Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved local ternary pattern features with application to pedestrian detection

Pedestrian detection is one of the challenging research topics in computer vision and efficient feature representation of a pedestrian attracts more and more attention. Traditional features such as Histogram of Oriented Gradients (HOG) were widely used in pedestrian detection, but because of their poor texture description ability, these feature based methods cannot achieve satisfactory pedestrian detection results. In this paper, the Local Ternary Patterns (LTP) is introduced into pedestrian detection, and a new feature, named Sqrt-LTP feature, is proposed by finding the square root of LTP feature. Experimental results on INRIA database set show that our Sqrt-LTP feature based pedestrian detection method can achieve more robust performance than both traditional HOG and LTP based approaches, indicating that the proposed Sqrt-LTP feature is a more discriminative feature and can be used for texture classification.
Application of Linux Audio in Hearing Aid Research

Development of algorithms for digital hearing aid processing includes many steps from the first algorithmic idea and implementation up to field tests with hearing impaired patients. Each of these steps has its own requirements towards the development environment. However, a common platform throughout the whole development process is desirable. This paper gives an overview of the application of Linux Audio in the hearing aid algorithm development process. The performance of portable hardware in terms of delay, battery runtime and processing power is investigated.
NAVIGATION PERFORMANCE EFFECTS OF RENDER METHOD AND LATENCY IN MOBILE AUDIO AUGMENTED REALITY

This paper describes a pilot study and main experiment that assess user performance at navigating to spatialised sound sources using a mobile audio augmented reality system. Experiments use a novel outdoor paradigm with an application-relevant navigation task to compare perception of two binaural rendering methods under several head-turn latencies. Binaural rendering methods examined were virtual, 6-speaker, first-order Ambisonic and virtual 12-speaker VBAP techniques. This study extends existing indoors research on the effects of head-turn latency for seated listeners. The pilot study examined the effect of capture radius (of 1, 2, 3, 4 and 5 metres) on mean distance efficiency for a single user’s navigation path to sound sources. A significant performance degradation was found to occur for radii of 2 m. The main experiment examined the effect of render method and total system latency to head-turns (176 ms minimum plus 0, 100, 200, 400 and 800 ms) on mean distance efficiency and subjective stability rating (on a scale of 1-5), for 8 participants. Render method significantly affected distance efficiency and 800 ms of added head-turn latency significantly affected subjective stability. Also, the study revealed a significant interaction effect of render method and head-turn latency: Ambisonic rendering didn’t significantly affect subjective stability due to added head-turn latency, while VBAP rendering did. Thus, it appears rendering method can mitigate or potentiate stability effects of head-turn latency. The study also exemplifies that the novel experimental paradigm is capable of revealing statistically significant performance differences between mobile audio AR implementations.
A virtual cloud computing provider for mobile devices

Along with the explosive growth of the mobile applications and up-raising cloud computing concept, mobile cloud computing appears to be a new potential technology for mobile services. Mobile cloud computing maps cloud computing ideas on top of the mobile environment, and overcomes obstacles that deal with performance (i.e. battery life, CPU, storage, and bandwidth), environment (i.e. heterogeneity, scalability, availability) and security (i.e. reliability, privacy) discussed in mobile computing. In this position paper we are going to focus in depth on a Hadoop based framework for ad-hoc mobile cloud computing — we specifically refer to a research paper and the authors' decision to use and port Hadoop to build a Virtual Cloud Computing Provider for Mobile Devices. In the beginning, we are going to outline a short introduction and enumerate the challenges that we face implementing a framework for mobile cloud computing. We conclude this paper with a short example of other Map-Reduce based MCC (Mobile Cloud Computing) framework, which achieved better performance using its own custom implementation.
Causal relationship between the volatility of stock market and selected macroeconomic variables: Case of Malaysia

