Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-grained dengue forecasting using telephone triage services

Calling patterns on a health hotline can accurately forecast dengue cases, 2 to 3 weeks ahead of time, at a subcity level. Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. We present results from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a subcity level (correlation of up to 0.93). Our system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from our system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. Our work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline.
Sample volume misregistration in linear array-based dual beam Doppler ultrasound systems

Large velocity estimation errors can occur in dual beam Doppler ultrasound velocity measurement systems when there is left/right sample volume misregistration, particularly when the interbeam angle is small. Such misregistration will occur when there is tissue inhomogeneity. This is investigated for a typical type of inhomogeneity-a layer of fat-by calculating the amount of both angle and translation misregistration occurring in such a system realized using a single linear array transducer. The complex sample volume sensitivity is calculated using a modified time domain approach, combining the spatial impulse response method with ray tracing. The effects on these misregistrations of altering the aperture sizes and their relative positions on the array is then investigated to derive an improved aperture configuration for dual beam velocity estimation. Arrangements with transmit apertures wider than the receive apertures are shown to be preferable in this context.
Improving the Spatial Ability of Distance Learning Students on the Material of Three-Dimensional Shapes through Mobile Learning Applications Based on Augmented Reality

This research develops a mobile learning application that produces a feasible and attractive product to be used as a learning resource. Then, the product was tested quantitatively on users to determine the product's readiness and its relationship to increasing the user's spatial abilities. This research is a continuing research that has been carried out previously regarding the development of ABRAR (Application of three-dimensional shapes with Augmented Reality) application (Anam et al., 2022). The product has been validated by content and media experts and has received positive feedback from student users. Data was collected in Nusa Tenggara: Regional Offices of UT Mataram and UT Kupang, involving 3.098 Elementary Education Study Program students. The sample size consisted of 310 respondents, determined using the Lemeshow formula. The product produced from this research is an augmented reality-based mobile learning application that serves as a learning resource. The results of the mobile learning media trial developed were interpreted as very positive. It shows that using mobile learning in geometry subjects provides a positive response and increases motivation in the learning process. In addition, the implementation of mobile learning in spatial ability has shown a 30% improvement in imagining the position of three-dimensional shapes. It indicates that mobile learning has influenced the respondents' spatial ability. Respondents can visualize three-dimensional shapes from a certain point of view by utilizing augmented reality (AR) technology. Furthermore, based on the feasibility test results, the resulting mobile learning meets the criteria of being excellent and feasible to use as a geometry learning medium. The result of the research is mobile learning application so that it can be applied for distance education with requirements has an Android application system and easy to connect the internet. Thus, the ABRAR application can support learning anytime and anywhere, improve understanding of the material, and improve students' spatial abilities.
The Range of Zero‐Associated Phoria in an Asymptomatic Clinical Population

Abstract The clinical concept of the range of zero‐associated phoria is developed, and normative data for this range are presented based on a clinical study of 201 asymptomatic patients. The relation of patient age, sex, and amount of near lens addition to this range is examined. The range at far is not related to patient age or sex. The range at near appears to be independent of patient age, sex, and the amount of near addition required by presbyopes.
Demonstration of tunable optical generation of higher-order modulation formats using nonlinearities and coherent frequency comb

We demonstrate the generation of optical 16-QAM and 64-QAM at EVM 6.8% and 6.4% respectively using nonlinearities and coherent frequency comb. We also demonstrated a successful transmission through 80-km SMF-28 after compensating with 20-km DCF with negligible penalty.
Network‐Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

We develop a network‐based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network‐based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network‐based tremor fingerprints can be continuously enriched with newly available data.
Handover prediction for wireless networks in office environments using Hidden Markov Model

