Miracle-dz/UniFAR-FTUs huggingface dataset:
A 28-GHz 12-dBm IIP3 Low-Noise Amplifier Using Source-Sensed Derivative Superposition of Cascode for Full-Duplex Receivers

This paper presents a highly linear CMOS low-noise amplifier (LNA) to handle self-interference in a full-duplex array. This LNA is designed using the derivative-superposition technique and source-sensed cascode topology. The third-order intermodulation (IM3) suppression is performed by applying different biases to each of the two cascode amplifiers. By using a thick-oxide transistor for the auxiliary amplifier, it operates reliably even at high VDD of cascode. The LNA achieves noise figure (NF) of 3.3-4.3 dB, 8.6-dB peak gain, -8-dBm input 1-dB gain compression point (IP1dB) and 12-dBm third-order input intercept point (IIP3) while consuming 14-mA current from 1.8-V supply voltage. The measured results show IM3 suppression of 8 dB depending on whether the auxiliary amplifier is turned on/off for linearization.
Semi-Supervised Outlier Detection Algorithms

Author(s): Tun, Jason Sopheap | Advisor(s): Wu, Yingnian | Abstract: In this paper, I compared 6 semi-supervised point outlier detection algorithms: LOF, robust PCA, autoencoder, SOM, one-class SVM and isolation forest. In all experiments, I training the models on only normal data points. Then, I use the models to detect the outliers in the testing data sets basing on the fact that if a point is not a normal point, the point is an outlier. I do the experiment on both generated data and real data. I found each of the 6 algorithms has both advantages and disadvantages. I have described them in details and give some suggested solutions to the weak points.
The influence on inertial load on adaptation processes during running at various speeds

The aim of the paper was to point to the current experimental experiences on adaptation processes in applying inertial loads depending on the quantity and place of its fixing, during running at various speeds. The effects of applying inertial load to certain kinematics and dynamic performances of running are presented in the paper (frequency and length of step, maximum speed of running, duration of swing phase, phase of flight, phase of contact, angle above the knee of the leg at the moment of jump, angle of maximum flexion of leg under the knee etc). The additional proofs of functional (physiological) changes i.e. metabolic adjustments (heart frequency, VO2 max, lactate production, energy expenditure etc) while running under additional load are stated. The effects of increased mechanic and physiological strain (recruiting the fast muscle fibers) on the morphological status (muscles and separate muscle fibers), are presented by means of experimental following of EMG activities during running on treadmill under additional load. Increase of muscle work, caused by inertia load, has also effects on the motor status, first of all through adequate showing of force and strength while running at various speeds. Force and strength differently correlate with the speed of running, which is systematically pointed out in this paper.
Multiple variational image assimilation for accessible micro-elastography

The mechanical properties of organic materials are relevant to biological processes at many scales. For example, pathological and physiological changes in tissue can often be detected by monitoring their elastic properties. At another scale, the stiffness of the substrate has a direct influence on cell function and differentiation. However, current elastographic methods do not yet cover all spatial scales and require complex and expensive experimental setups. In this work, we present a method that could be easily implemented in biology laboratories by using any available fluorescent microscope, regardless of its resolution, and a piezoelectric actuator. To this end, we have developed a variational framework that integrates multiple measurements into a PDE-constrained minimization of a customized image motion descriptor. Preliminary results with a linearly elastic PDE model show that this method is able to extract reliable maps of the rigidity modulus under the noisy imaging conditions characteristic of conventional microscopy and within the deformation ranges of commercially available actuators.
What Dietary Vitamins and Minerals Might Be Protective against Parkinson’s Disease?

Background and Objective: Dietary constituents may affect the progression of Parkinson’s disease (PD). This study aimed to assess the contribution of dietary intake of vitamins and minerals to the severity, motor and non-motor symptoms, and risk of PD. Methods: In this case-control study, 120 patients with PD and 50 healthy participants participated. Dietary intake of vitamins and minerals was determined using a 147-item food frequency questionnaire. The severity of PD was determined by the Unified Parkinson’s Disease Rating Scale (UPDRS). Results: Patients with PD had lower intake of several vitamins and minerals including lycopene, thiamine, vitamin B6, vitamin B12, pantothenic acid, magnesium, zinc, manganese, selenium, chromium, and phosphorus, but had higher intake of α-tocopherol. High dietary intake of vitamin A, α-carotene, β-cryptoxanthin, vitamin C, and α-tocopherol were correlated with increased odds of PD. High intake of lycopene, thiamin, vitamin B6, pantothenic acid, magnesium, zinc, manganese, chromium, and phosphorous correlated with reduced odds of PD. The predictive power of α-tocopherol concerning the risk of PD was stronger relative to other vitamins. Dietary intake of pantothenic acid was negatively correlated with PD severity and symptoms of motor examination and complication. The severity and motor symptoms of PD were also negatively correlated with β-carotene, vitamin C, riboflavin, vitamin B6, and biotin intake. The UPDRS total score and motor symptoms in PD patients were negatively correlated with phosphorus, magnesium, zinc, manganese, and chromium, and strongly with potassium intake. Conclusion: The findings indicate that adequate dietary intake of vitamins and minerals may have a preventive effect on developing PD and progression of motor decline.
Selective Layered Blockchain Framework for Privacy-preserving Data Management in Low-latency Mobile Networks

