk-loading large data sets for for the gridfile multi-attribute indexing technique. We propose a rectilinear partitioning algorithm that heuristically seeks to minimize the size of the gridfile needed to ensure no bucket overflows. Empirical studies on both synthetic data sets and on data sets drawn from computational fluid dynamics applications demonstrate that our algorithm is very efficient, and is able to handle large data sets. In addition, we present an algorithm for bulk-loading data sets too large to fit in main memory. Utilizing a sort of the entire data set it creates a gridfile without incurring any overflows. *This research was supported by the National Aeronautics and Space Administration under NASA contract NAS1-19480 while the authors were in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA 23681-0001. Nicol's work was also supported in part by NSF grant CCR-9210372 and NASA grant NAG-l-ll32.
Centriole cycle in Chinese hamster ovary cells as determined by whole- mount electron microscopy

In interphase Chinese hamster ovary (CHO) cells, the centrosome is attached to the nucleus very firmly. This nuclear-centrosome complex is isolated as a coherent structure by lysis and extraction of cells with Triton X-100 in a low ionic strength medium. Under these conditions, the ultrastructure of the centrioles attached to the nucleus can be discerned by electron microscopy of whole-mount preparations. The structural changes of the centrioles as a function of the cell cycle were monitored by this technique. Specifically, centriolar profiles were placed into six categories according to their orientation and the length ratio of daughter and parent centrioles. The proportion of centrioles in each category was plotted as a frequency histogram. The morphological changes in the centriole cycle were characterized by three distinguishable events: nucleation, elongation, and disorientation. The progress of centrioles through these stages was determined in synchronous populations of cells starting from S or M phase, in cells inhibited in DNA synthesis by addition of thymidine, and in cytoplasts. The results provide a quantitative description of the events of the centriole cycle. They also show that, in complete cells, nucleation, elongation, and disorientation are not dependent upon DNA synthesis. However, in cytoplasts, although elongation and disorientation occur as in normal cells, nucleation is blocked. Procentriole formation appeared to be inhibited by the removal of the nucleus. We suggest that coordination of centriole replication and nuclear replication may depend upon a signal arising from the nucleus.
Adherence to Immunoprophylaxis Regimens for Respiratory Syncytial Virus Infection in Insured and Medicaid Populations.

BACKGROUND Immunoprophylaxis is the only pharmaceutical intervention for mitigating respiratory syncytial virus (RSV) infection. Patient level data on adherence to American Academy of Pediatrics (AAP) immunoprophylaxis recommendations are limited. This study characterizes adherence to AAP guidelines in privately insured and Medicaid populations. METHODS We performed a retrospective birth cohort study of 211 174 privately insured children in Northern California; and 458 837 publicly insured children in Tennessee born between January 1, 1996 and December 31, 2008. Adherence to the AAP guideline was defined for eligible infants as the number of doses of RSV immunoprophylaxis administered over the number recommended for 4 mutually exclusive eligibility groups: chronic lung disease, prematurity <29 weeks, prematurity <32 weeks, and other eligibility. RESULTS We identified 3456 California (Kaiser Permanente Northern California [KPNC]) and 12 251 Tennessee (Tennessee Medicaid [TennCare]) infants meeting AAP eligibility criteria. Immunoprophylaxis administration increased over the study period, from 15% for all eligible groups in 1998 to 54% in 2007. Adherence was highest among babies with chronic lung disease (KPNC 67% and TennCare 55%). Nonadherence (0% adherence) was greatest among infants of African American mothers (adjusted odds ratio [AOR] = 1.32; 95% confidence interval [CI] = .98-1.78); those with mothers with less than a high school education (AOR = 1.58; CI = 1.09-2.30) in KPNC; and in infants of Hispanic mothers in TennCare (AOR = 1.65; CI = 1.24-2.20). In KPNC, 0.11% of ineligible term infants and 5% of ineligible premature infants received immunoprophylaxis; the corresponding proportions in TennCare were 1% and 11%. CONCLUSIONS Overall adherence with AAP guidelines has increased over time. Considerable overuse and underuse of immunoprophylaxis are evident with identifiable risk groups to target for improvement.
Unsupervised Skeleton Extraction and Motion Capture from Kinect Video via 3 D Deformable Matching

