Miracle-dz/UniFAR-FTUs huggingface dataset:
OFPT: OpenFlow based Parallel Transport in Datacenters

Although the dense interconnection datacenter networks (DCNs) (e.g. FatTree) provide multiple paths and high bisection bandwidth for each server pair, the single-path TCP (SPT) and ECMP which are widely used currently neither achieve high bandwidth utilization nor have good load balancing. Due to only one available transmission path, SPT cannot make full use of all available bandwidth, while ECMP’s random hashing results in many collisions. In this paper, we present OFPT, an OpenFlow based Parallel Transport framework, which integrates precise routing and scheduling for better load balancing and higher network throughput. By adopting OpenFlow based centralized control mechanism, OFPT computes the optimal path and bandwidth provision for each flow according to the global network view. To guarantee high throughput, OFPT dynamically schedules flows with Seamless Flow Migration Mechanism (SFMM), which can avoid packet loss in flow rerouting. Finally, we test OFPT on Mininet and implement it in a real testbed. The experimental results show that the average network throughput in OFPT is up to 97.5% of bisection bandwidth, which is higher than ECMP by 36%. Besides, OFPT decreases the average flow completion time (AFCT) and achieves better scalability.
Combining Rule-Based and Statistical Syntactic Analyzers

This paper presents the results of a set of preliminary experiments combining two knowledge-based partial dependency analyzers with two statistical parsers, applied to the Basque Dependency Treebank. The general idea will be to apply a stacked scheme where the output of the rule-based partial parsers will be given as input to MaltParser and MST, two state of the art statistical parsers. The results show a modest improvement over the baseline, although they also present interesting lines for further research.
Evolving regression trees robust to missing data

Data quality is a major concern in several fields of knowledge that rely on data analysis. Missing data, in particular, have a strong negative impact in machine learning, potentially harming the knowledge extraction process by skewing results and affecting the predictive performance of the induced models. For dealing with the problem of missing data, the literature in machine learning offers a variety of strategies which can be either in the form of a preprocessing step or of an embedded solution within a predictive method. In this paper, we propose a novel evolutionary algorithm for regression tree induction, which has embedded in its evolutionary cycle a robust framework for dealing with missing data. For comparison purposes, we evaluate six traditional regression algorithms over 10 public regression datasets that were artificially modified to present different levels of missing data. Results from the experimental analysis show that the proposed approach is the one that is less affected by the increasing levels of missing data, presenting an interesting trade-off between model interpretability and predictive performance especially for datasets with more than 40% of missing data.
Characterization and Spark Plasma Sintering of Mechanically Milled Aluminum-Carbon Nanotube (CNT) Composite Powders

Carbon nanotube (CNT) reinforced metallic composites have been recently gaining a significant amount of scientific interest. The promise of transmitting part of the remarkable and outstanding properties of CNTs to metals has spurred efforts to design and manufacture metallic composites reinforced with CNTs. Out of the metallic matrices investigated, Al has received significant attention. The stability of these powders at high temperatures is important to investigate, since they would ultimately be consolidated at elevated temperatures into useful products, and service temperature limits will be important. In this article, we discuss the detailed characterization of SPEX milled Al-CNT powders (well-dispersed CNTs in an Al matrix at 2.5 and 5.0 wt% loading), their heat treatment and consolidation using spark plasma sintering.
Improving the efficiency of an EEG-based brain computer interface using Filter Bank Common Spatial Pattern

Brain Computer Interface (BCI) systems are popular due to their ability to improve the quality of life of disabled people. They are proper tools to translate the movement intentions of human into control's signals for out devices. We use Filter Bank Common Spatial Pattern (FBCSP) which leads to extracting discriminative spatial patterns of Electroencephalogram (EEG) signals. In the FBCSP algorithm, the EEG signals are decomposed into multiple frequency bands using a filter bank. In order to exploit the time-varying feature of EEG signals and extract the unknown features, we partitioned the EEG signals into a number of time windows. Then, CSP features are extracted from all time windows of all frequency bands. Finally, feature selection and classification has been done using Mutual Information (MI) algorithm and Naive Bayesian classifier, respectively. We used EEG dataset BCI Competition IV dataset 2a to evaluate the proposed method. This dataset is a four-class two-session motor imagery dataset. The experimental results show that the mean kappa value has improved to 0.60.
Quantitative Chemical Analysis of Slag Ash of Novocherkassk State District Power Plant

