Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor fusion of delay and non-delay signal using Kalman Filter with moving covariance

A movement of the omni-directional mobile robot is deflected by a slippage of its wheels, measurement such as acceleration, angular velocity and computer vision should be applied to compensate error from slippage of each wheel. Each sensor has advantages and drawbacks. For example, computer vision(CV) can measure the absolute position and the angle of the robot but it requires much time to process images which causes timing delay. On the other hand, inertial sensors such as accelerometers and gyroscope can swiftly response to its movement. Unfortunately, the position and the angle are obtained by integrating the measured signal which causes accumulated error of estimating the position and angle. In this paper, Kalman Filter is applied to implement fusion of the delay and non-delay data. When a delay signal is available, a classical method, which filter delay signal is re-performing Kalman operation at every step from the time of measured delay signal to current time. Therefore, we proposed method that only need to update the stored covariance between two different time instants. With less computational cost comparing to the classical method and the uniformity of the computation in every iteration, the efficiency of Kalman filter remains the same. Also, this method can be applied to soft-nonlinear case by utilizing Extended Kalman filter.
Efficacy of Azadirachta indica and Zingiber officinale on Hirsutism in Polycystic Ovarian Disease: A Randomized Controlled Trial

Background & objectives: Hirsutism is defined as excessive terminal hair growth on androgen dependent areas of the body in women, which grows in a typical male distribution pattern. Polycystic ovarian disease (PCOD) is the most common cause of it, in 70-80% of women. Hirsutism is recognized to cause profound distress in affected women. Despite of available conventional treatment modalities, its management is still a challenge for physicians. The present study was conducted to evaluate the efficacy and safety of bark of Azadirachta indica (poste darakht Neem) and Zingiber officinale (Zanjabeel) on hirsutism in polycystic ovarian disease. Methods: A randomized controlled clinical study was carried out at Institute’s Hospital, Bengaluru. 40 hirsute women with Modified ferriman gallwey score (mFGS) of >6 associated with polycystic ovarian syndrome were assigned to two groups. In test group, decoction of Neem 24 g and Zanjabeel 4 g was given in 2 divided doses and in control group, standard drug (tablet cyproterone acetate 2 mg & ethinyl estradial 0.035 mg) was administered orally from 5th day of cycle for 21 days/cycle for three consecutive cycles for 3 months. Menstrual cycle pattern, ferriman gallway score, body mass index, hormonal assay, lipid profile, pelvic ultrasonography and safety profile were assessed during the trial. Data was analysed with student t test, chi square test and fisher exact test. Results: Fasting insulin (p=0.030), lipid profile (p<0.001) were reduces significantly in test as compared to control group. Modified ferriman gallwey score (p=0.950), body mass index (p=0.235), free testosterone ((p=0.185), and luteinizing hormone (p=0.203), reduced in test group similar to control group. Significant improvement of menstrual cycle was reported in control group (p=0.039) after treatment. Follicle stimulating hormone remained unchanged in both groups. Interpretation and conclusion: The test drugs poste darakht Neem and Zanjabeel are as effective as control group to improve Modified ferriman-gallwey score, bio chemical and hormonal changes in hirsutism due to PCOD.
Receptor Activated Ca2+ Release Is Inhibited by Boric Acid in Prostate Cancer Cells

Background The global disparity in cancer incidence remains a major public health problem. We focused on prostate cancer since microscopic disease in men is common, but the incidence of clinical disease varies more than 100 fold worldwide. Ca2+ signaling is a central regulator of cell proliferation, but has received little attention in cancer prevention. We and others have reported a strong dose-dependent reduction in the incidence of prostate and lung cancer within populations exposed to boron (B) in drinking water and food; and in tumor and cell proliferation in animal and cell culture models. Methods/Principal Findings We examined the impact of B on Ca2+ stores using cancer and non-cancer human prostate cell lines, Ca2+ indicators Rhod-2 AM and Indo-1 AM and confocal microscopy. In DU-145 cells, inhibition of Ca2+ release was apparent following treatment with Ringers containing RyR agonists cADPR, 4CmC or caffeine and respective levels of BA (50 µM), (1, 10 µM) or (10, 20, 50,150 µM). Less aggressive LNCaP cancer cells required 20 µM BA and the non-tumor cell line PWR1E required 150 µM BA to significantly inhibit caffeine stimulated Ca2+ release. BA (10 µM) and the RyR antagonist dantroline (10 µM) were equivalent in their ability to inhibit ER Ca2+ loss. Flow cytometry and confocal microscopy analysis showed exposure of DU-145 cells to 50 µM BA for 1 hr decreased stored [Ca2+] by 32%. Conclusion/Significance We show B causes a dose dependent decrease of Ca2+ release from ryanodine receptor sensitive stores. This occurred at BA concentrations present in blood of geographically disparate populations. Our results suggest higher BA blood levels lower the risk of prostate cancer by reducing intracellular Ca2+ signals and storage.
Analysis on Marketing Ability and Financial Performance in Internet Company