User mobility can cause severe degradation of QoS, which can be counteracted by performing handovers at the right moment. However, to perform a robust handover is a challenging task. In this paper, we study the handover problem for mobile wireless networks in the office environment, where the users' mobility is predictable. We propose a new mechanism using the Hidden Markov Model (HMM) to predict the Received Signal Strength (RSS) values. Based on the predicted values, each mobile user can perform the handover decision on his own using his preferred decision making algorithm. Extensive simulations were carried out for an office environment to evaluate the performance of the prediction handover scheme. The results showed that our HMM model can predict the RSS values accurately. The knowledge obtained from prediction is considered in the handover decision making algorithm, which can result in a seamless handover. Moreover, with the knowledge about the future RSS, mobile users can avoid multiple handovers in a short time known as “Ping-Pong” effect, which in turn reduces the signaling overheads on the network.
Real-Time Audio-to-Score Alignment Using Particle Filter for Coplayer Music Robots

Our goal is to develop a coplayer music robot capable of presenting a musical expression together with humans. Although many instrument-performing robots exist, they may have difficulty playing with human performers due to the lack of the synchronization function. The robot has to follow differences in humans' performance such as temporal fluctuations to play with human performers. We classify synchronization and musical expression into two levels: (1) melody level and (2) rhythm level to cope with erroneous synchronizations. The idea is as follows: When the synchronization with the melody is reliable, respond to the pitch the robot hears, when the synchronization is uncertain, try to follow the rhythm of the music. Our method estimates the score position for the melody level and the tempo for the rhythm level. The reliability of the score position estimation is extracted from the probability distribution of the score position. The experimental results demonstrate that our method outperforms the existing score following system in 16 songs out of 20 polyphonic songs. The error in the prediction of the score position is reduced by 69% on average. The results also revealed that the switching mechanism alleviates the error in the estimation of the score position.
Robust Head Tracking with Particles Based on Multiple Cues Fusion

This paper presents a fully automatic and highly robust head tracking algorithm based on the latest advances in real-time multi-view face detection techniques and multiple cues fusion under particle filter framework. Visual cues designed for general object tracking problem hardly suffice for robust head tracking under diverse or even severe circumstances, making it a necessity to utilize higher level information which is object-specific. To this end we introduce a vector-boosted multi-view face detector [2] as the " face cue " in addition to two other general visual cues targeting the entire head, color spatiogram[3] and contour gradient. Data fusion is done by an extended particle filter which supports multiple distinct yet interrelated state vectors (referring to face and head in our tracking context). Furthermore, pose information provided by the face cue is exploited to help achieve improved accuracy and efficiency in the fusion. Experiments show that our algorithm is highly robust against target position, size and pose change as well as unfavorable conditions such as occlusion, poor illumination and cluttered background.
JAMSTEC Model Intercomparision Project (JMIP)

The JAMSTEC Model Intercomparison Project (JMIP) provides a first opportunity to systematically compare multiple global models developed and/or used in JAMSTEC with the aim of moving toward better weather and climate predictions. Here, we evaluate climate simulations obtained from atmospheric models (AFES and MIROC5), atmospheric model with slab ocean (NICAM.12), and fully coupled model (SINTEX-F1 and SINTEX-F2). In these simulations, the sea surface temperature is fixed (for AFES and MIROC5) or nudged (NICAM.12, SINTEX-F1, and SINTEX-F2) to the observed historical one. We focus on the climatology and variability of precipitation and its associated phenomena, including the basic state, the energy budget of the atmosphere, extratropical cyclones, teleconnection, and the Asian monsoon. We further discuss the possible causes of similarities and differences among the five JMIP models. Though some or most of the dynamical and physical packages in the JMIP models have been developed independently, common model biases are found among them. The AFES and MIROC5, and the SINTEX-F1 and SINTEX-F2, show strong similarities. In many respects, NICAM.12 shows unique characteristics, such as the distributions of precipitation, shortwave radiation, and explosive extratropical cyclones and the onset of the Asian summer monsoon. To some extent, the similarities and differences among the JMIP models overlap with those among the Coupled Model Intercomparison Project Phase-5 (CMIP5) models, suggesting that JMIP can be used as a simple and in-depth version of CMIP to investigate the mechanisms of model bias. We suggest that this JMIP framework could be expanded to an intercomparison of weekly-to-seasonal scale weather forecasting; here, more fruitful discussion is expected through intensive collaboration among modeling and observation groups.
Activated human T lymphocytes express MHC class I heavy chains not associated with beta 2-microglobulin

