Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception of Pulmonary Function in Children with Asthma and Cystic Fibrosis.

Background: Under-perception of pulmonary dysfunction may delay appropriate treatment, while over-perception may result in unnecessary treatments. Objectives: To evaluate the ability of patients with asthma or cystic fibrosis and their subspecialty caregivers to assess changes in lung function based on their subjective clinical impressions. Methods: Patients were asked to qualitatively describe how they felt compared to their prior visit (same/better/worse) and to quantitatively estimate their forced expiratory volume in 1 s (FEV1) after being reminded of their FEV1 at the prior visit. Providers made similar estimates based on history and physical examination and knowledge of prior FEV1. After adjusting for relevant clinical covariates, lung function estimates were categorized as accurate (±5% of measured FEV1), overestimated (>5% above measured), and underestimated (>5% below measured). Results: One hundred nine patients estimated FEV1 on 179 occasions. Concordance between patient qualitative assessment and FEV1-based categories was low (κ = 0.08); 44% of patients reported feeling better than the FEV1-based category showed. Quantitatively, 56% of patient estimates were accurate, 18% were underestimated, and 26% overestimated; accuracy improved with age (odds ratio = 1.16, P = 0.01). Concordance between provider qualitative assessments and FEV1-based category was moderate (κ = 0.35); about 19% said their patient looked better than the FEV1-based category showed. Quantitatively, 65% of provider estimates were accurate, 16% were underestimated, and 19% were overestimated; accuracy improved with years of experience. Conclusions: Patients' and providers' perceptions of lung function were low to moderately accurate. Relying on subjective impression may place patients at risk for unnecessary treatments or increased morbidity. These findings highlight the importance of objective lung function assessment.
Preliminary Study of Water Quality Monitoring Based on WSN Technology

Water plays an important role in human daily life such as drinks of human, farming, livestock breeding and also manufacturing. However, urbanization modernization and industrialization had cause pollution to the clean water source for the usage agriculture. Therefore, it is vital to have a water quality monitoring tool which provides real-time water quality data and cover large area. Although the research of water quality monitoring system is widely done and applied, the current system is still expensive, small coverage and not user friendly. Moreover, most of the WQMS developed is not suitable for agriculture purpose due to the usage of unsuitable sensors. Thus, an affordable, large coverage and user friendly wireless water quality monitoring system with suitable sensors is proposed to assist the authority effectively in maintaining the health of the natural water source around the area. Here, a low-cost, large coverage and user friendly water quality monitoring system with multi sensor which is based on Wi-Fi Technology is presented. The process of designing the system involves three main parts which are hardware development, software development and also wireless network process. For hardware development part, different types of sensor will be employed and the sensors will be integrated with microcontroller. As for the software development, a few programming software is integrated for data acquisition, processing and displaying. Finally, for the wireless network process, wireless network module is used to for data transmitting and receiving while website is used to display the obtained data.
Secure Image Steganography Using Dual-Tree Complex Wavelet Transform Block Matching

Along with the thriving advancements in internet facility and related technology domain, information security becomes a key factor in the modern communication systems. Hackers have been trying to steal sensitive information and the data storing and communication becomes too risky via unsecured channels. Many techniques are used for safe and secure data communication and Steganography is one of the prominent ones. Hiding information among images is one of the popular and is known as Image Steganography. The current paper proposes a novel steganographic approach based on secret data hiding over DT-CWT subbands of the cover image. The high and low fidelity information in the secret data is separated and embedded in corresponding high and low frequency DT -CWT coefficients over best matched locations. The smooth and high-fidelity planes of the secret image get divided into small patches and intensity mapping is employed before finding the most suitable position in the subbands. Security keys were generated based on the embedded location and these keys are needed for decoding the embedded secret data at the receiver side. The efficiency and performance of various data samples at different resolutions are evaluated against the state of the art approaches using benchmarks and the results show better performance.
Disambiguating Music Artists at Scale with Audio Metric Learning

We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn artist embeddings directly from audio, and using a dedicated homonym artists dataset, we compare our method with a recent approach that learn similar embeddings using artist classifiers. While both systems have the ability to disambiguate unknown artists relying exclusively on audio, we show that our system is more suitable in the case when enough audio data is available for each artist in the train dataset. We also propose a new negative sampling method for metric learning that takes advantage of side information such as music genre during the learning phase and shows promising results for the artist clustering task.
Influence of Dosing Interval and Administration on the Bone Metabolism, Skeletal Effects, and Clinical Efficacy of Parathyroid Hormone in Treating Osteoporosis: A Narrative Review