This paper measures marketing ability and financial performance and examines what cause the overall operations inefficiency based on the operational process decomposition. The empirical research on e-portal and e-commerce company show that e-portal company has better marketing ability while e-commerce firm has better financial performance. For e-commerce company, it is marketing ability conversion inefficiency to lead to the overall operations inefficiency. In contrast, for e-portal company, lower financial performance conversion efficiency causes the overall operations inefficiency.
Cluster Profiles Of Achievement Goal Orientations And Well-Being Among Adolescents

Assessing students’ achievement goals and their wellbeing is important to improvise the academic settings and to facilitate the holistic development of the students. This study aimed 1) to identify the distinct cluster profiles of achievement goals and wellbeing, and 2) to find out any significant gender differences among the identified clusters profiles and the secondary school students. 404 students were selected for the study and the sample completed the Achievement Goal orientations inventory and Student wellbeing scale, both were developed and validated by the investigator. kmeans clustering analysis confirmed that three distinct clusters could segregate the students based on their goal orientations and wellbeing. The results showed that the three goal orientations were complimentary. The first cluster with 119 students had high performance approach and low wellbeing. This confirmed that the students demonstrating learning as a pursuit of competence have low wellbeing. The second cluster showed 129 students with mixed achievement goals and moderate wellbeing. The final clusters consisted of 156 students with mastery approach and high wellbeing. The chisquare analysis showed that there was significant difference among the cluster profiles across gender. The findings demonstrated that student disposition towards mastery goals confirmed better wellbeing than students displaying performance goals. © 2018 Published by Future Academy www.FutureAcademy. org.UK
Mixing Property and Functional Central Limit Theorems for a Sieve Bootstrap in Time Series

We study a bootstrap method for stationary real-valued time series, which is based on the method of sieves. We restrict ourselves to autoregressive sieve bootstraps. Given a sample X1; : : : ; Xn from a linear process fXtgt2ZZ, we approximate the underlying process by an autoregressive model with order p = p(n), where p(n)! 1; p(n) = o(n) as the sample size n! 1. Based on such a model a bootstrap process fX t gt2ZZ is constructed from which one can draw samples of any size. We give a novel result which says that with high probability, such a sieve bootstrap process fX t gt2ZZ satis es a new type of mixing condition. This implies that many results for stationary, mixing sequences carry over to the sieve bootstrap process. As an example we derive a functional central limit theorem under a bracketing condition.
Long term on-line speaker adaptation for large vocabulary dictation

Online speaker adaptation is desirable for speech recognition dictation applications, because it offers the possibility to improve the system with the speaker specific data obtained from the user. Since the user will work with such a device over a long period, for a dictation system, the long term adaptation performance is more important than the adaptation speed. In contrast to speaker dependent retraining, the speaker specific speech data does not need to be stored for online speaker adaptation and each adaptation step does not require a large computational effort. We describe our way of performing online Bayesian speaker adaptation using partial traceback. We compare supervised with unsupervised adaptation and speaker adaptation with speaker dependent training using the adaptation material. Compared to the speaker independent startup models, the error rate was divided by two after five hours of supervised adaptation in our experiments. In the long term experiments, supervised online adaptation performed similar to speaker dependent training using the adaptation material.
Online footstep planning for humanoid robots

We present an online algorithm for planning sequences of footstep locations that encode goal-directed navigation strategies for humanoid robots. Planning footsteps is more general than most existing navigation methods designed for wheeled robots, since the options of stepping over or upon obstacles in a cluttered terrain are available. Given a discrete set of plausible footstep locations, a forward dynamic programming approach is used to compute a footstep sequence to a specified goal location in the environment. Heuristics designed to minimize the number and complexity of the step motions are used to encode cost functions used for searching a footstep transition graph. If successful, the planner returns an optimal sequence of footstep locations according to the cost functions and plausible sets of footstep locations defined. We show results from an experimental implementation of the algorithm running on the H7 humanoid robot. Using a stereo vision system to sense obstacles in the immediate environment and identify a target goal location, the robot updates the current optimal footstep sequence to the goal from its present location.
A System Call-Centric Analysis and Stimulation Technique to Automatically Reconstruct Android Malware Behaviors