We present here the molecular characterization of a new activation- induced surface structure on human T lymphocytes, termed LA45, with high homology (93% at protein level) to MHC class I molecules. Antigen modulation and sequential immunoprecipitation experiments revealed that LA45 and HLA class I proteins do not crossreact with the corresponding antibodies. Furthermore, LA45 is not associated with beta 2-m. On the other hand, we could show that the separation of HLA-A,B,C and beta 2m molecules, induced by SDS-denaturation, leads to a conformational change in the heavy chain in such a way that it becomes reactive with LA45. The 90/45 kD LA45 proteins thus appear to be non-beta 2m- associated MHC class I alpha chains that are selectively expressed by activated but not by resting human T lymphocytes.
Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks

We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potentially mobile secondary users. We use the technique of Lyapunov Optimization to design an online flow control, scheduling and resource allocation algorithm that meets the desired objectives and provides explicit performance guarantees.
Multiview Video Service Framework for 3D Mobile Devices

Multiview broadcasting has recently gained much attraction from academic and commercial fields because it can deliver the immersive viewing of natural scenes. Therefore, various researches implementing multi-view video broadcasting have been conducted with a variety of different system architectures. However, the previous systems have focused on wired broadcasting. On the contrary, our system is designed to be applied to mobile 3DTV broadcasting. We present the system adaptable to mobile clients where the load on the client is reduced as much as possible. MPEG-21 multiview DIA description schemes are presented for the efficient adaptation between the serer and client.
Analysis of Intracranial Aneurysm Haemodynamics Altered by Wall Movement

Computational fluid dynamics is intensively used to deepen our understanding of aneurysm growth and rupture in an attempt to support physicians during therapy planning. Numerous studies assumed fully rigid vessel walls in their simulations, whose sole haemodynamics may fail to provide a satisfactory criterion for rupture risk assessment. Moreover, direct in vivo observations of intracranial aneurysm pulsation were recently reported, encouraging the development of fluid–structure interaction for their modelling and for new assessments. In this work, we describe a new fluid–structure interaction functional setting for the careful evaluation of different aneurysm shapes. The configurations consist of three real aneurysm domes positioned on a toroidal channel. All geometric features, employed meshes, flow quantities, comparisons with the rigid wall model and corresponding plots are provided for the sake of reproducibility. The results emphasise the alteration of flow patterns and haemodynamic descriptors when wall deformations were taken into account compared with a standard rigid wall approach, thereby underlining the impact of fluid–structure interaction modelling.
A Holistic Approach of Incubator Strategies in the Entrepreneurial Support Ecosystem

Entrepreneurial support ecosystem consists of multiple diversified players. Incubators, as central part of the entrepreneurial support ecosystem are crucial as they build the bridge between incubatees and their external environment. Our study aims to explore the strategic relationships of incubators with the other ecosystem players by engaging ecosystem and strategic alignment theories. The study investigate the articulation of specialization and diversification strategies (internal alignment) with those of coopetition (external alignment). We use a qualitative exploratory study based on 48 semi-structured interviews of five case studies related to the main types of incubators identified. This ecosystem methodology promotes a holistic approach to explore strategic relationships within the entrepreneurial support ecosystem by crossing the strategic visions of different players. Our main results show that incubators differentiate themselves by using various combinations of individual and collective strategi.
Mental States Recognition from Communication

In order to perform effective communication, agents must be able to foresee the effects of their utterances on the addressee’s mental state. In this paper we study the consequences of an utterance on the mental state of a hearer. Given an agent communication language with a STRIPS-like semantics, we propose a set of criteria that allow the binding of the speaker’s mental state to its uttering of a certain sentence. On the basis of these criteria, we give an abductive procedure that the hearer can adopt to partially r ecognize the speaker’s mental state that led to a specific utterance.
Mind the Gap: Bridging Multi-Domain Query Workloads with EmptyHeaded

