Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of 1-Week Yoga-Based Residential Program on Cardiovascular Variables of Hypertensive Patients: A Comparative Study

Introduction: Hypertension (HTN) is an important public health concern and a leading cause of morbidity and mortality worldwide. Yoga is a form of mind–body medicine shown to be effective in controlling blood pressure (BP) and reduces cardiac risk factors in HTN. Integrated approach of Yoga therapy (IAYT) is a residential yoga-based lifestyle intervention proven to be beneficial in several health conditions. Aim: To study the efficacy of 1 week of residential IAYT intervention on cardiovascular parameters in hypertensive patients. Methodology: Twenty hypertensive individuals (7 females) within age range between 30 and 60 years (average; 46.62 ± 9.9 years), who underwent 1 week of IAYT treatment for HTN, were compared with age- gender-matched non-IAYT group (5 females; average age; 47.08 ± 9.69 years) in terms of systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), cardiac output (CO), stroke volume (SV), baroreflex sensitivity (BRS), and total peripheral vascular resistance (TPVR), IAYT program consisted of sessions of asanas, breathing practices, meditation and relaxation techniques, low salt, low-calorie diet, devotional session, and counseling. Individuals in non-IAYT group followed their normal routine. All the variables were assessed before and after one week. Data were analyzed using SPSS version 16. RM-ANOVA was applied to assess within group and between group changes after intervention. Results: There was a significant improvement in SBP (P = 0.004), DBP (P = 0.008), MAP (0.03), BRS (P < 0.001), and TPVR (P = 0.007) in IAYT, group whereas in control group, we did not find significant difference in any of the variables. Between-group comparison showed a significant improvement in SBP (P = 0.038), BRS (P = 0.034), and TPVR (P = 0.015) in IAYT group as compared to non-IAYT group. Conclusion: One-week IAYT intervention showed an improvement in baroreflex sensitivity, systolic BP, and total peripheral vascular resistance in hypertensive patients. However, further randomized control trials need to be performed to confirm the present findings.
Biometrie identification using photoplethysmography signal

Due to the increase in the importance of the electronic data nowadays, finding a way to protect this data from hacking became a must. In this paper, the Photoplethysmographic signal known as the PPG signal is used as a biometric technique due to its several advantages, most importantly its unique form that differs between individuals. The PPG signal is taken using the Spo2 sensor which is a non-invasive method used for measuring the oxygen saturation from either the finger or the ear using 2 light emitting diodes (LEDs); using this PPG signal and its 2 derivatives 40 features depending on the signals' dimensions were extracted by Matlab, and then the K-nearest neighbor classifier was applied after tuning 2 parameters (the constant k and the distance metric) to check the efficiency of this method. The proposed algorithm was tested on dataset having signals previously processed. In future work, this technique is going to be implemented to unlock computers instead of using a password.
Private Values of Risk Tradeoffs at Superfund Sites: Housing Market Evidence on Learning about Risk

This paper incorporates a Bayesian learning model into a hedonic framework to estimate the value that residents place on avoiding cancer risks from hazardous-waste sites. We show that residents are willing to pay to avoid cancer risks from Superfund sites before the U.S. Environmental Protection Agency (EPA) releases its assessment (known as the Remedial Investigation) of the site. Residents' willingness to pay to avoid risks actually decreases after the release of the Remedial Investigation, suggesting that the information lowers the perceived levels of risk. This estimated willingness to pay implies a statistical value of cancer similar to the value-of-life estimates in labor market studies.
Deep-space Optical Communication System (DOCS) for ESA's Space Weather mission to Lagrange orbit L5