This study examines the dynamic relationship between the volatility of stock market and macroeconomics’ volatility in Malaysia during the period of February 1991 to February 2013. For this purpose, monthly data on the KLCI stock index and a set of macroeconomic variables are used. Firstly, in order to estimate the volatility of each series, the well-known GARCH family models are employed. The empirical stylized facts in the stock indices and the macroeconomic variables are presented. Secondly, in order to investigate the causation between variables, the Toda and Yamamoto causality test through a VAR method is conducted using the volatility of stock market and macroeconomic variables. In addition, from the VAR, we generate Impulse Response Function and Variance Decomposition to track the evolution of economic shocks through the system. The result of this study shows that exchange rate volatility affects Malaysian stock market volatility considerably. Furthermore, trade openness is able to affect stock market volatility in Malaysia. These results will provide more precise information for investors, hedgers, managers and policy makers.
Vascular Health and Risk Management Dovepress Video Abstract Reversing Anticoagulant Effects of Novel Oral Anticoagulants: Role of Ciraparantag, Andexanet Alfa, and Idarucizumab

Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: Abstract: Novel oral anticoagulants (NOACs) are increasingly used in clinical practice, but lack of commercially available reversal agents is a major barrier for mainstream use of these therapies. Specific antidotes to NOACs are under development. Idarucizumab (aDabi-Fab, BI 655075) is a novel humanized mouse monoclonal antibody that binds dabigatran and reverses its anticoagulant effect. In a recent Phase III study (Reversal Effects of Idarucizumab on Active Dabigatran), a 5 g intravenous infusion of idarucizumab resulted in the normalization of dilute thrombin time in 98% and 93% of the two groups studied, with normalization of ecarin-clotting time in 89% and 88% patients. Two other antidotes, andexanet alfa (PRT064445) and ciraparantag (PER977) are also under development for reversal of NOACs. In this review, we discuss commonly encountered management issues with NOACs such as periprocedural management, laboratory monitoring of anticoagulation, and management of bleeding. We review currently available data regarding specific antidotes to NOACs with respect to pharmacology and clinical trials.
Cost-Aware Activity Scheduling for Compressive Sleeping Wireless Sensor Networks

In this paper, we consider a compressive sleeping wireless sensor network (WSN) for monitoring parameters in the sensor field, where only a fraction of sensor nodes (SNs) are activated to perform the sensing task and their data are gathered at a fusion center (FC) to estimate all the other SNs' data using the compressive sensing (CS) principle. Typically, research published concerning CS implicitly assume the sampling costs for all samples are equal and suggest random sampling as an appropriate approach to achieve good reconstruction accuracy. However, this assumption does not hold for compressive sleeping WSNs, which have significant variability in sampling cost owing to the different physical conditions at particular SNs. To exploit this sampling cost nonuniformity, we propose a cost-aware activity scheduling approach that minimizes the sampling cost with constraints on the regularized mutual coherence of the equivalent sensing matrix. In addition, for the case with prior information about the signal support, we extend the proposed approach to incorporate the prior information by considering an additional constraint on the mean square error (MSE) of the oracle estimator for sparse recovery. Our numerical experiments demonstrate that, in comparison with other designs in the literature, the proposed activity scheduling approaches lead to improved tradeoffs between reconstruction accuracy and sampling cost for compressive sleeping WSNs.
Unbiased plain gradient adaptive IIR notch filter for echo cancellation

An adaptive echo canceller based on adaptive IIR notch filter with unbiased plain gradient algorithm is proposed, for the echo cancellation in telephone network. The unbiased plain gradient algorithm employs the bias removal techniques to remove the bias existing in the estimated parameter, resulting in obtaining good estimate of parameter for the echo canceller. Simulation results show that the proposed canceller outperforms the canceller with the classical LMS algorithm in terms of echo return loss enhancement (ERLE).
On the sampling requirements for exponential-work free-energy calculations

Calculation of free-energy differences using exponential-work averaging methods is examined, focusing on the amount of sampling required to converge to an accurate result. Prior research that has demonstrated that the necessary amount of sampling scales exponentially with the dissipated work associated with the reverse trajectory is reviewed. The result is demonstrated through a pedagogical application to a model process involving adiabatic compression and expansion of a gas.
Designing a socially assistive robot for pediatric care

