bluuebunny/crossref_metadata_2025 huggingface dataset:
Subperiosteal haematoma of the orbit secondary to frontal sinusitis
Authors: Mozaffari Mona , Boyd Elena , Whiteside Olivia
Published: September 2018

Reported is the case of a 79-year-old woman initially diagnosed with periorbital abscess on the background of a recent upper respiratory tract infection. Unexpectedly, intraoperative findings were that of a haematoma rather than an abscess. Subperiosteal orbital haematoma (SOH) is an extremely rare complication of rhinosinusitis. In contrast to the more common periorbital abscess, it is seldom listed as a complication of sinusitis. A review of reported cases suggests an older patient demographic are affected by SOH in contrast to periorbital abscess which typically affects paediatric patients. Given current demographic trends toward an older patient population with multiple comorbidities, failure to consider SOH as a differential will have important implications on preoperative workup, perioperative care and final outcome for patients. We present this case as a reminder of a rare but important complication of a common disease.
Development and evaluation of a novel Vital Signs Alert device for use in pregnancy in low-resource settings
Authors: Nathan Hannah L , Vousden Nicola , Lawley Elodie , de Greeff Annemarie , Hezelgrave Natasha L , Sloan Nicola , Tanna Nina , Goudar Shivaprasad S , Gidiri Muchabayiwa F , Sandall Jane , Chappell Lucy C , Shennan Andrew H
Published: September 2018

Objectives

Haemorrhage, hypertension, sepsis and abortion complications (often from haemorrhage or sepsis) contribute to 60% of all maternal deaths. Each is associated with vital signs (blood pressure (BP) and pulse) abnormalities, and the majority of deaths are preventable through simple and timely intervention. This paper presents the development and evaluation of the CRADLE Vital Signs Alert (VSA), an accurate, low-cost and easy-to-use device measuring BP and pulse with an integrated traffic light early warning system. The VSA was designed to be used by all cadres of healthcare providers for pregnant women in low-resource settings with the aim to prevent avoidable maternal mortality and morbidity.

Methods

The development and the mixed-methods clinical evaluation of the VSA are described.

Results

Preliminary fieldwork identified that introduction of BP devices to rural clinics improved antenatal surveillance of BP in pregnant women. The aesthetics of the integrated traffic light system were developed through iterative qualitative evaluation. The traffic lights trigger according to evidence-based vital sign thresholds in hypertension and haemodynamic compromise from haemorrhage and sepsis. The VSA can be reliably used as an auscultatory device, as well as its primary semiautomated function, and is suitable as a self-monitor used by pregnant women.

Conclusion

The VSA is an accurate device incorporating an evidence-based traffic light early warning system. It is designed to ensure suitability for healthcare providers with limited training and may improve care for women in pregnancy, childbirth and in the postnatal period.
Moral reasons not to breastfeed: a response to Woollard and Porter
Authors: Callahan Laura Frances
Published: September 2018

Woollard and Porter argue that mothers have no moral duty to breastfeed their babies. Rather, mothers simply have moral reason(s) to breastfeed, stemming from the benefits of breast feeding for babies. According to Woollard and Porter, doing what one has moral reason to do is often supererogatory, not obligatory. I agree that mothers have no moral duty to breastfeed. However, it is misleading to suggest that mothers in general have moral reason to breastfeed and to liken not breastfeeding to not performing some supererogatory action. I will suggest mothers in general have pro tanto, but not all-things-considered, moral reason to breastfeed. Moreover, I suggest many mothers also have pro tanto moral reasons not to breastfeed. Breastfeeding is not always supererogatory; indeed, sometimes not breastfeeding is morally better. This is important because it contradicts dangerous assumptions in public discourse. One way of characterising popular consensus about breastfeeding is as Woollard and Porter do: mothers have a duty to breastfeed. But we might also characterise popular consensus differently: all moral reasons for/against breastfeeding are on the ‘pro’ breastfeeding side of the issue, whereas if there are any (good) reasons not to breastfeed these are non-moral (prudential). Woollard and Porter’s argument is important and correct in concluding that mothers have no duty to breastfeed, but it leaves this other false and dangerous idea intact and even subtly reinforces it.
A pilot study assessing the spiritual, emotional, physical/environmental, and physiological needs of mechanically ventilated surgical intensive care unit patients via eye tracking devices, head nodding, and communication boards
Authors: Duffy Erin I , Garry Jonah , Talbot Lillian , Pasternak David , Flinn Ashley , Minardi Casey , Dookram Michele , Grant Kathleen , Fitzgerald Debbie , Rubano Jerry , Vosswinkel James , Jawa Randeep S
Published: September 2018

Background

Mechanically ventilated patients in the intensive care unit (ICU) are unable to communicate verbally. We sought to evaluate their needs via a communication board (CB) and a novel eye tracking device (ETD) that verbalizes selections made by gazing.