With the gradual development of Fourth Industrial Revolution technologies, such as artificial intelligence, the Internet of Things, and big data, and the considerable amount of data in mobile networks, low-latency communication and security management are becoming crucial. Blockchain is a data-distributed processing technology that tracks data records to support secure electronic money transactions and data security management in a peer-to-peer environment without the need of a central trusted authority. The data uploaded to the blockchain-shared ledger are immutable, making tracking integrity preservation facile. However, blockchain technology is limited because it is challenging to utilize in the industry owing to its inability to correct data, even when inaccurate data are uploaded. Accordingly, research on blockchain mechanisms that consider privacy-preserving data management is required to commercialize blockchain technology. Previously, off-chain, blacklist, and hard-fork methods have been proposed; however, their application is challenging or impractical. Therefore, to protect privacy, we propose a layered blockchain mechanism that can correct data by adding a buffer blockchain. We evaluated the latency, security, and space complexity of layered blockchains. The security and security-to-latency ratio for data management of the selective layered blockchain is 2.2 and 11.3 times higher than the conventional blockchains, respectively. The proposed selective layered blockchain is expected to promote the commercialization of blockchain technologies in various industries by protecting user privacy.
Accelerating High b-Value Diffusion-Weighted MRI Using a Convolutional Recurrent Neural Network (CRNN-DWI)

Purpose: To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. Methods: A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. Results: Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). Conclusions: CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden.
Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization

The use of small, low-Earth-orbit satellites enables many novel Earth observation use cases due to their cost efficiency. To cope with the challenging communication environment, machine learning algorithms, such as artificial neural networks, can be applied onboard the satellites. They help to prioritize or pre-process sensor measurements and to reduce the amount of data transmitted to Earth. However, transferring and updating machine learning models to suit changing prioritization requirements poses a number of challenges in itself due to short contact times of satellites with ground stations and lossy communication links. We propose a new transmission mechanism for model updates that retains high performance even when these updates have been only partially transmitted. We achieve this by approximating missing model weights using a vector quantization approach. Using a support structure of quantized vector indices, we can approximate the model with a small amount of data, which is transmitted first, while retaining a high performance. The model performance can then be incrementally improved, as more exact model weights are transmitted to the satellites. Our evaluation shows that this approach significantly outperforms existing baselines.
Biology and Ecology of Delia planipalpis (Stein) (Diptera: Anthomyiidae), an Emerging Pest of Broccoli in Mexico

Simple Summary The development of effective pest management tools for the radish fly, Delia planipalpis, requires an understanding of the basic biology and reproductive behavior of this pest. In the present study, we investigated the pest’s biology on radish and confirmed the identity of the pest by sequencing the COI gene. The development of immature stages lasted 32–33 days. Female flies began mating 1–4 days after emergence, whereas males attempted to mate immediately after emergence. Most females only mated once during the 4-day observation period. Females preferred to lay eggs on radish plants rather than broccoli, although this result may have been influenced by the plant used to rear the insect colony. Male and female flies lived longer when individualized (14.5–22.5 days) compared to insects that were confined in groups (10–11 days). This is the first detailed and statistically rigorous study on the basic biology of this important pest of broccoli and other cruciferous crops. Abstract Delia planipalpis (Stein) (Diptera: Anthomyiidae) is a pest of crucifers, such as broccoli, radish, cauliflower, turnip and cabbage. It has been recently described in Mexico as a significant emerging pest of broccoli. Due the lack of knowledge of this pest, the present study aimed to determine its life cycle, female sexual maturation, copulation, oviposition behavior and adult longevity. The identity of the fly in Mexico was confirmed genetically by sequencing the cytochrome oxidase subunit 1 gene (COI). The mean development time of D. planipalpis was 32–33 days on radish at 24 °C under laboratory conditions. Females became sexually mature 1–2 days after emergence, and the highest incidence of matings was recorded on the second day (60%). Under choice conditions, D. planipalpis females preferred to oviposit on radish plants, rather than broccoli plants, possibly due to the use of radish for rearing the laboratory colony. Oviposition and the mean number of eggs laid varied among the broccoli varieties, with the highest oviposition observed on the Tlaloc variety. Repeated attempts to rear the laboratory colony on broccoli plants failed. Radish-reared insects of both sexes lived longer when individualized in the adult stage (14.5–22.5 days) than when adult flies were maintained in groups (10–11 days). This study contributes to the understanding of D. planipalpis biology and provides information that can be used to establish future control strategies against this pest.
Probing the seeding of hydrodynamic instabilities from nonuniformities in ablator materials using 2D velocimetry