With more than 500 million of activations reported in Q3 2012, Android mobile devices are becoming ubiquitous and trends conﬁrm this is unlikely to slow down. App stores, such as Google Play, drive the entire economy of mobile applications. Unfortunately, high turnovers and access to sensitive data have soon attracted the interests of cyber-criminals too with malware now hitting Android devices at an alarmingly rising pace. In this paper we present Copper-Droid , an approach built on top of QEMU to automatically perform out-of-the-box dynamic behavioral analysis of Android malware. To this end, CopperDroid presents a uniﬁed analysis to characterize low-level OS-speciﬁc and high-level Android-speciﬁc behaviors. Based on the observation that such behaviors are however achieved through the invocation of system calls, CopperDroid ’s VM-based dynamic system call-centric analysis is able to faithfully describe the behavior of Android malware whether it is initiated from Java, JNI or native code execution. We carried out extensive experiments to assess the eﬀec-tiveness of our analyses on a large Android malware data set of more than 1,200 samples belonging to 49 Android malware families (provided by the Android Malware Genome Project) and about 400 samples over 13 families (collected from the Contagio project). Our experiments show that a proper malware stimulation strategy (e.g., sending SMS, placing calls) successfully discloses additional behaviors on a non-negligible portion of the analyzed malware samples.
Autonomic trust prediction for pervasive systems

In recent years, various trust management models based on the human notion of trust have been proposed to support trust-aware decision making in pervasive systems. However, the degree of subjectivity embedded in human trust often clashes with the requirements imposed by the target scenario: on one hand, pervasive computing calls for autonomic and light-weight systems that impose minimum burden on the user of the device (and on the device itself); on the other hand, computational models of human trust seem to demand a large amount of user input and physical resources. The result is often a computational trust model that does not 'compute': either the degree of subjectivity it offers is limited, or its complexity compromises its usability. In this paper, we present an accurate and efficient trust prediction model that is based on a basic Kalman filter. We discuss simulation results to demonstrate that the predictor is capable of capturing the natural disposition to trust of the user of the device, while being autonomic and light-weight.
A Compressed Sensing-Based Denoising Approach in Crude Oil Price Forecasting

Crude oil price forecasting has been a difficult challenge for years. To improve the forecasting performance, a novel forecasting method is proposed through combining compressed sensing based denoising (CSD) approach and least square support vector regression (LSSVR) forecasting model. In the forecasting model, the grid search algorithm is used to optimize the parameter of LSSVR. Compared with the wavelet denoising method, the compressed sensing based denoising method shows better performance when they are applied to forecasting models. The entire forecasting model CSD-LSSVR also shows its superiority in direction accuracy prediction which is of great significance in business decision making.
Penetrance Analysis of the PALB2 c.1592delT Founder Mutation

Purpose:PALB2 is a recently identified breast cancer susceptibility gene. We have previously identified in the Finnish population a PALB2 c.1592delT founder truncation mutation that is associated with an increased risk of breast cancer. In the present study, we wanted to assess in more detail the increased risk (hazard ratio, HR) and the age-specific cumulative risk (penetrance) of c.1592delT with regard to susceptibility to breast and other forms of cancer. Experimental Design: Modified segregation analyses fitted under maximum likelihood theory were used to estimate age-specific cumulative risks and HRs using the families of mutation carriers identified from a consecutive series of breast cancer cases unselected for age at onset or family history. Results: We found a substantially increased risk of breast cancer [HR, 6.1; 95% confidence interval (95% CI), 2.2-17.2; P = 0.01] equivalent to a 40% (95% CI, 17-77) breast cancer risk by age 70 years, comparable to that for carriers of mutations in BRCA2. We found marginal evidence (P = 0.06) that the HR for breast cancer decreased with age by 4.2% per year (95% CI, 0.2-8.1), from 7.5-fold at age 30 years to 2.0-fold at age 60 years. Conclusions: Our results suggest that it may be appropriate to offer PALB2 c.1592delT mutation testing to Finnish women with breast cancer, especially those with an early age at onset or a family history of breast or related cancers, and to offer carriers the option of participation in extended disease surveillance programs.
Central circuitry in the jellyfish Aglantha digitale IV. Pathways coordinating feeding behaviour