Methods

This was a pilot prospective study conducted in a tertiary care surgical ICU. Continuously mechanically ventilated adult surgical ICU patients with a Richmond Agitation-Sedation Scale score of −1 to +1, without cognitive impairment, were eligible. We asked patients four yes-or-no questions to assess basic needs regarding presence of pain, need for endotracheal suction, satisfactory room temperature, and position comfort. Patients were then asked if there was anything else that they wanted to communicate. All responses were confirmed by head nodding.

Results

The median accuracy of the CB (100% (IQR 100%–100%)) for basic needs communication (yes/no questions) was comparable with that of the ETD (100% (IQR 68.8%–100%); p=0.14) in the 12 enrolled patients. Notably, 83% of patients desired to communicate additional information, ranging from spiritual (eg, desire for prayer/chaplain), emotional (eg, frustration, desire for comfort), physical/environmental (eg, television), to physiological (eg, thirst/hunger) needs.

Discussion

The majority of patients desired to communicate something other than basic needs. Unless specifically assessed via an assistive communication device (eg, CB or ETD), some of these other needs would have been difficult to discern.

Level of evidence

IV therapeutic care/management.
Aspects of quantum energy and stress in inhomogeneous unbounded dielectric continua
Authors: Goto S. , Tucker R. W. , Walton T. J.
Published: September 2018

This article explores a number of issues associated with the problem of calculating and detecting electromagnetic quantum induced energy and stress in a stationary dielectric material with a smooth inhomogeneous polarizability. By concentrating on a particular system composed of an ENZ-type (epsilon-near-zero) meta-material, chosen to have a particular anisotropic and smooth inhomogeneous permittivity, confined in an infinitely long perfectly conducting open rectangular waveguide, we are able to deduce analytically from the source-free Maxwell’s equations and their boundary conditions a complete set of bounded harmonic electromagnetic evanescent eigen-modes and their associated eigen-frequencies. Since these solutions prohibit the existence of asymptotic scattering states in the guide, the application of the conventional Lifshitz approach to the Casimir stress problem becomes uncertain. An alternative approach is adopted based upon the spectral properties of the system and a regularization scheme constructed with direct applicability to more general systems composed of dielectrics with smooth inhomogeneous permittivities and open systems that may only admit evanescent modes. This more general scheme enables one, for the first time, to prescribe precise criteria for the extraction of finite quantum expectation values from regularized mode sums together with error bounds on these values, and is used to derive analytic or numeric results for regularized electromagnetic ground state expectation values in the guide.
A review of total energy–momenta in GR with a positive cosmological constant
Authors: Szabados László B. , Tod Paul
Published: September 2018

A review is given of the various approaches to and expressions for total energy–momentum and mass in the presence of a positive cosmological constant in Einstein’s field equations, together with a discussion of the key conceptual questions, main ideas and techniques behind them.
MICROSTRUCTURES AND WEAR RESISTANCE OF THE ARGON ARC CLAD NICKEL-BASED COMPOSITES ON TITANIUM ALLOY
Authors: LI JIANING , SU MOLIN , ZHANG LIWEI
Published: September 2018

The composites were obtained by the argon-arc cladding (AAC) of the Deloro22-Si3N4-Fe pre-placed powders on a TA1 titanium alloy substrate, which improved the wear resistance of the substrate. Such composites were investigated by means of the scanning electron microscope (SEM), the microscope and the high resolution transmission electron microscope (HRTEM). The results indicated that the amorphous phases were produced in such AAC composites, increasing the wear resistance. With addition of Y2O3, lots of the micro/nanoscale particles were formed, which further improved the wear resistance of such AAC composites.
Identification of jointly correlated gene sets
Authors: Ren Yuanfang , Ay Ahmet , Gerke Travis A. , Kahveci Tamer
Published: September 2018

Associations between expressions of genes play a key role in deciphering their functions. Correlation score between pairs of genes is often utilized to associate two genes. However, the relationship between genes is often more complex; multiple genes might collaborate to control the transcription of a gene. In this paper, we introduce the problem of searching pairs of genes, which collectively correlate with another gene. This problem is computationally much harder than the classical problem of identifying pairwise gene associations. Exhaustive search is infeasible for transcriptomic datasets also; since for [Formula: see text] genes, there are [Formula: see text] possible gene combinations. Our method builds three filters to avoid computing the association for a large fraction of the gene combinations, which do not produce high correlation. Our experiments on a synthetic dataset and a prostate cancer dataset demonstrate that our method produces accurate results at the transcriptome level in practical time. Moreover, our method identifies biologically novel results which classical pairwise gene association studies are unlikely to discover.
H-curvature tensors on IK-normal complex contact metric manifolds
Authors: Turgut Vanli Aysel , Unal Inan
Published: September 2018