We present the design of the Huggable robot that can playfully interact with children and provide socio-emotional support for them in pediatric care context. Our design takes into consideration that many young patients are nervous, intimidated, and are socio-emotionally vulnerable at hospitals. The Huggable robot has a childish and furry look be perceived friendly and can perform swift and smooth motions. It uses a smart phone device for its computational power and internal sensors. The robot's haptic sensors perceive physical touch and can use the information in meaningful ways. The modular arm component allows easy sensor replacement and increases the usability of the Huggable robot for various pediatric care services. From a preliminary pilot user study with two healthy and two ill children, all participants enjoyed playing with the robot but the two children with medical conditions showed caring and empathetic behaviors than the two health children. We learned various types of physical touch occurred during the child-robot interaction, and will continue to develop more intelligent haptic sensory system for the Huggable robot to better assist and support child patients' socio-emotional needs.
Exploiting wavelet-domain intra-scale and inter-scale dependencies in Bayesian compressive sensing with context modeling

We propose a novel Bayesian compressive sensing reconstruction algorithm based on the context modeling of intra-scale wavelet coefficients, which utilizes the statistical dependencies in different directions. We assume that the wavelet coefficients obey a spike-and-slab probability model, whose parameters can be estimated according to a novel context-based model. In the context-based model, 3×3, 5×5 and 7×7 neighboring blocks are classified into 3 classes, 4 classes and 4 classes respectively. By determining the significance state of each class and parent coefficient, we estimate the significance probability of the current coefficient. Based on the above new wavelet coefficients' prior probability model, we propose the corresponding Bayesian compressive sensing reconstruction algorithm by using Markov Chain Monte Carlo (MCMC) method. Experimental results show that compared with the tree-structured wavelet compressive sensing (TSW-CS) which only uses the interscale dependencies, the proposed algorithm improves the peak-signal-to-noise-ratio (PSNR) up to nearly 2dB at the sampling rate of 0.9.
HIF‐1 is expressed in normoxic tissue and displays an organ‐specific regulation under systemic hypoxia

Adaptation to hypoxia is regulated by hypoxia‐inducible factor 1 (HIF‐1), a heterodimeric transcription factor consisting of an oxygen‐regulated α subunit and a constitutively expressed β subunit. Although HIF‐1 is regulated mainly by oxygen tension through the oxygen‐dependent degradation of its α subunit, in vitro it can also be modulated by cytokines, hormones and genetic alterations. To investigate HIF‐1 activation in vivo, we determined the spatial and temporal distribution of HIF‐1 in healthy mice subjected to varying fractions of inspiratory oxygen. Immunohisto‐chemical examination of brain, kidney, liver, heart, and skeletal muscle revealed that HIF‐1α is present in mice kept under normoxic conditions and is further in‐creased in response to systemic hypoxia. Moreover, immunoblot analysis showed that the kinetics of HIF‐1 α expression varies among different organs. In liver and kidney, HIF‐1 α reaches maximal levels after1hand gradually decreases to baseline levels after4hof continuous hypoxia. In the brain, however, HIF‐1α is maximally expressed after 5 h and declines to basal levels by 12 h. Whereas HIF‐1 β is constitutively expressed in brain and kidney nuclear extracts, its hepatic expression increases concomitantly with HIF‐1 α. Overall, HIF‐1 α expression in normoxic mice suggests that HIF‐1 has an important role in tissue homeostasis. —Stroka, D. M. , Burkhardt, T. , Desbaillets, I. , Wenger, R. H. , Neil, D. A. H. , Bauer, C. , Gassmann, M. , Candinas, D. HIF‐1 is expressed in normoxic tissue and displays an organ specific regulation under systemic hypoxia. FASEB J. 15, 2445–2453 (2001)
STROBE-AMS: recommendations to optimise reporting of epidemiological studies on antimicrobial resistance and informing improvement in antimicrobial stewardship