SUMMARY The hydromedusan jellyfish Aglantha digitale feeds on small planktonic organisms carried to the margin by tentacle flexions. During feeding, the manubrium bends across (`points') and seizes the prey with flared lips. In immobilized preparations, pointing to a source of electrical stimulation was accurate, 70% of the time, to within 15°. Cutting experiments showed that the conduction pathways concerned with pointing and lip flaring are located in eight radial strands consisting of a radial canal, a giant nerve axon and a bundle of small axons with FMRFamide-like immunoreactivity. Application of food juices to sites on the margin and tentacles evoked trains of impulses in the axon bundles (F events; conduction velocity 15.5±3.7 cm s-1) and in the epithelium lining the radial canals (E events; conduction velocity 28.5±3.5 cm s-1). Impulses were conducted circularly in the outer nerve ring (F events) or in the ring canal (E events). Unilateral flexions of the manubrium during pointing arise from preferential excitation of one or more of eight longitudinal `muscle bands' in the wall of the manubrium and peduncle. Lip flaring represents symmetrical contraction of all eight bands. Cutting experiments revealed that F events mediate pointing; E events mediate lip flaring. Thus the endodermal radial canals, which in other hydromedusae mediate protective `crumpling', provide the conduction pathway for manubrial lip flaring. Aglantha's alternative protective response – escape swimming – makes crumpling unnecessary, releasing the pathway for use in feeding. Trains of E events, generated in the manubrium during ingestion, propagate to the margin and inhibit rhythmic (slow) swimming with a duration that depended on their number and frequency. Inhibition of swimming appeared to facilitate transfer of food from the margin to the mouth, but how it comes about is unclear.
Flexible Holonic Organization Modeling and Cultural Evolution

Flexible organization is an inevitable choice of the enterprises to response to changes under dynamic environment. This paper proposes a holon (holonic organization) recursive model combining characteristics of holon with rules of the organization, and discusses the holonic structure and the formation process of holonic organization. This paper designs cultural evolution model of holonic organization applying cultural algorithm, which constructs holon population space and holon belief space to realize the organizational goal and organization self-evolution. Holonic organization modeling based on cultural algorithm breaks the traditional framework for the organization adaptation, and supports self-adaptation and self-evolution of organization. Finally, flexible holonic virtual enterprise model applying the above ideas is designed, which shows the modeling method for the organization to establish an effective response mechanism is effective and feasible under dynamic environment.
OmniVib: Towards Cross-body Spatiotemporal Vibrotactile Notifications for Mobile Phones

Previous works illustrate that one's palm can reliably recognize 10 or more spatiotemporal vibrotactile patterns. However, recognition of the same patterns on other body parts is unknown. In this paper, we investigate how users perceive spatiotemporal vibrotactile patterns on the arm, palm, thigh, and waist. Results of the first two experiments indicate that precise recognition of either position or orientation is difficult across multiple body parts. Nonetheless, users were able to distinguish whether two vibration pulses were from the same location when played in quick succession. Based on this finding, we designed eight spatiotemporal vibrotactile patterns and evaluated them in two additional experiments. The results demonstrate that these patterns can be reliably recognized (>80%) across the four tested body parts, both in the lab and in a more realistic context.
Enhanced MMSE channel estimation using timing error statistics for wireless OFDM systems

Estimation and tracking of the frequency-selective time-varying channel response is a challenging task for wireless communication systems incorporating coherent OFDM. In pilot-symbol-assisted (PSA) OFDM systems, the minimum mean-square-error (MMSE) estimator provides the optimum performance based on the channel statistics (channel correlation function and SNR). In OFDM systems, FFT-block timing error introduces a linear phase rotation to data modulated on individual subcarriers. An MMSE channel estimator designed only using the wireless channel statistics performs only sub-optimally when subcarrier phase rotations due to block timing errors are present. In this paper, we show that by using the block timing error statistics of the OFDM time-synchronizer the performance of the MMSE channel estimation can be significantly improved. Numerical results show that the bit-error-probability (BEP) performance degradation due to timing errors can be almost completely recovered by the proposed technique.
Improved Static Analysis of Parameterised Boolean Equation Systems using Control Flow Reconstruction

We present a sound static analysis technique for fighting the combinatorial explosion of parameterised Boolean equation systems (PBESs). These essentially are systems of mutually recursive fixed point equations ranging over first-order logic formulae. Our method detects parameters that are not live by analysing a control flow graph of a PBES, and it subsequently eliminates such parameters. We show that a naive approach to constructing a control flow graph, needed for the analysis, may suffer from an exponential blow-up, and we define an approximate analysis that avoids this problem. The effectiveness of our techniques is evaluated using a number of case studies.
A Learning Algorithm for Self-Organizing Maps Based on a Low-Pass Filter Scheme

In this work a novel training algorithm is proposed for the formation of topology preserving maps. In the proposed algorithm the weights are updated incrementally by using a higher-order difference equation, which implements a low pass digital filter. It is shown that by suitably choosing the filter the learning process can adaptively follow a specific dynamic.