Recombinant human parathyroid hormone (PTH) is the key anabolic agent used for preventing fracture in postmenopausal women with osteoporosis. In bone metabolism, PTH signaling is mediated through a G protein–coupled receptor that affects various post‐receptor signaling pathways. Results of preclinical and clinical studies have shown that PTH improves both the structure and strength of bone tissue. Once daily subcutaneous injection of the PTH fragment, teriparatide (PTH [1‐34]), is the most commonly recommended formulation and dosing strategy in clinical practice. However, other dosing intervals, formulations, and routes have been investigated in preclinical and clinical studies. In particular, once‐weekly and cyclical administration have been investigated mainly as a means of reducing the high direct costs of treatment. In preclinical studies, bone formation/resorption markers, bone mineral density measurements, and histomorphometric parameters improved with both once‐daily and once‐weekly administration. However, the magnitude and duration of such improvements were generally greater with once‐daily PTH administration. In clinical studies, reductions in fracture incidence were also noted with both once‐daily and once‐weekly PTH administration, although improvements in nonvertebral fractures are less evident with once‐weekly administration. This narrative review details the differences between PTH formulation and dosing strategies in relation to preclinical and clinical efficacy/safety parameters, although it should be stressed that no head‐to‐head studies allow direct comparisons. This review also seeks to outline practical considerations involved with PTH prescribing and new directions in research regarding routes of administration. © 2017 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.
Cross-Lagged Relation of Leisure Activity Participation to Trail Making Test Performance 6 Years Later: Differential Patterns in Old Age and Very Old Age

Objective: We investigated cross-lagged relations between leisure activity participation and Trail Making Test (TMT) performance over 6 years and whether those reciprocal associations differed between individuals. Method: We analyzed data from 232 participants tested on performance in TMT Parts A and B as well as interviewed on leisure activity participation in 2 waves 6 years apart. Mean age in the Wave 1 was 73.42 years. Participants were also tested on vocabulary (Mill Hill scale) as a proxy indicator of crystallized intelligence and reported information on early and midlife cognitive reserve markers (education and occupation). Latent cross-lagged models were applied to investigate potential reciprocal activity−TMT relationships. Results: The relation of leisure activity participation predicting TMT performance 6 years later was significantly larger than was the relation of TMT performance predicting later leisure activity participation. Statistically comparing different moderator groups revealed that this pattern was evident both in individuals with low education and in those with high education but, notably, emerged in only young-old adults (but not in old-old adults), in individuals with a low cognitive level of job in midlife (but not in those with a high cognitive level of job in midlife), and in individuals with high scores in vocabulary (but not in those with low scores in vocabulary). Conclusions: Late-life leisure activity participation may predict later cognitive status in terms of TMT performance, but individuals may markedly differ with respect to such effects. Implications for current cognitive reserve and neuropsychological aging research are discussed.
Enhanced image approximation using shifted rank-1 reconstruction

Low rank approximation has been extensively studied in the past. It is most suitable to reproduce rectangular like structures in the data. In this work we introduce a generalization using shifted rank-1 matrices to approximate $A\in\mathbb{C}^{M\times N}$. These matrices are of the form $S_{\lambda}(uv^*)$ where $u\in\mathbb{C}^M$, $v\in\mathbb{C}^N$ and $\lambda\in\mathbb{Z}^N$. The operator $S_{\lambda}$ circularly shifts the k-th column of $uv^*$ by $\lambda_k$. These kind of shifts naturally appear in applications, where an object $u$ is observed in $N$ measurements at different positions indicated by the shift $\lambda$. The vector $v$ gives the observation intensity. Exemplary, a seismic wave can be recorded at $N$ sensors with different time of arrival $\lambda$; Or a car moves through a video changing its position in every frame. We present theoretical results as well as an efficient algorithm to calculate a shifted rank-1 approximation in $O(NM \log M)$. The benefit of the proposed method is demonstrated in numerical experiments. A comparison to other sparse approximation methods is given. Finally, we illustrate the utility of the extracted parameters for direct information extraction in several applications including video processing or non-destructive testing.
Joint Geometric Unsupervised Learning and Truthful Auction for Local Energy Market

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful.
Molecular and Immunohistochemical Markers with Prognostic and Predictive Significance in Liver Metastases from Colorectal Carcinoma

Despite the significant recent achievements in the diagnosis and treatment of colorectal cancer (CRC), the prognosis of these patients has currently plateaued. During the past few years, the opportunity to consider multiple treatment modalities (including surgery and other locoregional treatments, systemic therapy, and targeted therapy) led to the research of novel prognostic and predictive biomarkers in CRC liver metastases (CRCLM) patients. In this review, we seek to describe the current state of knowledge of CRCLM biomarkers and to outline impending clinical perspectives, in particular focusing on the cutting-edge tools available for their characterization.
Robust EEG-based cross-site and cross-protocol classification of states of consciousness

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
Analysis of Stabilograms of Healthy Human Using Time and Frequency Characteristics