Objectives To explore the accuracy of application of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) tool in epidemiological studies focused on the evaluation of the role of antibiotics in selecting resistance, and to derive and test an extension of STROBE to improve the suitability of the tool in evaluating the quality of reporting in these area. Methods A three-step study was performed. First, a systematic review of the literature analysing the association between antimicrobial exposure and acquisition of methicillin-resistant Staphylococcus aureus and/or multidrug-resistant Acinetobacter baumannii was performed. Second, articles were reviewed according to the STROBE checklist for epidemiological studies. Third, a set of potential new items focused on antimicrobial-resistance quality indicators was derived through an expert two-round RAND-modified Delphi procedure and tested on the articles selected through the literature review. Results The literature search identified 78 studies. Overall, the quality of reporting appeared to be poor in most areas. Five STROBE items, comprising statistical analysis and study objectives, were satisfactory in <25% of the studies. Informative abstract, reporting of bias, control of confounding, generalisability and description of study size were missing in more than half the articles. A set of 21 new items was developed and tested. The new items focused particularly on the study setting, antimicrobial usage indicators, and patients epidemiological and clinical characteristics. The performance of the new items in included studies was very low (<25%). Conclusions Our paper reveals that reporting in epidemiological papers analysing the association between antimicrobial usage and development of resistance is poor. The implementation of the newly developed STROBE for antimicrobial stewardship (AMS) tool should enhance appropriate study design and reporting, and therefore contribute to the improvement of evidence to be used for AMS programme development and assessment.
The Rehabilitative Ideal versus the Criminogenic Reality: The Consequences of Warehousing Prisoners

ABSTRACT Using data on more than 55,000 offenders released from Minnesota prisons, we examine whether criminogenic effects arising from imprisonment may stem from a lack of institutional programming. We also evaluate the impact of participation in multiple correctional interventions on recidivism. The results show 31 percent of the Minnesota prisoners were warehoused, which significantly increased the odds of recidivism by 13 percent. Participation in at least one successful recidivism-reduction intervention lowered the odds of recidivism by 12 percent, while involvement in two effective programs decreased it by 26 percent. We conclude by discussing the implications of warehousing, which was more likely to occur for prisoners with brief stays in prison who were admitted as probation or parole violators.
Toward Efficient Querying of Compressed Network Payloads

Forensic analysts typically require access to application-layer information gathered over long periods of time to completely investigate network security incidents. Unfortunately, storing longitudinal network data is often at odds with maintaining detailed payload information due to the overhead associated with storing and querying such data. Thus, the analyst is left to choose between coarse information about long-term network activities or brief glimpses of detailed attack activity. In this paper, we take the first steps toward a storage framework for network payload information that provides a better balance between these two extremes. We take advantage of the redundancy found in network data to aggregate payload information into flexible and efficiently compressible data objects that are associated with network flows. To enable interactive querying, we introduce a hierarchical indexing structure for both the flow and payload information, which allows us to quickly prune irrelevant data and answer queries directly from the indexing information. Our empirical results on data collected from a campus network show that our approach can significantly reduce the volume of the stored data, while simultaneously preserving the ability to perform detailed queries with response times on the order of seconds.
Information technology-related specific traits: Linkage with general traits and relative explanatory power

A considerable body of work on technology adoption and use has focused on two information technology-related user traits, i.e., personal innovativeness and computer playfulness. However, there has been little effort to ground this body of work in trait research in psychology and other disciplines. One rationale for the emphasis on domain-specific traits is that they are more explanatory of outcome variables than general traits. However, this assumption has not been extensively examined in the IS empirical literature. Thus, this study seeks to ground the two IT-related traits in the Big Five trait model and empirically assess their relative capabilities in explaining variances in user outcomes. Implications of our findings and contributions are discussed.
Factors Affecting the Prescribing Patterns of Antibiotics and Injections