Quantitative chemical composition of ash and slag mix generated upon combustion of Donetsk culm is determined. It is established that ash and slag mix of Novocherkassk state district power plant (SDPP) (Rostov region, Russia) after coal combustion has the following composition: iron – 2.3%; mineral constituents – 75.8%; calcium oxide – 20.4%; aluminum oxide – 0.0118%; water – 1.3022%; manganese oxide – 0.18%; copper oxide – 0.0043%; plumbum – 0.0017%; it is qualified as hazard class IV for environment. Cadmium, mercury, arsenic, selenium, antimony, bismuth are contained in trace amounts < 0.1 mg/kg. In order to reduce dust emissions the cleaning stage I of gas scrubbing facility is equipped with group cyclone comprised of four cyclone units. Medium and coarse particles under the action of centrifugal force are deposited in the cyclone. This promoted decrease in dust load on bag filters, the cleaning stage II. Qualitative chemical composition of ash captured by group cyclone and that of ash captured by bag filter are determined. Cadmium, copper, plumbum, zinc, iron, manganese, and calcium are contained in coarse and fine particles captured by group cyclone and bag filter. Aluminum referred to light metals is completely captured by cyclone. The ash dust in its bulk is of dark gray color. The ash captured by cyclone contains coarse particles of black color and the ash captured bag filter contains fine particles of light colors (gray-yellow color producing silky gloss). This is characteristic for ash dust after combustion of Donetsk culm. The main component of the ash is silicon dioxide, its content in the ash captured by filter reaches 91%. These fine particles create light tone of this ash. keywords: Fly Ash, Slag ash, Quantitative chemical analysis, Thermal power plant.
Linear precoding for Faster-than-Nyquist signaling

Faster-than-Nyquist (FTN) signaling is a nonorthogonal transmission scheme, in which the pulses appear faster than the rate known from the Nyquist intersymbol interference (ISI)-free transmission criterion. Due to the violation of the Nyquist theorem, while a higher transmission rate is offered, ISI is introduced and the receiver complexity is greatly improved in the FTN signaling. The precoding technique can preprocess transmitted data before shaping filtering at the transmitter to remove ISI and relieve the receiver of heavy equalization or estimation procedures in the FTN signaling. In this paper, we investigate three linear precoding schemes, i.e., singular value decomposition (SVD), GTMH(G-to-minus-half) and Cholesky factorization (Cho) to reduce ISI and improve the performance of signal detection in FTN systems. We evaluate the performance of these precoding methods in terms of the bit error rate (BER), mutual information and power spectrum density (PSD). Numerous simulation results demonstrate that the GTMH and Cho precoded FTN have better BER and mutual information performance than the SVD scheme, but at the cost of broadening the spectrum of the signal.
Understanding the Nature of Measurement Error When Estimating Energy Expenditure and Physical Activity via Physical Activity Recall.

BACKGROUND The National Health and Nutrition Examination Survey physical activity questionnaire (PAQ) is used to estimate activity energy expenditure (AEE) and moderate to vigorous physical activity (MVPA). Bias and variance in estimates of AEE and MVPA from the PAQ have not been described, nor the impact of measurement error when utilizing the PAQ to predict biomarkers and categorize individuals. METHODS The PAQ was administered to 385 adults to estimate AEE (AEE:PAQ) and MVPA (MVPA:PAQ), while simultaneously measuring AEE with doubly labeled water (DLW; AEE:DLW) and MVPA with an accelerometer (MVPA:A). RESULTS Although AEE:PAQ [3.4 (2.2) MJ·d-1] was not significantly different from AEE:DLW [3.6 (1.6) MJ·d-1; P > . 14], MVPA:PAQ [36.2 (24.4) min·d-1] was significantly higher than MVPA:A [8.0 (10.4) min·d-1; P < . 0001]. AEE:PAQ regressed on AEE:DLW and MVPA:PAQ regressed on MVPA:A yielded not only significant positive relationships but also large residual variances. The relationships between AEE and MVPA, and 10 of the 12 biomarkers were underestimated by the PAQ. When compared with accelerometers, the PAQ overestimated the number of participants who met the Physical Activity Guidelines for Americans. CONCLUSIONS Group-level bias in AEE:PAQ was small, but large for MVPA:PAQ. Poor within-participant estimates of AEE:PAQ and MVPA:PAQ lead to attenuated relationships with biomarkers and misclassifications of participants who met or who did not meet the Physical Activity Guidelines for Americans.
Optimization of centipede robot body designs through evolutionary algorithms and multiple rough terrains simulation