IK-normal complex contact metric manifolds have some important properties. There are several applications of this kind of contact manifolds in theoretical physics. In this paper, we studied on [Formula: see text]-curvature tensors for IK-normal complex contact metric manifolds. We have shown that there is no IK-normal complex contact metric manifold with constant sectional curvature and an IK-normal complex contact metric manifold is not Ricci semi-symmetric.
Hierarchical Spatial Aggregation for Level-of-Detail Visualization of 3D Thematic Data
Authors: Vollmer Jan Ole , Trapp Matthias , Schumann Heidrun , Döllner Jürgen
Published: September 2018

Thematic maps are a common tool to visualize semantic data with a spatial reference. Combining thematic data with a geometric representation of their natural reference frame aids the viewer’s ability in gaining an overview, as well as perceiving patterns with respect to location; however, as the amount of data for visualization continues to increase, problems such as information overload and visual clutter impede perception, requiring data aggregation and level-of-detail visualization techniques. While existing aggregation techniques for thematic data operate in a 2D reference frame (i.e., map), we present two aggregation techniques for 3D spatial and spatiotemporal data mapped onto virtual city models that hierarchically aggregate thematic data in real time during rendering to support on-the-fly and on-demand level-of-detail generation. An object-based technique performs aggregation based on scene-specific objects and their hierarchy to facilitate per-object analysis, while the scene-based technique aggregates data solely based on spatial locations, thus supporting visual analysis of data with arbitrary reference geometry. Both techniques can apply different aggregation functions (mean, minimum, and maximum) for ordinal, interval, and ratio-scaled data and can be easily extended with additional functions. Our implementation utilizes the programmable graphics pipeline and requires suitably encoded data, i.e., textures or vertex attributes. We demonstrate the application of both techniques using real-world datasets, including solar potential analyses and the propagation of pressure waves in a virtual city model.
Foveated Depth-of-Field Filtering in Head-Mounted Displays
Authors: Weier Martin , Roth Thorsten , Hinkenjann André , Slusallek Philipp
Published: September 2018

In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.
Understanding the Long-Term Use of Smart Speaker Assistants
Authors: Bentley Frank , Luvogt Chris , Silverman Max , Wirasinghe Rushani , White Brooke , Lottridge Danielle
Published: September 2018

Over the past two years the Ubicomp vision of ambient voice assistants, in the form of smart speakers such as the Amazon Echo and Google Home, has been integrated into tens of millions of homes. However, the use of these systems over time in the home has not been studied in depth. We set out to understand exactly what users are doing with these devices over time through analyzing voice history logs of 65,499 interactions with existing Google Home devices from 88 diverse homes over an average of 110 days. We found that specific types of commands were made more often at particular times of day and that commands in some domains increased in length over time as participants tried out new ways to interact with their devices, yet exploration of new topics was low. Four distinct user groups also emerged based on using the device more or less during the day vs. in the evening or using particular categories. We conclude by comparing smart speaker use to a similar study of smartphone use and offer implications for the design of new smart speaker assistants and skills, highlighting specific areas where both manufacturers and skill providers can focus in this domain.
The Role of Urban Mobility in Retail Business Survival
Authors: D'Silva Krittika , Jayarajah Kasthuri , Noulas Anastasios , Mascolo Cecilia , Misra Archan
Published: September 2018

Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform, Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level effects. Through careful statistical analysis of Foursquare and taxi mobility data, we uncover a set of discriminative features, belonging to the neighbourhood's static characteristics, the venue-specific customer visit dynamics, and the neighbourhood's mobility dynamics. We demonstrate that classifiers trained on such features can predict such survival with high accuracy, achieving approximately 80% precision and recall across the cities. We also show that the impact of such features varies across new and established venues and across different cities. Besides achieving a significant improvement over past work on business vitality prediction, our work demonstrates the vital role that mobility dynamics plays in the economic evolution of a city.
Online Deep Ensemble Learning for Predicting Citywide Human Mobility
Authors: Fan Zipei , Song Xuan , Xia Tianqi , Jiang Renhe , Shibasaki Ryosuke , Sakuramachi Ritsu
Published: September 2018

