Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving scattered pilot channel estimation by sector selection on FDD MIMO/OFDM systems

This paper proposes the combined scheme with the scattered pilot channel estimation and the sector antenna selection to improve throughput and BER performance. Even in the frequency division duplex (FDD) system, the optimal transmission sector can be selected by using up and down link correlation without the feedback information (FBI). Sector selection can alleviate a frequency selectivity by eliminating the number of incoming reflected paths and accordingly the coherence bandwidth can be enlarged. Exploiting this feature, channel estimation accuracy can be kept even though reducing the number of pilot symbols in frequency domain. This paper introduces the high time resolution carrier interferometry (HTRCI) and the simple linear interpolation (SLI) as the scattered pilot channel estimation and evaluates their achievable throughput performance. Computer simulation on 2×2 MIMO/OFDM sector selection system disclosed that HTRCI and SLI can improve BER and throughput performance. Particularly, maximum throughput is improved by 7.5% in the case of HTRCI.
Functional MRI registration with tissue‐specific patch‐based functional correlation tensors

Population studies of brain function with resting‐state functional magnetic resonance imaging (rs‐fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high‐resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs‐fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole‐brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white‐matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level‐dependent (BOLD) signals using tissue‐specific patch‐based functional correlation tensors (ts‐PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi‐channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.
Tungsten diselenide for mode-locked erbium-doped fiber lasers with short pulse duration

In this paper, a WSe2 film prepared by chemical vapor deposition (CVD) is transferred onto a tapered fiber, and a WSe2 saturable absorber (SA) is fabricated. In order to measure the third-order optical nonlinearity of the WSe2, the Z-scan technique is applied. The modulation depth of the WSe2 SA is measured as being 21.89%. Taking advantage of the remarkable nonlinear absorption characteristic of the WSe2 SA, a mode-locked erbium-doped fiber laser is demonstrated at 1557.4 nm with a bandwidth of 25.8 nm and signal to noise ratio of 96 dB. To the best of our knowledge, the pulse duration of 163.5 fs is confirmed to be the shortest compared with previous mode-locked fiber lasers based on transition-metal dichalcogenides SAs. These results indicate that WSe2 is a powerful competitor in the application of ultrashort pulse lasers.
Microfluidic platforms for the study of neuronal injury in vitro

Traumatic brain injury (TBI) affects 5.3 million people in the United States, and there are 12,500 new cases of spinal cord injury (SCI) every year. There is yet a significant need for in vitro models of TBI and SCI in order to understand the biological mechanisms underlying central nervous system (CNS) injury and to identify and test therapeutics to aid in recovery from neuronal injuries. While TBI or SCI studies have been aided with traditional in vivo and in vitro models, the innate limitations in specificity of injury, isolation of neuronal regions, and reproducibility of these models can decrease their usefulness in examining the neurobiology of injury. Microfluidic devices provide several advantages over traditional methods by allowing researchers to (1) examine the effect of injury on specific neural components, (2) fluidically isolate neuronal regions to examine specific effects on subcellular components, and (3) reproducibly create a variety of injuries to model TBI and SCI. These microfluidic devices are adaptable for modeling a wide range of injuries, and in this review, we will examine different methodologies and models recently utilized to examine neuronal injury. Specifically, we will examine vacuum‐assisted axotomy, physical injury, chemical injury, and laser‐based axotomy. Finally, we will discuss the benefits and downsides to each type of injury model and discuss how researchers can use these parameters to pick a particular microfluidic device to model CNS injury.
Low-Profile EndFire Leaky-Wave Antenna With Air Media

A low-profile endfire leaky-wave antenna is proposed in this paper. The proposed low profile antenna utilizes microstrip line to excite planar inverted-L antenna (PILA) elements. Air media is utilized in the proposed array to realize the endfire property. By using air media, the proposed leaky-wave array maintains phase constant slightly larger than the wavenumber in free space and achieves endfire radiation. PILA elements are periodically spaced to radiate and the spacing period is designed as <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula>/4 (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength in free space) to achieve a relatively wide impedance bandwidth. A prototype of the proposed endfire antenna is fabricated and tested. The measured results are consistent with full-wave simulation results. The length of the whole leaky-wave array is about <inline-formula> <tex-math notation="LaTeX">$6\lambda _{0}$ </tex-math></inline-formula>, and the measured gain in the endfire direction is 11.5 dBi at 5.1 GHz.
A novel three-dimensional culture system for oligodendrocyte precursor cells