Biomimetic centipede robots can be well-suited to a number of applications, including search-and-rescue around demolished rubble, logistics in rocky and hazardous areas, and more. The design space for such robots in quite large, with numerous open possibilities for body and leg shapes, configurations and numbers of components. On the other hand, realistic systems aim toward optimizing various performance measures, some of which can be directly calculated from the design parameters, while others require physical experimentation and simulation. In this paper, we present parametric models of centipede robots as well as rough terrains, and then we evaluate the performance of different robot designs across different terrains. We incrementally derive better designs through an appropriately crafted evolutionary algorithm. Promising results as well as insights are discussed. Finally, we provide a clear list of future steps and a forward-looking conclusion.
TO BELIEVE THE UNBELIEVABLE: CONVENTIONAL SCIENTIFIC EXPLANATION VS UNCONVENTIONAL STUDIES AND TECHNOLOGIES; SOME EXAMPLES OF WATER STRUCTURE – BASED TECHNOLOGIES AND RESEARCH

Water is the miraculous liquid that supports and sustains life on Earth. Water has unsuspected qualities that cannot be seen in any other substances. It is a universal solvent, as it can dissolve most natural substances. It is highly sensitive not only to physical but also to informational stimuli. Water has memory – the water molecules’ ability to store information on the physical and chemical structure of the environment it comes into contact with. It records any external influence and stores information. Because of our ignorance with regard to the way we use and approach water, we have endangered our very existence on Earth. The irrational use of synthetic chemicals and their eventual transfer to water have severely affected this inestimable vital resource. This paper aims to reveal lesser known studies, research, and technologies involving water, that could fundamentally change the narrow, materialistic way of thinking many of us share, and could provide effective and radical solutions for removing pollution and providing clean, healthy, and balanced energy sources and lifestyles, in full compliance with nature’s harmonious and wise laws. Such research and unconventional technologies cannot be explained conventionally, by Cartesian thinking and analysis, but by the well-directed reflection of those who know how to listen and understand the mysterious voice of nature.
Search for rare protein altering variants influencing susceptibility to multiple myeloma

The genetic basis underlying the inherited risk of developing multiple myeloma (MM) is largely unknown. To examine the impact of rare protein altering variants on the risk of developing MM we analyzed high-coverage exome sequencing data on 513 MM cases and 1,569 healthy controls, performing both single variant and gene burden tests. We did not identify any recurrent coding low-frequency alleles (1–5%) with moderate effect that were statistically associated with MM. In a gene burden analysis we did however identify a promising relationship between variation in the marrow kinetochore microtubule stromal gene KIF18A, which plays a role in control mitotic chromosome positioning dynamics, and risk of MM (P =3.6×10−6). Further analysis showed KIF18A displays a distinct pattern of expression across molecular subgroups of MM as well as being associated with patient survival. Our results inform future study design and provide a resource for contextualizing the impact of candidate MM susceptibility genes.
A learning-based human facial image quality evaluation method in video-based face recognition systems