Predicting citywide human mobility is critical to an effective management and regulation of city governance, especially during a rare event (e.g. large event such as New Year's celebration or Comiket). Classical models can effectively predict routine human mobility, but irregular mobility during a rare event (precedented or unprecedented), which is much more difficult to model, has not drawn sufficient attention. Moreover, the complexity and non-linearity of human mobility hinders a simple model from making an accurate prediction. Bearing these facts in mind, we propose a novel online gating neural network framework with two phases. In the offline training phase, we train a gated recurrent unit-based human mobility predictor for each day in our training set, while in the online predicting phase, we construct an online adaptive human mobility predictor as well as a gating neural network that switches among the pre-trained predictors and the online adaptive human predictor. Our approach was evaluated using a real-world GPS-log dataset from Tokyo and Osaka and achieved a higher prediction accuracy than baseline models.
People Like Me
Authors: Feustel Clayton , Aggarwal Shyamak , Lee Bongshin , Wilcox Lauren
Published: September 2018

Increases in data complexity in personal informatics systems require new ways of contextualizing personal data to facilitate meaningful reflection. An emerging approach for providing such context includes augmenting one's personal data with the data of others "like them" to help individuals make sense of their data. However, we do not yet understand how an individual's self-reflection process is affected when the data of others is made available. In this paper, we investigate how people reflect on three types of personal data when presented alongside a large set of aggregated data of multiple cohorts. We conducted personal and cohort data reviews using a subset of participants from a mobile-sensing study that collected physical activity, digital social activity, and perceived stress, from 47 students over three weeks. Participants preferred to use characteristics of the data (e.g., maxima, minima) and graphical presentation (e.g., appearance of trends) along with demographic identities (e.g., age, gender) when relating to cohorts. We further characterize how participants incorporated cohort data into their self-reflection process, and conclude with discussion of the implications for personal informatics systems that leverage the data of "people like me" to enable meaningful reflection.
A Simple but Quantifiable Approach to Dynamic Price Prediction in Ride-on-demand Services Leveraging Multi-source Urban Data
Authors: Guo Suiming , Chen Chao , Wang Jingyuan , Liu Yaxiao , Xu Ke , Zhang Daqing , Chiu Dah Ming
Published: September 2018

Ride-on-demand (RoD) services such as Uber and Didi are becoming increasingly popular, and in these services dynamic prices play an important role in balancing the supply and demand to benefit both drivers and passengers. However, dynamic prices also create concerns. For passengers, the "unpredictable" prices sometimes prevent them from making quick decisions: one may wonder if it is possible to get a lower price if s/he chooses to wait a while. It is necessary to provide more information to them, and predicting the dynamic prices is a possible solution. For the transportation industry and policy makers, there are also concerns about the relationship between RoD services and their more traditional counterparts such as metro, bus, and taxi: whether they affect each other and how.

In this paper we tackle these two concerns by predicting the dynamic prices using multi-source urban data. Price prediction could help passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is based on urban data from multiple sources, including the RoD service itself, taxi service, public transportation, weather, the map of a city, etc. We train a simple linear regression model with high-dimensional composite features to perform the prediction. By combining simple basic features into composite features, we compensate for the loss of expressiveness in a linear model due to the lack of non-linearity. Additionally, the use of multi-source data and a linear model enables us to quantify and explain the relationship between multiple means of transportation by examining the weights of different features in the model. Our hope is that the study not only serves as an accurate prediction to make passengers more satisfied, but also sheds light on the concern about the relationship between different means of transportation for either the industry or policy makers.
CrowdProbe
Authors: Hong Hande , De Silva Girisha Durrel , Chan Mun Choon
Published: September 2018

Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes. Such information can be captured with inexpensive monitors and used to extract user behavior. To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months. We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 1.7 million opportunistically obtained probe request frames.

However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior. In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints. With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses.
SweepLoc
Authors: Li Mingkuan , Liu Ning , Niu Qun , Liu Chang , Chan S.-H. Gary , Gao Chengying
Published: September 2018

Indoor localization based on visual landmarks has received much attention in commercial sites with rich features (e.g., shopping malls, museums) recently because landmarks are relatively stable over a long time. Prior arts often require a user to take multiple independent images around his/her location, and manually confirm shortlisted landmarks. The process is sophisticated, inconvenient, slow, unnatural and error-prone. To overcome these limitations, we propose SweepLoc, a novel, efficient and automatic video-based indoor localization system. SweepLoc mimics our natural scanning around to identify nearby landmarks in an unfamiliar site to localize.