Executing domain specific workloads from a relational data warehouse is an increasingly popular task. Unfortunately, classic relational database management systems (RDBMS) are suboptimal in many domains (e.g., graph and linear algebra queries), and it is challenging to transfer data from an RDBMS to a domain specific toolkit in an efficient manner. This demonstration showcases the EmptyHeaded engine: an interactive query processing engine that leverages a novel query architecture to support efficient execution in multiple domains. To enable a unified design, the EmptyHeaded architecture is built around recent theoretical advancements in join processing and automated in-query data transformations. This demonstration highlights the strengths and weaknesses of this novel type of query processing architecture while showcasing its flexibility in multiple domains. In particular, attendees will use EmptyHeaded's Jupyter notebook front-end to interactively learn the theoretical advantages of this new (and largely unknown) approach and directly observe its performance impact in multiple domains.
Effect of various analgesics and lubricants on isolation of Chlamydia trachomatis and Neisseria gonorrhoeae

Five preparations used as analgesics or lubricants in surgical, obstetrical, gynecological, and investigative procedures were tested for their effect on the isolation of Chlamydia trachomatis. Three lignocaine preparations and a lubricating jelly containing 2% phenol were inhibitory to chlamydiae. In contrast, K-Y lubricating jelly was relatively nontoxic to chlamydiae. Since K-Y jelly also had only slight toxic activity against gonococci, it is recommended for the lubrication of instruments which need to be used for the efficient isolation of these microorganisms.
Formulations and algorithms for multichannel complex NMF

This paper studies some formulations and algorithms for the multichannel extension of nonnegative matrix factorization (NMF). We model the inter-channel characteristics of each NMF basis, including both the amplitude ratios and the phase differences on a channel pair. The learned inter-channel characteristics provide useful information for binding each NMF basis to each source component in such a situation that multiple sources are mixed in a convolutive manner and observed at multiple microphones. Effective optimization algorithms based on majorization are derived by using properly designed auxiliary functions. Experimental results show that the algorithms converged favorably regardless of the initialization.
Using Ontologies and the Web to Learn Lexical Semantics

A variety of text processing tasks require or benefit from semantic resources such as ontologies and lexicons. Creating these resources manually is tedious, time consuming, and prone to error. We present a new algorithm for using the web to determine the correct concept in an existing ontology to lexicalize previously unknown words, such as might be discovered while processing texts. A detailed empirical comparison of our algorithm with two existing algorithms (Cilibrasi & Vitanyi 2004, Maedche et al. 2002) is described, leading to insights into the sources of the algorithms' strengths and weaknesses.
CCG Categories for Distributional Semantic Models

For the last decade, distributional semantics has been an active area of research to address the problem of understanding the semantics of words in natural language. The core principal of the distributional semantic approach is that the linguistic context surrounding a given word, which is represented as a vector, provides important information about its meaning. In this paper we investigate the possibility to exploit Combinatory Categorial Grammar (CCG) categories as syntactic features to be relevant for characterizing the context vector and hence the meaning of words. We find that the CCG categories can enhance the representation of verb meaning.
Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation

Dissolved gas analysis (DGA) has been widely applied to diagnose internal faults in transformer insulation systems. However, the accuracy of DGA technique is limited because of the lack of positive correlation of the fault-identifying gases with faults found in power transformers. This paper presents a laboratory study on the correlation between oil dissolved gas formation and partial discharge (PD) statistical parameters. Canonical correlation analysis (CCA) is employed to explore the underlying correlation and to extract principal feature parameters and gases in the development of different PD defects. This study is aimed to provide more information in assisting the separation, classification and identification of PD defects, which might improve the existing transformer dissolved gas analysis (DGA) schemes. An application of a novel ratio method for discharge diagnosis is proposed. The evaluation of DGA data both in laboratory and actual transformers proves the effectiveness of the method and the correlation investigation.