This paper presents a novel method to extract skeletons of complex articulated objects from 3D point cloud sequences collected by the Kinect. Our approach is more robust than the traditional video-based and stereobased approaches, as the Kinect directly provides 3D information without any markers, 2D-to-3D-transition assumptions, and feature point extraction. We track all the raw 3D points on the object, and utilize the point trajectories to determine the object skeleton. The point tracking is achieved by the 3D non-rigid matching based on the Markov Random Field (MRF) Deformation Model. To reduce the large computational cost of the non-rigid matching, a coarse-to-fine procedure is proposed. To the best of our knowledge, this is the first to extract skeletons of highly deformable objects from 3D point cloud sequences by point tracking. Experiments prove our method’s good performance, and the extracted skeletons are successfully applied to the motion capture.
Robust state estimation with q-integral observers

A notion of q-integral (qI) observers for multiple-input single output linear systems is introduced. Then the theory is extended to nonlinear systems with single output. We show that the q-integral observer guarantees robustness against both measurement errors and unmodeled dynamics. An example is given to show the efficacy of the proposed robust observers.
Conceptual and Technical Challenges in Network Meta-analysis

The increase in treatment options creates an urgent need for comparative effectiveness research. Randomized, controlled trials comparing several treatments are usually not feasible, so other methodological approaches are needed. Meta-analyses provide summary estimates of treatment effects by combining data from many studies. However, an important drawback is that standard meta-analyses can compare only 2 interventions at a time. A new meta-analytic technique, called network meta-analysis (or multiple treatments meta-analysis or mixed-treatment comparison), allows assessment of the relative effectiveness of several interventions, synthesizing evidence across a network of randomized trials. Despite the growing prevalence and influence of network meta-analysis in many fields of medicine, several issues need to be addressed when constructing one to avoid conclusions that are inaccurate, invalid, or not clearly justified. This article explores the scope and limitations of network meta-analysis and offers advice on dealing with heterogeneity, inconsistency, and potential sources of bias in the available evidence to increase awareness among physicians about some of the challenges in interpretation.
High-speed and reliable object recognition based on low-dimensional local shape features

In this paper, we propose a high-speed 3-D object recognition method using new feature values. Features for the object recognition method proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. We use these three-dimensional features to recognize the position and pose of multiple objects stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. We have also reduced the computing time needed for data matching by using only effective points selected on the basis of their estimated distinctiveness. Experimental results using actual scenes have demonstrated that the computing time is about 93 times faster than that of the conventional SHOT method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 17.9% higher than that of the SHOT method. Also, we confirmed that the proposed method achieves higher-speed matching and higher recognition success rate than the conventional methods.
Design Requirements for Ubiquitous Computing Environments for Healthcare Professionals

Ubiquitous computing environments can support clinical administrative routines in new ways. The aim of such computing approaches is to enhance routine physical work, thus it is important to identify specific design requirements. We studied healthcare professionals in an emergency room and developed the computer-augmented environment NOSTOS to support teamwork in that setting. NOSTOS uses digital pens and paper-based media as the primary input interface for data capture and as a means of controlling the system. NOSTOS also includes a digital desk, walk-up displays, and sensor technology that allow the system to track documents and activities in the workplace. We propose a set of requirements and discuss the value of tangible user interfaces for healthcare personnel. Our results suggest that the key requirements are flexibility in terms of system usage and seamless integration between digital and physical components. We also discuss how ubiquitous computing approaches like NOSTOS can be beneficial in the medical workplace.
Feature Selection on High Throughput SELDI-TOF Mass-Spectrometry Data for Identifying Biomarker Candidates in Ovarian and Prostate Cancer

High-throughput mass-spectrometry screening has the potential of superior results in detecting early stage cancer than traditional biomarkers. Proteomic data poses novel challenges for data mining, especially concerning the curse of dimensionality. In this paper, we present a 3-step feature selection framework combining the advantages of efficient filter and effective wrapper techniques. We demonstrate the performance of our framework on two SELDI-TOF-MS data sets for identifying biomarker candidates in ovarian and prostate cancer
Nasal contribution to breathing with exercise: effect of race and gender.