Oligodendrocytes are generated from oligodendrocyte precursor cells (OPCs). Mechanisms of OPC differentiation have been extensively examined with two-dimensional cell culture systems. However, these cellular events may be more accurately represented using a three-dimensional (3D) model. In this study, we report the development of a novel 3D OPC culture system using gels composed of a mixture of collagen and hyaluronan, wherein cultured rat primary OPCs can proliferate and differentiate into oligodendrocytes. Our data show that the gel concentration and cell-seeding density are critical factors for the numbers of OPCs and oligodendrocytes in our 3D culture system. In addition, Notch signaling, which supports cell-to-cell communication, may also be important for OPC function in our system because a Notch inhibitor DAPT suppressed OPC proliferation and differentiation. Taken together, cultured rat OPCs can grow in collagen-/hyaluronan-based gels, and our novel 3D OPC culture system may offer a useful platform for examining the mechanisms of OPC function in vitro.
Cardiac amyloidosis is associated with increased aortic stiffness

Cardiac amyloidosis (CA) is as an infiltrative disorder primarily caused by extracellular tissue deposition of amyloid fibrils in the myocardial interstitium. The current study was designed to test whether alterations in ascending aortic elastic properties could be detected by echocardiography in CA patients, and to compare their results to controls.
The Effect of Weather during Rearing on Morphometric Traits of Juvenile Cliff Swallows

Episodes of food deprivation may change how nestling birds allocate energy to the growth of skeletal and feather morphological traits during development. Cliff swallows (Petrochelidon pyrrhonota) are colonial, insectivorous birds that regularly experience brief periods of severe weather-induced food deprivation during the nesting season which may affect offspring development. We investigated how annual variation in timing of rearing and weather were associated with length of wing and tail, skeletal traits, and body mass in juvenile cliff swallows reared in southwestern Nebraska during 2001–2006. As predicted under conditions of food deprivation, nestling skeletal and feather measurements were generally smaller in cooler years. However, variability explained by weather was small, suggesting that morphometric traits of juvenile cliff swallows were not highly sensitive to weather conditions experienced during this study. Measurements of juvenile morphological traits were positively correlated with measurements taken as adults, meaning that any variation among juveniles in response to rearing conditions showed evidence of persisting into a bird’s first breeding season. Our results show that body size in this species is phenotypically plastic and influenced, in part, by weather variables.
A double-frequency combined positioning algorithm of beidou navigation satellite system

With the second civilian signal B2I announced, Beidou Navigation Satellite System has been into the era of multiple frequency application. In order to improve the accuracy of BDS positioning, an estimation algorithm of BDS positioning based on Particle Swarm Optimization and Square-Root Unscented Kalman Filter is proposed, which could estimate the noise covariance matrix accurately and compensate defects of Extended Kalman Filter and Unscented Kalman Filter. Based on above, a new algorithm of BDS double-frequency combined positioning is proposed. Experimental results show that the proposed method can effectively improve the accuracy and stability of Beidou System positioning.
GPU CUDA accelerated video inpainting using synthetic algorithms

Video inpainting is essential in many multimedia applications such as film, media restoration, professional post-production and editing for personal use. Most algorithms are unfriendly to parallelization and are biased towards linear sequence processing. This paper presents a universal and automatic method for video inpainting. A heuristic method is used to avoid searching the whole patch in the case of small-scale occlusion and it also reduces the blurriness caused by limited area search in the case of large-scale occlusion. Eliminating "incorrect" patches in the initialization step of patch search, log 1 + 3 rounds propagation and optimized random search are varied to improve the performance of patch search and reconstruction. For small-scale region inpainting, some additional novel factors are added, such as the position of the occlusion, the Isophote directions of field points and the ratio of effective information. This makes the Gaussian kernel weight coefficients more accurate and more representative. For industrial use, the entire process is carried out completely on GPUs. Experiments were conducted on a series of test videos as well as samples used by other competing algorithms. The proposed method demonstrates the effectiveness of inpainting through the qualitative and quantitative results provided in this paper.
A survey on data mining classification algorithms

Data mining refers to extracting or mining knowledge from large amount of data. It is also defined as finding hidden information from a database. It is a technique which is used primarily for discovering unknown patterns and that converts raw data into user understandable information. Nowadays it is being increasingly used in science and technology to extract the vast amount of data. Classification is the separating the given data according to their characteristics similar to one another. These are some of the classification methods Naïve Bayes Classifier, Decision tree, Neural Networks, and Support Vector Machine.
Guiding the management of cervical cancer with convolutional neural networks