Video-based face recognition systems suffer from severe performance degradation under uncontrolled real-world conditions. Using multiple images can enhance recognition performance, however, also introduces extra computational burden. In this paper, we propose a learning-based facial image quality evaluation method, which can be applied to selection of high-quality images and perform quality-based importance weighting in video-based face recognition systems. Features are carefully designed for human faces particularly, and are capable of applying in real-time applications for their low computational complexity. A random forest regressor is utilized to learn a subjective quality function, which is trained on a database labeled manually where the quality of images is scored from 1 to 5. Experiment demonstrates that the proposed method can effectively estimate the subjective quality score of facial images, and can lead to performance gain when applied in video-based face recognition systems.
A stackelberg game for pricing uplink power in wide-band cognitive radio networks

We study the problem of pricing uplink power in wide-band cognitive radio networks under the objective of revenue maximization for the service provider and while ensuring incentive compatibility for the users. User utility is modeled as a concave function of the signal-to-noise ratio (SNR) at the base station, and the problem is formulated as a Stackelberg game. Namely, the service provider imposes differentiated prices per unit of transmitting power and the users consequently update their power levels to maximize their net utilities. We devise a pricing policy and give conditions for its optimality when all the users are to be accommodated in the network. We show that there exist infinitely many Nash equilibrium points that reward the service provider with the same revenue. The pricing policy charges more from users that have better channel conditions and more willingness to pay for the provided service. We then study properties of the optimal revenue with respect to different parameters in the network. We show that for regimes with symmetric users who share the same level of willingness to pay, the optimal revenue is concave and increasing in the number of users in the network. We analytically obtain achievable SNRs for this special case, and finally present a numerical study in support of our results.
Design and Synthesis of Three Naphtol Derivatives using the Three Component System

Three naphtol derivativesweresynthesized using several strategies; in first stage, was synthetized the compound 4(1-[(2-Amino-ethylamino)-phenyl-methyl]-naphthalen-2-ol) using the three-component system (β-naphtol, benzaldehyde and ethylenediamine). The secondstage involves the synthesis of1-{[2-Hex-1-ynyl-phenyl-amino)-ethylamino]-phenyl-methyl}-naphtalen2-ol (6) by the reaction of 4 with 1-hexyne andbenzaldheydeusing cupric chloride as catalyst. The third stage, was achieved by synthesis of 3,8-di(naphtyl-2-ol)-1,10-diphenyl-4,7-diaza-deca-1,9diene (8) using the three-component system (cinamaldehyde, β-naphtol and ethylenediamine). The structure of all compounds obtainedwas confirmed by spectroscopy and spectrometry data. In conclusion, in this study we report some efficient methods for synthesis of naphtol derivatives.It is important to mention that the methods used are highly versatile and the yield is good.
Soft robotic glove for hand rehabilitation based on a novel fabrication method

Soft robotics have shown tremendous potential in helping patients with hand pathologies perform rehabilitation exercises because of the affordability and the inherent softness the soft actuators possess. To date, most of the soft actuators are fabricated with shape deposition manufacturing (SDM) method, which is time-consuming and might not be able to withstand high pressure or preserve the air tightness after multiple inflationdeflation cycles. In this paper, we present a novel monolithic fabrication method combining the advantages of lost-wax and inverse-flow-injection processes. Compared with the actuators formed with traditional fabrication methods, the actuators formed with the novel method proposed in this paper not only possess superior fabrication efficiency, but also can bear 2.5 times higher pressure, and still show advantages on the fatigue endurance testing when performed at the same pressure and frequency. Lastly, a prototyped hand rehabilitation system consisting of a soft robotic glove, an integrated control box and a concise interface are presented.
Nursing Philosophy of community mental health nurses in Japan: A qualitative, descriptive study

The present study reports the findings of a qualitative, descriptive study that sought to clarify nursing philosophy for community mental health nurses (CMHN) working at independent psychiatric home-visit nursing agencies in Japan. We carried out participant observation and semistructured interviews with 13 CMHN in rural and urban areas. We identified eight subthemes and three higher-order themes based on these subthemes. CMHN embraced a nursing philosophy in which they: (i) have respect for consumers' ways of life and their self-realization; (ii) find harmony between view of life and work; and (iii) build communities where residents support each other beyond their roles. Together, these themes constitute a valuable nursing philosophy that supports the recovery of people with mental illness. The themes could also help educate professionals about principles and meanings relevant to recovery, which are regarded as key to changing the professional's care paradigm from a biomedical model to a recovery model.
Lung ultrasound reclassification of chest X‐ray data after pediatric cardiac surgery