In SweepLoc, a user simply takes a short video clip (about 6 to 8 seconds) of his/her surroundings by sweeping the camera. Using correlation and scene continuity between successive video frames, it automatically and efficiently selects key frames (where potential landmarks are centered) and subsequently reduces the decision error on landmarks. With identified landmarks, SweepLoc formulates an optimization problem to locate the user, taking compass noise and floor map constraint into account. We have implemented SweepLoc in Android platform. Our extensive experimental results in a food plaza and a premium mall demonstrate that SweepLoc is fast (less than 1 second to localize), and achieves substantially better accuracy as compared with the state-of-the-art approaches (reducing the localization error by 30%).
Training-Free Human Vitality Monitoring Using Commodity Wi-Fi Devices
Authors: Li Xiang , Zhang Daqing , Xiong Jie , Zhang Yue , Li Shengjie , Wang Yasha , Mei Hong
Published: September 2018

Device-free sensing using ubiquitous Wi-Fi signals has recently attracted lots of attention. Among the sensed information, two important basic contexts are (i) whether a target is still or not and (ii) where the target is located. Continuous monitoring of these contexts provides us with rich datasets to obtain important high-level semantics of the target such as living habits, physical conditions and emotions. However, even to obtain these two basic contexts, offline training and calibration are needed in traditional methods, limiting the real-life adoption of the proposed sensing systems. In this paper, using the commodity Wi-Fi infrastructure, we propose a training-free human vitality sensing platform, WiVit. It could capture these two contexts together with the target's movements speed information in real-time without any human effort in offline training or calibration. Based on our extensive experiments in three typical indoor environments, the precision of activity detection is higher than 98% and the area detection accuracy is close to 100%. Moreover, we implement a short-term activity recognition system on our platform to recognize 4 types of actions, and we can reach an average accuracy of 94.2%. We also take a feasibility study of monitoring long-term activities of daily living to show our platform's potential applications in practice.
SAW
Authors: Mare Shrirang , Rawassizadeh Reza , Peterson Ronald , Kotz David
Published: September 2018

Token-based proximity authentication methods that authenticate users based on physical proximity are effortless, but lack explicit user intentionality, which may result in accidental logins. For example, a user may get logged in when she is near a computer or just passing by, even if she does not intend to use that computer. Lack of user intentionality in proximity-based methods makes them less suitable for multi-user shared computer environments, despite their desired usability benefits over passwords. We present an authentication method for desktops called Seamless Authentication using Wristbands (SAW), which addresses the lack of intentionality limitation of proximity-based methods. SAW uses a low-effort user input step for explicitly conveying user intentionality, while keeping the overall usability of the method better than password-based methods. In SAW, a user wears a wristband that acts as the user's identity token, and to authenticate to a desktop, the user provides a low-effort input by tapping a key on the keyboard multiple times or wiggling the mouse with the wristband hand. This input to the desktop conveys that someone wishes to log in to the desktop, and SAW verifies the user who wishes to log in by confirming the user's proximity and correlating the received keyboard or mouse inputs with the user's wrist movement, as measured by the wristband. In our feasibility user study (n=17), SAW proved quick to authenticate (within two seconds), with a low false-negative rate of 2.5% and worst-case false-positive rate of 1.8%. In our user perception study (n=16), a majority of the participants rated it as more usable than passwords.
A Tale of Two Interactions
Authors: Muralidhar Skanda , Mast Marianne Schmid , Gatica-Perez Daniel
Published: September 2018

People behave differently in different situations. With the advances in ubiquitous sensing technologies, it is now easier to capture human behavior across multiple situations automatically and unobtrusively. We investigate human behavior across two situations that are ubiquitous in hospitality (job interview and reception desk) with the objective of inferring performance on the job. Utilizing a dataset of 338 dyadic interactions, played by students from a hospitality management school, we first study the connections between automatically extracted nonverbal cues, linguistic content, and various perceived variables of soft skills and performance in these two situations. A correlation analysis reveals connection between perceived variables and nonverbal cues displayed during job interviews, and perceived performance on the job. We then propose a computational framework, with nonverbal cues and linguistic style from the two interactions as features, to infer the perceived performance and soft skills in the reception desk situation as a regression task. The best inference performance, with R2 = 0.40, is achieved using a combination of nonverbal cues extracted from the reception desk setting and the human-rated interview scores. We observe that some behavioral cues (greater speaking turn duration and head nods) are positively correlated to higher ratings for all perceived variables across both situations. The best performance using verbal content is achieved by fusion of LIWC and Doc2Vec features with R2 = 0.25 for perceived performance. Our work has implications for the creation of behavioral training systems with focus on specific behaviors for hospitality students.
Deep Room Recognition Using Inaudible Echos
Authors: Song Qun , Gu Chaojie , Tan Rui
Published: September 2018