ESA plans to embark the Deep-space Optical Communications System (DOCS) on the Space WEather (SWE) mission to the Sun-Earth Lagrange point L5 in the frame of its Space Situational Awareness (SSA) program. DOCS is an in-orbit technology demonstration that also serves a scientific objective, namely the transfer of high-resolution solar imagery. The characteristics of the SWE L5 mission provide substantial advantages for deep-space optical communication, because the equidistant triangular orbital geometry between the sun, the Earth, and L5 (all three distances are equal to 1 AU ≈ 150 million km) ensures that the sun is always separated by 60 degrees from both the space and the ground terminal. This allows for very efficient solar stray-light shielding and thermal management. It also reduces the pointing requirements of the DOCS Space Terminal (DST); a coarse pointing mechanism is not required, nor is a point-ahead assembly. The SSA SWE L5 mission Phase A, Phase B1 and B2 studies to be conducted 2017–2019 will take into account the accommodation and interfacing of the DST. DOCS aims to demonstrate a data rate of 10 Mbps over 1 AU to a 4-meter optical ground station. The development of the DST will follow a similar approach as foreseen for the Asteroid Impact Mission (AIM), namely to increase the technology readiness level (TRL) of existing developments wherever possible, or reasonable. The DST will be located on a side panel of the S/C, and will constantly face the Earth. Residual attitude movements (pointing errors) of the S/C will be covered by the field-of view of the DST telescope (an aperture diameter of 200 mm is envisaged) and by a fine pointing assembly. ESA is also embarking on a study to develop a 4-meter class DOCS ground terminal (DGT). Furthermore, the Consultative Committee for Space Data Systems (CCSDS) is currently developing a standard (“Blue Book”) for “High Photon Efficiency Optical Communications”. DOCS will serve as one of two implementations of the standard, which are required for it to become applicable.
On Accelerating the Regularized Alternating Least Square Algorithm for Tensors

In this paper, we discuss the acceleration of the regularized alternating least square (RALS) algorithm for tensor approximation. We propose a fast iterative method using a Aitken-Stefensen like updates for the regularized algorithm. Through numerical experiments, the fast algorithm demonstrate a faster convergence rate for the accelerated version in comparison to both the standard and regularized alternating least squares algorithms. In addition, we analyze the global convergence based on the Kurdyka- Lojasiewicz inequality as well as show that the RALS algorithm has a linear local convergence rate.
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naïve Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.
Resource constrained cellular neural networks for real-time obstacle detection using FPGAs

Due to the fast growing industry of smart cars and autonomous driving, advanced driver assistance systems (ADAS) with its applications have attracted a lot of attention. As a crucial part of ADAS, obstacle detection has been challenge due to the real-tme and resource-constraint requirements. Cellular neural network (CeNN) has been popular for obstacle detection, however suffers from high computation complexity. In this paper we propose a compressed CeNN framework for real-time ADAS obstacle detection in embedded FPGAs. Particularly, parameter quantizaion is adopted. Parameter quantization quantizes the numbers in CeNN templates to powers of two, so that complex and expensive multiplications can be converted to simple and cheap shift operations, which only require a minimum number of registers and LEs. Experimental results on FPGAs show that our approach can significantly improve the resource utilization, and as a direct consequence a speedup up to 7.8x can be achieved with no performance loss compared with the state-of-the-art implementations.
Maladaptive Habitat Use of a North American Woodpecker in Population Decline

Rapid anthropogenic habitat changes can lead to non-ideal habitat use by animals, often resulting in lower fitness and population declines. An extreme case of use and fitness mismatch is an ecological trap where habitat quality cues are disjointed from the true quality of the habitat. Species primarily associated with anthropogenically altered habitat, such as red-headed woodpeckers (Melanerpes erythrocephalus), may be especially vulnerable to use and fitness mismatch as they encounter novel environmental challenges. We investigated multi-scale habitat use and nesting success of red-headed woodpeckers to assess their vulnerability to mismatches between use and fitness as a result of non-ideal habitat use across multiple scales. We found that habitat characteristics that promote feeding potential such as canopy openness and greater dead limb length appeared paramount and were consistent in use across several spatial scales although reproductive fitness suffered. This contrasts with the assumption that habitat use by nesting birds should instead favor predation avoidance at smaller scales to improve reproductive fitness and suggests that maladaptive, foodbased habitat use by red-headed woodpeckers in southern Ontario may result in ecological traps for the species. Whether due to poor habitat choices or costly ones in favor of feeding potential, it is vital to consider this behavior in conservation and management plans for this and similar species. We suggest multi-scale habitat use studies that consider fitness outcomes are critical for species-at-risk in human-modified landscapes.
Family reunification – who, when, and how? Family trajectories among migrants in Italy