Because the nose acts as a filter to prevent penetration of toxic particles and gases to the lower respiratory tract, the route of breathing, oral vs. nasal, may be an important determinant of toxicant dose to the lungs. Using respiratory inductance plethysmography and a nasal mask fitted with flowmeter, we measured the nasal contribution to breathing at rest and during exercise (to 60% maximum workload) in healthy young adults (men/women = 11/11 and Caucasian/African-American = 11/11). We found that the nasal contribution to breathing is less during submaximal exercise in the Caucasians vs. African-Americans (e.g., at 60% maximum workload, mean nasal-to-total ventilation ratio = 0.40 +/- 0.21 and 0.65 +/- 0.24, respectively, P < 0.05). This difference is likely due to the African-Americans' ability to achieve higher maximal inspiratory flows through their nose than the Caucasians. Men also had a lesser nasal contribution to breathing during exercise compared with women. This is likely due to greater minute ventilations at any given percentage of maximum workload in men vs. women.
Using a Kernel-Based Approach to Visualize Integrated Chronic Fatigue Syndrome Datasets

We describe the use of a kernel–based approach using the Laplacian matrix to visualize an integrated Chronic Fatigue Syndrome dataset comprising symptom and fatigue questionnaire and patient classification data, complete blood evaluation data and patient gene expression profiles. We present visualizations of the individual and integrated datasets with the linear and Gaussian kernel functions. An efficient approach inspired by computational linguistics for constructing a linear kernel matrix for the gene expression data is described. Visualizations of the questionnaire data show a cluster of non–fatigued individuals distinct from those suffering from Chronic Fatigue Syndrome that supports the fact that diagnosis is generally made using this kind of data. Clusters unrelated to patient classes were found in the gene expression data. Structure from the gene expression dataset dominated visualizations of integrated datasets that included gene expression data.
GPU-Based Parallel Nonlinear Conjugate Gradient Algorithms

In this paper we describe some parallel algorithms for solving nonlinear systems using CUDA (Compute Unified Device Architecture) over a GPU (Graphics Processing Unit). The proposed algorithms are based on both the Fletcher-Reeves version of the nonlinear conjugate gradient method and a polynomial preconditioner type based on block two-stage methods. Several strategies of parallelization and different storage formats for sparse matrices are discussed. The reported numerical experiments analyze the behavior of these algorithms working in a fine grain parallel environment compared with a thread-based environment.
Continuous Queries in Oracle

This paper describes Continuous Queries (CQ) in Oracle RDBMS, a feature that incorporates stream and complex event processing into an RDBMS, the first such attempt in commercial databases. The feature is based on the concept of query difference and allows us to monitor real time changes to the query as the result of changes to its underlying tables. The result of a continuous query can be deposited into historical tables or queues for further asynchronous de-queuing, or can invoke a synchronous trigger for procedural processing. The main contribution of our CQ engine is that it allows us to react to complex scenarios of changes to data such as mixed INSERT, DELETE and UPDATE changes, unlike the existing stream processing systems that deal with INSERTS only. We support a wide range of query shapes including inner, semi and anti-joins, aggregates and window functions. More details are given to the efficient computation of query difference for general cases and their optimizations based on semantic constraints. They are shown to improve the response time for practical cases by more than an order of magnitude. We also show how delaying CQ re-computation can improve its performance by batch processing the changes to the base tables.
A critical essay on modern macroeconomic theory

In the early 1980s, rational expectations and new classical economics dominated macroeconomic theory. This essay evolved from the authors' profound disagreement with that trend. It demonstrates not only how the new classical view got macroeconomics wrong, but also how to go about doing macroeconomics the right way. Hahn and Solow argue that what was originally offered as a normative model based on perfect foresight and universal perfect competition has been almost casually transformed into a model for interpreting real macroeconomic behavior. After explaining microeconomic foundations, the authors introduce a better macro model, one that can say useful things about the fluctuation of employment, the correlation between wages and employment, and the role for corrective monetary policy.
Emotional event detection using relevance feedback

Affective content analysis is necessary to represent a user's preferences in various applications such as video data retrieval and video abstraction. In this paper, we propose a new method to detect emotional events such as fear, sadness, and joy from video data using relevance feedback scheme. We ask the user to provide feedbacks regarding the relevance with emotions for video shot. Then, the system is learned from training data to achieve an improved performance in detecting emotional events. Even though simple low level features are used, experimental results are encouraging.
G-consistent price system and bid-ask pricing for European contingent claims under Knightian uncertainty