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).
Cooperative Target Tracking and Signal Propagation Learning Using Mobile Sensors
Authors: Tan Jiajie , Wong Wangkit , Zhu Xinyu , Wu Hang , Chan S.-H. Gary
Published: September 2018

Target tracking refers to positioning mobile objects over time. The targets may be hospital patients, park visitors, mall shoppers, warehouse assets, etc. We consider a novel cooperative system to track targets, where a target carries low-cost RF tag which not only beacons its ID, but also receives and rebroadcasts beacons of tags within a certain hop away. Mobile sensors, equipped with localization and communication modules, are used to capture and forward the beacons to a server to track the targets. Such multi-hop approach greatly extends the sensing range of the mobile sensors, or equivalently, the beaconing range of the tags, leading to cost-effective deployment.

We propose Mosent, a highly accurate multi-hop system using mobile sensors for target tracking. To account for complex signal propagation in different indoor and outdoor environment, we represent the received signal strength (RSS) matrix overcoming the assumption on propagation model. Given sensor locations, beacons detected by the sensors and RSS matrix, Mosent jointly considers temporal and spatial information to track targets using a modified particle filter. Mosent has an optional, independent and offline module to learn spatial signal propagation in terms of RSS matrix using cooperative mobile sensors equipped with beaconing transceivers. We have implemented Mosent and conducted extensive experiments. Our results show that Mosent achieves 4.37m and 9.46m tracking error in the campus and the shopping mall, respectively, which outperforms other state-of-the-art approaches with significantly lower tracking error (often by more than 30%).
SoberMotion
Authors: You Chuang-Wen , Lin Ya-Fang , Chuang Yaliang , Lee Ya-Han , Hsu Pei-Yi , Lin Shih-Yao , Chang Chih-Chun , Chung Yi-Ju , Chen Yi-Ling , Huang Ming-Chyi , Shen Ping-Hsuan , Tseng Hsin-Tung , Wang Hao-Chuan
Published: September 2018

In this study, we sought to assist probation officers in their efforts to reduce the risk that offenders on probation would re-commit the offense of driving under the influence (DUI) of alcohol. We prototyped a support system called SoberMotion, in which a breathalyzer is connected to the offender's smart phone via Bluetooth. Development of the system was based on pilot interviews with probation officers, psychiatrists, and offenders aimed at identifying the challenges and opportunities associated with this type of technology-based support mechanism. A corresponding phone app logs alcohol use behavior and identifies situations in which offenders should evaluate their sobriety before operating a vehicle. We conducted a two-month field study involving eight DUI offenders on probation, with the aim of evaluating the feasibility of the SoberMotion system. Our results indicate that most of the participants who drank alcohol while following the program performed multiple alcohol screening tests before operating a vehicle in order to avoid re-committing DUI. Responses collected via qualitative interviews indicate that SoberMotion is an effective approach to extending the power of probation officers seeking to reduce the risk of DUI recidivism.
Origins of Hot Jupiters
Authors: Dawson Rebekah I. , Johnson John Asher
Published: September 2018

Hot Jupiters were the first exoplanets to be discovered around main sequence stars and astonished us with their close-in orbits. They are a prime example of how exoplanets have challenged our textbook, solar-system inspired story of how planetary systems form and evolve. More than twenty years after the discovery of the first hot Jupiter, there is no consensus on their predominant origin channel. Three classes of hot Jupiter creation hypotheses have been proposed: in situ formation, disk migration, and high-eccentricity tidal migration. Although no origin channel alone satisfactorily explains all the evidence, two major origin channels together plausibly account for properties of hot Jupiters themselves and their connections to other exoplanet populations.
Bubble Dynamics in Soft and Biological Matter
Authors: Dollet Benjamin , Marmottant Philippe , Garbin Valeria
Published: September 2018

Bubbles are present in a large variety of emerging applications, from advanced materials to biology and medicine, as either laser-generated or acoustically driven bubbles. In these applications, the bubbles undergo oscillatory dynamics and collapse inside—or near—soft and biological materials. The presence of a soft, viscoelastic medium strongly affects the bubble dynamics, both its linear resonance properties and its nonlinear behavior. Surfactant molecules or solid particles adsorbed on a bubble surface can also modify the bubble dynamics through the rheological properties of the interfacial layer. Furthermore, the interaction of bubbles with biological cells and tissues is highly dependent on the mechanical properties of these soft deformable media. This review covers recent developments in bubble dynamics in soft and biological matter for different confinement conditions: bubbles in a viscoelastic medium, coated by a viscoelastic layer, or in the vicinity of soft confinement or objects. The review surveys current work in the field and illustrates open questions for future research.
Active Surveillance as First-Line Management of Papillary Microcarcinoma
Authors: Ito Yasuhiro , Miyauchi Akira
Published: September 2018