In order to appropriately treat cervical cancer, making an accurate determination of a patient’s cervical type is critical. However, doing so can be difficult, even for trained healthcare providers, and no algorithms currently exist to aid them. Here, we describe the development and implementation of a convolutional neural network-based algorithm capable of distinguishing between three cervical types. Our approach extends standard transfer learning pipelines for fine-tuning a deep convolutional neural network, specifically a residual network (ResNet), with customized data augmentation and model ensembling techniques. Our algorithm achieves an accuracy of 81% on our internal test set and a multi-class cross-entropy loss of 0.557 on the Kaggle test set, resulting in a leaderboard position of 25th out of over 800 teams, as of June 12, 2017, with three days left to finalize model submissions.
Two accelerated-aging tests for wood-based panels

Two simplified accelerated-aging exposures were evaluated as possible alternatives to the current six-cycle test in ASTM D 1037. The exposures consisted of four cycles of alternate hot-water soaking and oven drying. For oriented strandboard, flakeboard, and particleboard, bending properties and residual thickness swelling after either exposure were similar to those after the standard six-cycle test. For hard-board, the alternative exposures had considerably less of an effect on properties because much less water was adsorbed compared to that in the standard exposure, which includes steaming. Supplemental tests showed that hardboard had adequate water adsorption during vacuum-pressure soak in room-temperature water
Eight Principles of Uncertainty for Life Cycle Assessment of Biofuel Systems

New environmental regulations in the USA and Europe require a reduction of greenhouse gas (GHG) emissions from transportation fuels as a component of climate change mitigation policy. The US Energy Independence and Security Act of 2007 (EISA) requires GHG emission reductions from the life cycles of biofuels compared to gasoline, by 20% for ethanol from maize grain (maizeethanol), 60% for cellulosic ethanol, and 50% for other advanced biofuels. To determine these reductions, the US Environmental Protection Agency (EPA) employs life cycle assessment (LCA) methods which were not used previously in national environmental regulations. These regulations, entitled the “Renewable Fuel Standard 2” (RFS2), build on concurrent state efforts by the California Air Resources Board (CARB) under the Low Carbon Fuel Standard (LCFS). These regulations can affect billions of dollars in financial incentives and market access for the existing biofuel industry and they will determine how new feedstocks for biofuels are developed in the future. Over roughly the last twenty years, LCA has been applied to biofuel production systems for determining GHG emissions and energy efficiency, but these evolving methods have been inconsistent [1–3]. These methods are used to estimate direct emissions from the life cycle from crop production to finished fuels, while also considering upstream emissions such as from fertilizer production. Contrary to these relatively simple analyses, the assessments currently developed under state and federal law are generally far more complex by including global modeling. The use of global models has been encouraged by findings that indirect effects from biofuel production, which are international in scope, lead
Watershed-Induced Limnological and Microbial Status in Two Oligotrophic Andean Lakes Exposed to the Same Climatic Scenario

Laguna Negra and Lo Encañado are two oligotrophic Andean lakes forming part of the system fed by meltwater from distinct glacial tongues of the Echaurren glacier in central Chile, which is in a recession period. The recent increase in temperature and decline in precipitation have led to an increase of glacial meltwater and sediments entering these lakes. Although the lacustrine systems are also hydrogeologically connected, the limnology of the lakes is strongly controlled by the surface processes related to the respective sub-watersheds and hydrology. Watershed characteristics (area and length, slope, lithology, resistance to erosion, among others) affect the chemical and physical characteristics of both lakes (e.g., nutrient concentration and turbidity). We studied physical and chemical variables and performed 16S rRNA amplicon sequencing to determine the specific microbial signature of the lakes. The transparency, temperature, turbidity and concentrations of chlorophyll-a, dissolved organic matter, nutrients and the total number of cells, revealed the different status of both lakes at the time of sampling. The predominant bacterial groups in both lakes were Proteobacteria, Verrucomicrobia, and Bacteroidetes. Interestingly, the contribution of phototrophs was significantly higher in LN compared to LE (13 and 4% respectively) and the major fraction corresponded to Anoxygenic Phototrophs (AP) represented by Chloroflexi, Alpha, and Betaproteobacteria. Multivariate analyses showed that the nutrient levels and the light availability of both lakes, which finally depend on the hydrological characteristics of the respective watersheds, explain the differential community composition/function. The abundance of a diverse photoheterotrophic bacterioplankton community suggests that the ability to utilize solar energy along with organic and inorganic substrates is a key function in these oligotrophic mountain lakes.
Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts