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the European contingent claims under Knightian uncertain financial market. Firstly, we consider the basic risky asset price model on the uncertain financial market, which we construct here is the model with drift uncertain and volatility uncertain. We describe such model by using generalized G-Brownian motion and call it as G-asset price system. We present the uncertain risk premium which is uncertain and distributed with maximum distribution. We derive the closed form of bid-ask price of the European contingent claim against the underlying risky asset with G-asset price system as the discounted conditional G-expecation of the claim, and the bid and ask prices are the viscosity solutions to the nonlinear HJB equations.Furthermore, we consider the main part of this paper, i.e., consider the risky asset on the Knightian uncertain financial market with the price fluctuation shows as continuous trajectories. We propose the G-conditional full support condition by using uncertain capacity, and the risky asset price path satisfying the G-conditional full support condition could be approximated by its G-consistent asset price systems. We derive that the bid and ask prices of the European contingent claim against such risky asset under uncertain can be expressed by discounted of some conditional G-expectation of the claim. We give examples, such as G-Markovian processes and the geometric fractional G-Brownian motion [9], satisfying the G-conditional full support condition.
Relating Demand Behavior and Production Policies in the Manufacturing Supply Chain

Production decisions in a manufacturing supply chain are no longer driven by manual systems based on instinct and experience. They are regulated interactions between analysts, production managers and their collective manipulation of policies within the production information system. This paper studies the demand behavior in manufacturing supply chains and its relationships to the logic of production information systems pertaining to order batching, multiple schedule releases, bill of material processing, and resource leveling. The analysis helps us to understand the possible causes of demand amplification, the dependencies and mutual sensitivities of production decisions over multiple supply tiers, and the possible effects of production lot sizes, product design, and capacity levels. Main results include that multiple schedule releases may be used to stabilize supply chain operations, production batching may amplify demand variations across supply tiers, and that operating close to capacity tends to dampen demand variation downstream. Subject Categorizations: Production/Scheduling, planning: manufacturing supply chain operations Manufacturing, performance/productivity: demand behavior analysis
A Rule-Based Approach to Address Semantic Accuracy Problems on Linked Data

In 2001, Berners-Lee et al. [2] defined the Semantic Web (SW) as an extension of the current Web in which information is given well-defined meaning through the use of common standards and technologies to facilitate the sharing and reuse of data. In 2006, the related term Linked Data (LD) [3, 7] was proposed as a way to identify a set of best practices for publishing data using SW tools that allow to link these isolated datasets in a large network of distributed data [17]. Since then, the number of available datasets that follow the SW and LD ideas has been considerably increasing, leading to what is currently known as the Web of Data (WoD).
Diachronic Stylistic Changes in British and American Varieties of 20th Century Written English Language

In this paper we present the results of a study investigating the diachronic changes of four stylistic features: average sentence length, Automated Readability Index, lexical density and lexical richness in 20th century written English language. All experiments were conducted on the largest existing diachronic corpora of British and American English ‐ the Brown ‘family’ corpora, employing NLP techniques for automatic extraction of the features. Additionally, we compare the trends of changes between the two English varieties and make suggestions for future studies of diachronic language change.
An Experimental Methodology for an End-to-End Evaluation in Speech-to-Speech Translation

This paper describes the evaluation methodology used to evaluate the TC-STAR speech-to-speech translation (SST) system and the results from the third year of the project. It follows the results presented in Hamon (2007), dealing with the first end-to-end evaluation of the project. In this paper, we try to experiment with the methodology and the protocol during a second end-to-end evaluation, by comparing outputs from the TC-STAR system with interpreters from the European parliament. For this purpose, we test different criteria of evaluation and type of questions within a comprehension test. The results show that interpreters do not translate all the information (as opposed to the automatic system), but the quality of SST is still far from that of human translation. The experimental comprehension test used provides new information to study the quality of automatic systems, but without settling the issue of which protocol is the best. This depends on what the evaluator wants to know about the SST: either to have a subjective end-user evaluation or a more objective one.
A Computational Model of Perceptual Deficits in Medial Temporal Lobe Amnesia

Damage to the Medial Temporal Lobe (MTL) impairs declarative memory and perception. The Representational-Hierarchical (RH) Account explains such impairments by assuming that MTL stores conjunctive representations of items and events, and that individuals with MTL damage must rely upon representations of simple visual features in posterior visual cortex. A recent study revealed a surprising anti-perceptual learning effect in MTL-damaged individuals: with exposure to a set of visual stimuli, discrimination performance worsened rather than improved. We expand the RH account to explain this paradox by assuming that visual discrimination is performed using a familiarity heuristic. Exposure to a set of highly similar stimuli entails repeated presentation of simple visual features, eventually rendering all feature representations equally (maximally) familiar and hence inutile for solving the task. Since the unique conjunctions represented in MTL do not occur repeatedly, healthy individuals are shielded from this perceptual interference. We simulate this mechanism with a neural network previously used to simulate recognition memory, thereby providing a model that accounts for both mnemonic and perceptual deficits caused by MTL damage using a unified architecture and mechanism.