Background: Family reunification of migrants in their destination country is often the result of a process that includes one or more steps and combines family and migration events. However, the lack of relevant data often limits the possibility of studying all these stages. Objective: The paper aims to identify family migration models by means of the entire family migration trajectory, highlighting the relationship of these models with the migration project and with the family, cultural, and gender norms of the country of origin. Methods: We used sequence analysis on a large sample of resident families with at least one foreign-born member in Italy, using data collected by the Italian Institute of Statistics in 2011–2012. We focused exclusively on migrants from less developed countries and the countries of Central and Eastern Europe. Results: Our results indicate that family characteristics and stability in the host country are not always relevant for family reunification. Family, cultural, and gender norms of the home country and the nature of the migration project itself determine the family migration model. When the migration project becomes orientated, or converges over time to settlement, family reunification is pursued regardless of the possible difficulties and conditions during emigration. Conversely, during the time that migrants are maintaining a temporary project, family reunification is not even considered. Contribution: Adopting a longitudinal approach, we generalised and extended previous results, considering migrants from additional countries and studying the reunification of all family members. We compared family migration models and their association with gender, cultural, and family norms of the country of origin and the family migration project.
28 GHz Doherty Power Amplifier in CMOS SOI With 28% Back-Off PAE

A single-stage, symmetric Doherty power amplifier (PA) in 45 nm CMOS silicon on insulator at 28 GHz is presented. The PA achieves a saturated output power of 22.4 dBm, a peak power added efficiency (PAE) of 40%, and a 6 dB back-off PAE of 28%. High efficiency is attained due to low combiner losses of 0.5 dB, obtained using a recently developed combiner synthesis technique. A compact modeling approach for parasitic-extracted PA transistors is presented, which considerably reduced simulation time. The PA is based on two-stack power devices and occupies overall chip area of only 0.63 mm2, including pads.
Net Asset Value Discounts in Listed Private Equity Funds

This paper investigates determinants and consequences of net asset value discounts in listed private equity funds. Listed private equity funds share characteristics of closed-end mutual funds and traditional unlisted private equity funds and can therefore offer insights into both. Our results have particular relevance to the pricing of unlisted private equity funds where no market prices are observable. We find that funds start at an initial premium of -2.5 % and adapt to the long-term average of -21 % after two years. Fund returns display a U-shaped seasonality, which is related to publishing dates of annual reports. Stock performance is exceptionally weak in buyout funds after their initial public offering. Premia predict future stock returns and are explained by liquidity and by investor sentiment, but not by the fund's investment degree. A decrease in premia over the first few quarters after the fund's IPO remains unexplained, which partially supports the management ability hypothesis. Private equity fund premia depend on credit markets and systematic risk. This relation suggests that some information about the fund's portfolio is not reflected in net asset values, which seem to proxy for future fund cash flows.
Diversifying in public real estate: The ex-post performance

We calculate the ex-post, realised portfolio performance for an investor who diversifies among US stocks, bonds, real estate indirect investment vehicles (E-REITS), and cash. Simulations are performed for two alternative asset allocation frameworks — classical and Bayesian — and for scenarios involving two different samples and six different investment horizons. Interestingly, the ex-post welfare cost of restricting portfolio choice to traditional financial assets (ie, stocks, bonds, and cash) is only found to be positive in all scenarios for a Bayesian investor. On the contrary, substitution of E-REITS for stocks in optimal portfolios turns out to reduce ex-post portfolio performance over the nineties and for a Classical investor who ignores parameter estimation uncertainty.
From echolocation clicks to animal density--acoustic sampling of harbor porpoises with static dataloggers.