Lung ultrasound is gaining consensus for the diagnosis of some pulmonary conditions. Pulmonary complications are common in pediatric cardiac surgery. However, its use remains limited in this setting. Our aim was to test the feasibility of lung ultrasound following pediatric cardiac surgery and to compare lung ultrasound and chest X‐ray findings, assessing whether lung ultrasound may provide additional information.
Does Host Self‐Regulation Increase the Likelihood of Insect‐Pathogen Population Cycles?

The ability of insect pathogens (in particular viruses) to cause long-term stable limit cycles in their hosts was first demonstrated theoretically by Anderson and May (1980, 1981). Since then, there has been extensive discussion and controversy about whether the cycles seen in temperate forest insects (Varley 1949; Baltensweiler 1964; Baltensweiler et al. 1977) are caused by pathogens (Myers 1988) and whether the addition of greater biological detail to the simple Anderson and May model increases or decreases the likelihood of cycles. The Anderson and May model is a set of coupled ordinary differential equations, related to the Lotka-Volterra predator-prey model, in which neither the host nor the pathogen shows any density-dependent self-regulation. Since the type of forest insect that shows long-term cycles frequently severely depletes its food supply, it is natural to ask whether the addition of host selfregulation makes cycles more likely. This was first done by Bowers et al. (1993), who concluded that density dependence was unlikely to increase the probability of population cycles, although the opposite conclusion was reached the following year by Dwyer (1994). The apparent contradiction was reconciled by White et al. (1996), who showed that the two studies had incorporated host self-
Phase-controlled, speckle-free holographic projection with applications in precision optogenetics

Abstract. Holographic speckle is a major impediment to computer-generated holographic (CGH) projections in applications ranging from display, optical tweezers, and machining to optogenetic neural control. We present an iterative phase retrieval algorithm that allows the projection of amplitude-controlled speckle-free one-dimensional patterns with a high degree of pattern uniformity. The algorithm, termed the weighted Gerchberg–Saxton with phase-control (GSW-PC), is shown to have the ability to simultaneously control both the phase and amplitude of projected patterns with high diffraction efficiencies. Furthermore, we show that the framework can address the challenge of projecting volumetric phase and amplitude-controlled patterns, by incorporating GSW-PC with the angular spectrum method. The algorithms’ performance is numerically and experimentally tested, and further compared with conventional and modern CGH techniques.
From Fresnel Diffraction Model to Fine-grained Human Respiration Sensing with Commodity Wi-Fi Devices

Non-intrusive respiration sensing without any device attached to the target plays a particular important role in our everyday lives. However, existing solutions either require dedicated hardware or employ special-purpose signals which are not cost-effective, significantly limiting their real-life applications. Also very few work concerns about the theory behind and can explain the large performance variations in different scenarios. In this paper, we employ the cheap commodity Wi-Fi hardware already ubiquitously deployed around us for respiration sensing. For the first time, we utilize the Fresnel diffraction model to accurately quantify the relationship between the diffraction gain and human target's subtle chest displacement and thus successfully turn the previously considered "destructive" obstruction diffraction in the First Fresnel Zone (FFZ) into beneficial sensing capability. By not just considering the chest displacement at the frontside as the existing solutions, but also the subtle displacement at the backside, we achieve surprisingly matching results with respect to the theoretical plots and become the first to clearly explain the theory behind the performance distinction between lying and sitting for respiration sensing. With two cheap commodity Wi-Fi cards each equipped with just one antenna, we are able to achieve higher than 98% accuracy of respiration rate monitoring at more than 60% of the locations in the FFZ. Furthermore, we are able to present the detail heatmap of the sensing capability at each location inside the FFZ to guide the respiration sensing so users clearly know where are the good positions for respiration monitoring and if located at a bad position, how to move just slightly to reach a good position.