Papillary thyroid microcarcinoma (PMC) is defined as papillary thyroid carcinoma ≤10 mm. Active surveillance of PMC without high-risk features, such as clinical node metastasis, distant metastasis, and clinical evidence of significant extrathyroid extension, was initiated in two Japanese hospitals in the mid-1990s. This strategy was incorporated into guidelines in Japan in 2010 and in the United States in 2015. In studies conducted by the two hospitals, most PMCs grew very slowly or did not grow, and none of the patients during active surveillance showed distant metastasis or died of thyroid carcinoma. Furthermore, none of the patients who underwent surgery after progression signs were detected showed significant recurrence. Therefore, we conclude that active surveillance should be the first line in management of low-risk PMC, because it is safer and less costly than immediate surgery. Active surveillance helps in avoiding adverse events of surgery and is an economical strategy.
Innate Immune Signaling in Nonalcoholic Fatty Liver Disease and Cardiovascular Diseases
Authors: Cai Jingjing , Xu Meng , Zhang Xiaojing , Li Hongliang
Published: September 2018

The physiological significance of innate immune signaling lies primarily in its role in host defense against invading pathogens. It is becoming increasingly clear that innate immune signaling also modulates the development of metabolic diseases, especially nonalcoholic fatty liver disease and cardiovascular diseases, which are characterized by chronic, low-grade inflammation due to a disarrangement of innate immune signaling. Notably, recent studies indicate that in addition to regulating canonical innate immune-mediated inflammatory responses (or immune-dependent signaling-induced responses), molecules of the innate immune system regulate pathophysiological responses in multiple organs during metabolic disturbances (termed immune-independent signaling-induced responses), including the disruption of metabolic homeostasis, tissue repair, and cell survival. In addition, emerging evidence from the study of immunometabolism indicates that the systemic metabolic status may have profound effects on cellular immune function and phenotypes through the alteration of cell-intrinsic metabolism. We summarize how the innate immune system interacts with metabolic disturbances to trigger immune-dependent and immune-independent pathogenesis in the context of nonalcoholic fatty liver disease, as representative of metabolic diseases, and cardiovascular diseases.
Personality Across the Life Span
Authors: Costa Paul T. , McCrae Robert R. , Löckenhoff Corinna E.
Published: September 2018

Trait stability and maturation are fundamental principles of contemporary personality psychology and have been shown to hold across many cultures. However, it has proven difficult to move beyond these general findings to a detailed account of trait development. There are pervasive and unexplained inconsistencies across studies that may be due to ( a) insufficient attention to measurement error, ( b) subtle but age-sensitive differences in alternative measures of the same trait, or ( c) different perspectives reflected in self-reports and observer ratings. Multiscale, multimethod—and ideally multinational—studies are needed. Several hypotheses have been proposed to account for trait stability and change, but supporting evidence is currently weak or indirect; trait development is a fertile if sometimes frustrating field for theory and research. Beyond traits, there are approaches to personality development that are of interest to students of adult development, and these may be fruitfully addressed from a trait perspective.
A Lucky Accident: Brugada Syndrome Associated with Out-of-Hospital Cardiac Arrest
Authors: Lee Michelle T. , Marah Naddi
Published: September 2018

About 350,000 cases of out-of-hospital cardiac arrest (OHCA) occur yearly in the United States. Unfortunately, even with treatment from emergency medical service (EMS) staff and hospitalization, only 12% survive past discharge for multiple reasons. Classically, Brugada syndrome (BrS) initially presents as a new syncopal episode in young males without obstructive coronary artery disease (CAD). However, in this case report, a patient who emergently presented with a ST-elevation myocardial infarction (STEMI) challenges the stereotypical presentation. Despite successful stent placement for relatively minor obstructive CAD, new ST-segment elevations appeared on electrocardiogram (ECG) and persistent ventricular fibrillation arrests may signify an additional underlying pathology of BrS.
Dynamic Contrast-Enhanced Imaging as a Prognostic Tool in Early Diagnosis of Prostate Cancer: Correlation with PSA and Clinical Stage
Authors: Wu Xingchen , Reinikainen Petri , Kapanen Mika , Vierikko Tuula , Ryymin Pertti , Kellokumpu-Lehtinen Pirkko-Liisa
Published: September 2018