There are serious problems concerning the inadequate prescription of antibiotics and overuse of injections in primary care. However, the determinants of prescription patterns in Korea are not well-documented. To examine the area characteristics affecting the prescription of antibiotics and injections in primary care practices in the treatment of respiratory tract infections (RTIs), a nationwide cross-sectional study was performed in all 250 administrative districts of Korea. The outcome was modeled as a binary variable: over-prescription or not compared with the nation-wide average. Over-prescription of antibiotics was associated with the ratio of specialists to general physicians and over-prescription in previous years in the area (adjusted odds ratio [aOR], 4.8; 95% confidence interval [CI] 1.5-14.8; and aOR, 12.0; 95% CI 5.5-25.9, respectively). Over-use of injections was associated with younger population, urban living and the number of hospital beds in the area (aOR, 0.2; 95% CI 0.1-0.4; aOR, 0.3; 95% CI 0.1-0.8; and aOR, 0.4, 95% CI 0.2-0.9; respectively). There were differences in the prescribing patterns in different districts; prescription patterns were affected more by supply factors than by demand factors. Highly competitive medical environment associated with supply factors is a significant determinant of prescription patterns in Korea.
Compensation of amplifier nonlinearities on wavelet packet division multiplexing

Wavelet packet division multiplexing (WPDM) is a high-capacity, flexible and robust orthogonal multiplexing scheme in which the message signals are waveform-coded onto wavelet packet basis functions for transmission. However, WPDM suffers from severe performance degradation in the presence of high-power amplifier (HPA) nonlinearities. Data predistortion using the pth-order Volterra inverse is proposed to combat the amplifier nonlinearities in a WPDM system. A 5th-order Volterra inverse with truncated memory length is designed based on the Volterra series channel model. Computer simulations are presented to demonstrate the capability of the proposed technique in compensating amplifier nonlinearities even under system parameter discrepancies. Guidelines are also proposed for designing a wavelet filter which leads to better predistortion with the truncated Volterra inverse.
Resistive tapered stripline (RTS) in electroencephalogram recordings during MRI

A purely resistive stripline lead is introduced to reduce the specific absorption rate (SAR) increase due to the presence of leads in electroencephalography (EEG) measurements during magnetic resonance imaging. Discontinuities in the resistivity profile of the stripline introduce high-frequency inductive impedance that is not present in the low-frequency band of the EEG recordings. The changes in SAR introduced by the resistive tapered stripline (RTS) are studied using the finite-difference time-domain (FDTD) algorithm on a spherical phantom that models the human head. The FDTD simulations predict a reduction of the peak SAR when using RTS versus conventional resistive carbon fibers. Furthermore, our FDTD simulations have been validated on a simple prototype that exhibits no temperature increase on a spherical phantom.
White Matter Glucose Metabolism during Intracortical Electrostimulation: A Quantitative [18F]Fluorodeoxyglucose Autoradiography Study in the Rat

[(18)F]Fluorodeoxyglucose (FDG) autoradiography was used to analyze the effects of intracortical electrostimulation on local cerebral metabolic rate for glucose (LCMRglu). The hindleg area in rat brains was electrically stimulated with different frequencies (0, 0.1, 0.25, 0.5, 0.75, 1 stimulus trains per second, two animals per condition). The major result was a strong positive correlation between stimulation frequency and LCMRglu in the callosal fibers originating in the stimulated cortical area. At the highest stimulation frequency callosal LCMRglu was 50.01 micromol/min/100 g compared to 27.87 micromol/min/100 g at baseline. LCMRglu in gray and white matter control areas was stable across conditions. Direct injection of FDG in the stimulated cortex failed to produce increased callosal uptake, excluding the possibility that FDG uptake in the corpus callosum is related to axonal diffusion. Although several previous autoradiographic studies have demonstrated alterations in LCMRglu in white matter, correlations between neural activity and LCMRglu have never been explicitly addressed. Changes in white matter metabolism most likely reflect changes of electrical fiber activity and thus the presented results bear important implications for brain imaging studies.