Monitoring abundance and population trends of small odontocetes is notoriously difficult and labor intensive. There is a need to develop alternative methods to the traditional visual line transect surveys, especially for low density areas. Here, the prospect of obtaining robust density estimates for porpoises by passive acoustic monitoring (PAM) is demonstrated by combining rigorous application of methods adapted from distance sampling to PAM. Acoustic dataloggers (T-PODs) were deployed in an area where harbor porpoises concurrently were tracked visually. Probability of detection was estimated in a mark-recapture approach, where a visual sighting constituted a "mark" and a simultaneous acoustic detection a "recapture. " As a distance could be assigned to each visual observation, a detection function was estimated. Effective detection radius of T-PODs ranged from 22 to 104 m depending on T-POD type, T-POD sensitivity, train classification settings, and snapshot duration. The T-POD density estimates corresponded to the visual densities derived concurrently for the same period. With more dataloggers, located according to a systematic design, density estimates would be obtainable for a larger area. This provides a method suitable for monitoring in areas with densities too low for visual surveys to be practically feasible, e.g., the endangered harbor porpoise population in the Baltic.
Cognitive changes following multiple-modality exercise and mind-motor training in older adults with subjective cognitive complaints: The M4 study

Background We investigated the effects of multiple-modality exercise with additional mind-motor training on cognition in community-dwelling older adults with subjective cognitive complaints. Methods Participants (n = 127, mean age 67.5 [7.3] years, 71% women) were randomized to receive 45 minutes of multiple-modality exercise with additional 15 minutes of either mind-motor training (M4, n = 63) or control (balance, range of motion and breathing exercises [M2, n = 64]). In total, both groups exercised 60 minutes/day, 3 days/week, for 24 weeks. Standardized global cognitive functioning (GCF), concentration, reasoning, planning, and memory were assessed at 24 weeks and after a 28-week no-contact follow-up. Results There were no significant differences in the study primary outcomes. The M4 group, however, showed trends for greater improvements in GCF and memory (both, P = .07) compared to the M2 group at 24 weeks. Significant differences between group in GCF (P = .03) and memory (P = .02) were observed after the 28-week no-contact follow-up favouring the M4 group. Discussion Additional mind-motor training did not impart immediate greater benefits to cognition among the study participants.
MashChord: A Structured Peer-to-Peer Architecture for Mashups Based on Chord

—Mashups are key category of Web 2.0 applications. Due to their personalization property, mashup platforms suffer from scalability and efﬁciency issues. Most mashup platforms are based on either centralized or loosely distributed architectures and that causes several deﬁciencies when searching for mashups. This paper presents MashChord–a structured peer-to-peer (P2P) architecture for mashups based on Chord. Naturally, Chord supports exact matching search queries. We show how we customize this to provide partial matching search queries in MashChord. MashChord architecture increases the efﬁciency of searching for mashups. We utilize this structured architecture to incorporate several features into MashChord that further improve the functionality of our platform such as ofﬂoading mashup execution to peers.
On the Reduction of Computational Complexity of Deep Convolutional Neural Networks †

Deep convolutional neural networks (ConvNets), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D) convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.
Ambient temperature shapes reproductive output during pregnancy and lactation in the common vole (Microtus arvalis): a test of the heat dissipation limit theory

SUMMARY The heat dissipation limit theory suggests that heat generated during metabolism limits energy intake and, thus, reproductive output. Experiments in laboratory strains of mice and rats, and also domestic livestock generally support this theory. Selection for many generations in the laboratory and in livestock has increased litter size or productivity in these animals. To test the wider validity of the heat dissipation limit theory, we studied common voles (Microtus arvalis), which have small litter sizes by comparison with mice and rats, and regular addition of wild-caught individuals of this species to our laboratory colony ensures a natural genetic background. A crossover design of ambient temperatures (21 and 30°C) during pregnancy and lactation was used. High ambient temperature during lactation decreased milk production, slowing pup growth. The effect on pup growth was amplified when ambient temperature was also high during pregnancy. Shaving fur off dams at 30°C resulted in faster growth of pups; however, no significant increase in food intake and or milk production was detected. With increasing litter size (natural and enlarged), asymptotic food intake during lactation levelled off in the largest litters at both 21 and 30°C. Interestingly, the effects of lactation temperature on pup growth where also observed at smaller litter sizes. This suggests that vole dams trade-off costs associated with hyperthermia during lactation with the yield from investment in pup growth. Moreover, pup survival was higher at 30°C, despite lower growth, probably owing to thermoregulatory benefits. It remains to be seen how the balance is established between the negative effect of high ambient temperature on maternal milk production and pup growth (and/or future reproduction of the dam) and the positive effect of high temperatures on pup survival. This balance ultimately determines the effect of different ambient temperatures on reproductive success.