Background and Purpose. Although several methods have been developed to predict the outcome of patients with prostate cancer, early diagnosis of individual patient remains challenging. The aim of the present study was to correlate tumor perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical prognostic factors and further to explore the diagnostic value of DCE-MRI parameters in early stage prostate cancer. Patients and Methods. Sixty-two newly diagnosed patients with histologically proven prostate adenocarcinoma were enrolled in our prospective study. Transrectal ultrasound-guided biopsy (12 cores, 6 on each lobe) was performed in each patient. Pathology was reviewed and graded according to the Gleason system. DCE-MRI was performed and analyzed using a two-compartmental model; quantitative parameters including volume transfer constant (Ktrans), reflux constant (Kep), and initial area under curve (iAUC) were calculated from the tumors and correlated with prostate-specific antigen (PSA), Gleason score, and clinical stage. Results. Ktrans (0.11 ± 0.02 min−1 versus 0.16 ± 0.06 min−1; p<0.05), Kep (0.38 ± 0.08 min−1 versus 0.60 ± 0.23 min−1; p<0.01), and iAUC (14.33 ± 2.66 mmoL/L/min versus 17.40 ± 5.97 mmoL/L/min; p<0.05) were all lower in the clinical stage T1c tumors (tumor number, n=11) than that of tumors in clinical stage T2 (n=58). Serum PSA correlated with both tumor Ktrans (r=0.304, p<0.05) and iAUC (r=0.258, p<0.05). Conclusions. Our study has confirmed that DCE-MRI is a promising biomarker that reflects the microcirculation of prostate cancer. DCE-MRI in combination with clinical prognostic factors may provide an effective new tool for the basis of early diagnosis and treatment decisions.
The State-of-the-Art of Knowledge-Intensive Agriculture: A Review on Applied Sensing Systems and Data Analytics
Authors: Basnet Barun , Bang Junho
Published: September 2018

The application of sensors and information and communication technology (ICT) in agriculture has played a vital role in improving agricultural production and the value chain. Recently, the use of data analytics has shifted agriculture from input-intensive to knowledge-intensive as a large amount of agricultural data can be stored, shared, and analyzed to create information. In this paper, we have reviewed existing sensors and data analytics techniques used in different areas of agriculture. We have classified agriculture into five categories and reviewed the state-of-the-art technology in practice and ongoing research in each of these areas. Also, we have presented a case study of Korean scenario compared with other developed nations and addressed some of the issues associated with it. Finally, we have discussed current and future challenges and provided our views on how such issues can be addressed.
GIS‐Enabled Culvert Design: A Case Study in Tuscaloosa, Alabama
Authors: Greer Ashton D. , Wilbanks Zachary B. , Clifton Leah D. , Wilson Bradford , Graettinger Andrew J.
Published: September 2018

A GIS‐enabled culvert design module is presented. This module employs Python programming to combine a proposed culvert location, topography, land use, and rainfall data to automatically design a culvert. The module is embedded within ESRI ArcGIS 10.4 software, providing a seamless single platform that eliminates error propagation associated with cross‐platform data transfer as well as providing 95% time savings over traditional calculation methods. The module uses United States Geological Survey digital elevation data to analyze watershed topography. Runoff coefficients are determined from data available through the National Land Cover Database. Rainfall data are retrieved from the National Oceanic and Atmospheric Administration and combined with watershed and land use information to calculate peak discharge using the rational method. Peak discharge is then combined with culvert design parameters to design a single‐barrel culvert. The module was used to redesign ten existing culverts along a highway in Tuscaloosa, Alabama, resulting in designs for updated land cover and rainfall conditions. Results from the techniques developed herein can be used for planning purposes and to highlight vulnerabilities in the existing infrastructure. The automation methods may be extended to other hydrologic objectives and runoff mitigation design such as open‐channel design and detention or retention ponds.
Tracking Control of Chaotic Systems via Optimized Active Disturbance Rejection Control
Authors: Abu Khadra Fayiz
Published: September 2018

For tracking control of chaotic systems, we develop an active disturbance rejection (ADR) control method. Using the first state of the system as the only available state, a time-varying bandwidth extended state observer reconstructs the remaining states and the total disturbance. A time-varying bandwidth feedback controller forces all the states of the system to follow exactly the reference signal and its derivative. The parameters of the ADR controller are optimized using a genetic algorithm. As the objective function, we chose the weighted sum of the integral of the absolute error and the integral of the absolute control signal. Two chaotic systems—the Duffing system and the Genesio–Tesi system—are considered in computer simulation tests. Results of these simulations are presented to demonstrate the effectiveness of the ADRC method in controlling chaotic systems.