Static computer posturography method was used in this work together with stability posture tests to evaluate person's ability to maintain balance. Stabilogram, the curve reflecting the change of person's Center of Pressure (COP) location during balance maintenance, which results in three variables that depend on time - COP point ($\boldsymbol{x}, \boldsymbol{y}$ coordinates) and force applied to that point, was measured during six tasks. For each next task, part of a biological systems that participate in maintaining balance was excluded (visual system first, then proprioception system, then both). COP parameters were obtained in two domains: time (mean and standard deviation of normalized COP coordinates in sagittal and frontal plane, mean and standard deviation of module of normalized COP speed in sagittal and frontal plane, mean and standard deviation of module of normalized COP acceleration in sagittal and frontal plane) and frequency (spectral density power ratio in four frequency ranges: [0; 0.5], [0.5; 1], [1; 1.5], [1.5; 4] Hz for the frontal and sagittal planes). It was found that decrease of posture stability leads to increase of the frequency and amplitude of the COP fluctuations and spectral power is redistributed from low to the higher frequencies. Also, frequency and amplitude of the COP projection in the frontal plane is growing faster than in the sagittal, so instability in the frontal plane is larger due to less contact area of the foot with the supporting surface.
Adaptive Damping With Cross Derivative Controller For Stability In AC -Microgrid

Stability of droop controlled microgrid is largely depends upon the value of droop coefficients and decoupling of active and reactive powers due to transmission line R/X ratio. A high value of droop coefficient causes low frequency power oscillations between sources. These power oscillations may remain for long duration causing huge power losses in inverters or may increase with time. The paper presents a novel method to damp out these power oscillations in microgrid and stabilize the grid with higher droop coefficients. The technique has been verified in MATLAB/Simulink.
Feminizing Wolbachia endosymbiont disrupts maternal sex chromosome inheritance in a butterfly species

Genomes are vulnerable to selfish genetic elements that enhance their own transmission often at the expense of host fitness. Examples are cytoplasmic elements such as maternally inherited bacteria that cause feminization, male-killing, parthenogenesis and cytoplasmic incompatibility. We demonstrate, for the first time, that segregation distortion, a phenomenon so far seen only for nuclear genetic elements, can also be caused by a cytoplasmic element, the ubiquitous endosymbiotic bacterium Wolbachia. For Eurema mandarina butterfly lineages with a Z0 sex chromosome constitution, we provide direct and conclusive evidence that Wolbachia induces production of all-female progeny by a dual role: the compensation for the female-determining function that is absent in Z0 lineages (feminization) and the prevention of maternal sex chromosome inheritance to offspring as a specific type of segregation distortion. Therefore, our findings highlight that both sex determination and chromosome inheritance — crucially important developmental processes of higher eukaryotes — can be manipulated by cytoplasmic parasites.
Efficient Software-Based Partitioning for Commercial-off-the-Shelf NoC-based MPSoCs for Mixed-Criticality Systems

Some industrial domains, characterized by particularly strict safety standards – e.g., avionics – are facing issues when addressing the usage of commercial-off-the-shelf multi-processor system-on-chips and in particular new network-on-chip-based architectures. One key issue is related to the usage of such system-on-chips to implement mixed-criticality systems with the main goal of reducing size, weight, and power consumption of on-board equipment by reducing the number of computers moving from federated architectures based on singlecore processors to a single multi-core processor. To comply with relevant safety standards, a mixed-criticality system should be proven to enforce isolation among safety-critical and non-safetycritical tasks running on the multi-core hardware platform. This paper presents a software-level methodology tackling such issue. The proposed methodology exploits knowledge of the deterministic routing algorithm used by the network-on-chip to implement a safe and efficient partitioning of the system. This paper presents the software implementation and an experimental evaluation of the solution, proving its suitability for integration in an avionic application.
A Survey on Image Fusion Techniques and Performance Metrics

Image fusion is the process of merging two or more relevant information into one image. The resulted image will have more explanatory than original images. Multispectral image (MS) is obtained from satellite, multispectral image having rich spectral information and low spatial resolution. MS have less information which is not suitable for remote sensor application. Panchromatic (PAN) image is one of the types of satellite images. PAN images have more spectral information but low spatial information. In remote sensing application more spatial and spectral information is required, so merging MS and PAN will result in rich spatial and spectral image. Many fusion algorithms are supported to fuse MS and PAN. Some techniques are principal component analysis, discrete wavelet transform, pixel-level image fusion and multisensor image fusion. Qualitative analysis determines the performance of fused image by comparison between original image and resulted fused image. Some qualitative metrics are evaluated using Root mean square error (RMSE), Relative global dimensional synthesis error (ERGAS), Quality factor (Q4), Cross correlation (CC) and Spectral angle mapper (SAM). This paper reviews about various fusion techniques in remote sensor and quality metrics.
Information-Flow Control with Fading Labels

Information-flow control mechanisms generally invest the same amount of resources to protect information of varying importance. In this paper, we introduce the concept of fading labels. Fading labels are security labels that stop propagating their taint after a fixed amount of uses. Their use allows mechanisms to spend more resources on more important information.
Toward Preventive Network Service Management by Neural Networks

In this paper, we’ve explored the possibility of expecting/avoiding degradation of network service performance of Network function virtualization (NFV) by long-term monitoring of hardware and network performances, and adjusting resource assignments of VNFs by preventive management based on Neural Networks (NNs). We’ve constructed an experimental network service, periodically inserted the specific amount of stresses to the hardware and networks systems, and let the NN learn the long-term trends of service performance changes, then try to adjust VNF resource assignment based on NN’s expectations. We evaluate and discuss the results of our experiments.