Quantitative estimation of a driver's vigilance level has a great value for improving driving safety and preventing accidents. Previous studies have identified correlations between electroencephalogram (EEG) spectrum power and a driver's mental states such as vigilance and alertness. Studies have also built classification models that can estimate vigilance state changes based on data collected from drivers. In the present study, we propose a system to detect vigilance level using not only a driver's EEG signals but also driving contexts as inputs. We combined a support vector machine with particle swarm optimization methods to improve classification accuracy. A simulated driving task was conducted to demonstrate the reliability of the proposed system. Twenty participants were assigned a 2-h sustained-attention driving task to identify a lead car's brake events. Our system was able to account for 84.1% of experimental reaction times with 162-ms prediction errors. A newly introduced driving context factor, road curves, improved the prediction accuracy by 2–5% with 30–80 ms smaller errors. These findings demonstrated the potential value of the proposed system for estimating driver vigilance level on a time scale of seconds.
Results of mujeres felices por ser saludables: a dietary/breast heafth randomized chnicail trial for latino women

Background: Data are limited on the efficacy of health-fo-cused interventions for young, low-acculturated Latino women. Because breast cancer is the most commonly diagnosed cancer and the most common cause of cancer mortality in this population, combined interventions that address both early detection and dietary patterns could help reduce both morbidity and mortality associated with breast cancer in this underserved population.Purpose: Mujeres Felices por ser Saludables was a randomized intervention study designed to assess the efficacy of an 8-month combined dietary and breast health intervention to reduce fat and increase fiber intake as well as to increase the frequency and proficiency of breast self-examination (BSE) and reduce anxiety related toBSE among LatinasMethods: Blocked randomization in blocks of 6 was used to randomize 256 20- to 40-year-old Latinas to the intervention (n = 127) or control group (n = 129). The intervention group attended an 8-month multi-component education program designed specifically for low-acculturated Latinas. The control group received mailed health education material on a schedule comparable to the intervention. A total of 195 women (76.2%) completed both the baseline and 8-month follow-up interviews.Results: The intervention and control groups were similar on baseline sociodemographic characteristics. At the 8-month follow up, the intervention group reported lower dietary fat (p >.001) and higher fiber intake (p = .06); a higher proportion reported practicingBSE at the recommended interval (p > .001) and showed improvedBSE proficiency (p >.001) compared to the control group.BSE-related anxiety was low for both groups at baseline, and no diffirrence in reduction was observed.Conclusions: This project provides a successful model for achirving dietary change and improving breast health behavior in young, low-acculturated Latinas.
Janus Kinase 2 (JAK2) Dissociates Hepatosteatosis from Hepatocellular Carcinoma in Mice*

Hepatocellular carcinoma is an end-stage complication of non-alcoholic fatty liver disease (NAFLD). Inflammation plays a critical role in the progression of non-alcoholic fatty liver disease and the development of hepatocellular carcinoma. However, whether steatosis per se promotes liver cancer, and the molecular mechanisms that control the progression in this disease spectrum remain largely elusive. The Janus kinase signal transducers and activators of transcription (JAK-STAT) pathway mediates signal transduction by numerous cytokines that regulate inflammation and may contribute to hepatocarcinogenesis. Mice with hepatocyte-specific deletion of JAK2 (L-JAK2 KO) develop extensive fatty liver spontaneously. We show here that this simple steatosis was insufficient to drive carcinogenesis. In fact, L-JAK2 KO mice were markedly protected from chemically induced tumor formation. Using the methionine choline-deficient dietary model to induce steatohepatitis, we found that steatohepatitis development was completely arrested in L-JAK2 KO mice despite the presence of steatosis, suggesting that JAK2 is the critical factor required for inflammatory progression in the liver. In line with this, L-JAK2 KO mice exhibited attenuated inflammation after chemical carcinogen challenge. This was associated with increased hepatocyte apoptosis without elevated compensatory proliferation, thus thwarting expansion of transformed hepatocytes. Taken together, our findings identify an indispensable role of JAK2 in hepatocarcinogenesis through regulating critical inflammatory pathways. Targeting the JAK-STAT pathway may provide a novel therapeutic option for the treatment of hepatocellular carcinoma.