Despite the extensive work done to characterize and improve the smoothness of ablator materials used in inertial confinement fusion (ICF), features indicative of seeded instability growth in these materials are still observed. A two-dimensional imaging velocimetry technique has been used on Omega to measure the velocity non-uniformities of shock fronts launched by indirect drive in the three ablator materials of current interest, glow-discharge polymer, beryllium, and high-density carbon ablators. Observed features are deviations from shock front planarity with amplitudes of a few tens of nanometers, local velocity variations of a few tens of m/s, and transverse spatial scales ranging from 5 to 200 μm. These data will help develop a full understanding of the effects of surface topography, dynamic material response, and internal heterogeneities on the stability of ICF capsules. For all three ablators, we have quantified perturbations at amplitudes that can dominate conventional surface roughness seeds to hydrodynamic instability.Despite the extensive work done to characterize and improve the smoothness of ablator materials used in inertial confinement fusion (ICF), features indicative of seeded instability growth in these materials are still observed. A two-dimensional imaging velocimetry technique has been used on Omega to measure the velocity non-uniformities of shock fronts launched by indirect drive in the three ablator materials of current interest, glow-discharge polymer, beryllium, and high-density carbon ablators. Observed features are deviations from shock front planarity with amplitudes of a few tens of nanometers, local velocity variations of a few tens of m/s, and transverse spatial scales ranging from 5 to 200 μm. These data will help develop a full understanding of the effects of surface topography, dynamic material response, and internal heterogeneities on the stability of ICF capsules. For all three ablators, we have quantified perturbations at amplitudes that can dominate conventional surface roughness seeds to h.
Small is Powerful: Recent Progress in Solution‐Processed Small Molecule Solar Cells

Over the last 5 years, research on the synthesis, device engineering, and device physics of solution‐processed small molecule solar cells (SMSCs) has rapidly expanded. Improvements in molecular design and emergent device processing techniques have helped solution‐processed SMSCs overcome earlier difficulties in controlling active layer morphology, such that many systems are now at—or approaching—10% power conversion efficiency. In this review, details of the highest performing blend systems are presented in order to identify key trends and provide perspective on current progress in the field. Among the best systems, a planarized molecular structure is prevalent, which can be achieved using large fused‐ring moieties, intermolecular non‐bonding interactions, and side chain engineering. To obtain efficient devices, the highest performing systems have been optimized through the careful combination of thermal and solvent annealing procedures. Even without additional processing, some systems have been able to obtain interconnected morphologies and efficient charge generation and charge transport. Ultimately, the design of more efficient materials also requires additional understanding of the device physics and loss mechanisms. After highlighting what is known to date on processes limiting device efficiency, an outlook on the most important challenges remaining to the field is provided.
Motion compensated reconstruction of the aortic valve for computed tomography

Motion compensated cardiac reconstruction in computed tomography (CT) has traditionally been focused on coronary arteries. However, with the increasing number of cardiac CT scans being performed for the diagnosis and treatment planning of valvular diseases, there is a clear need for motion correction of the aortic valve region to assist with the reproducibility of aortic annulus measurements. A second pass approach for aortic valve motion compensation on retrospective ECG-gated CT scans is introduced here. The processing chain is comprised of four steps. A gated multi-phase cardiac reconstruction is first performed, followed by a gradient based filter to enhance the edges in the resulting time series of volume images. Subsequently these normalized filtered results are made to undergo an elastic registration and finally followed by a motion compensated reconstruction that includes the estimated motion vector fields. The method was applied to twelve clinical cases and tested for systolic (30% R-R interval) and diastolic (70% R-R interval) imaging of the aortic valve. This second pass approach leads to a significant reduction of motion artifacts especially in late systole.
Optimizing Crop Production with Bacterial Inputs: Insights into Chemical Dialogue between Sphingomonas sediminicola and Pisum sativum

The use of biological inputs is an interesting approach to optimize crop production and reduce the use of chemical inputs. Understanding the chemical communication between bacteria and plants is critical to optimizing this approach. Recently, we have shown that Sphingomonas (S.) sediminicola can improve both nitrogen supply and yield in pea. Here, we used biochemical methods and untargeted metabolomics to investigate the chemical dialog between S. sediminicola and pea. We also evaluated the metabolic capacities of S. sediminicola by metabolic profiling. Our results showed that peas release a wide range of hexoses, organic acids, and amino acids during their development, which can generally recruit and select fast-growing organisms. In the presence of S. sediminicola, a more specific pattern of these molecules took place, gradually adapting to the metabolic capabilities of the bacterium, especially for pentoses and flavonoids. In turn, S. sediminicola is able to produce several compounds involved in cell differentiation, biofilm formation, and quorum sensing to shape its environment, as well as several molecules that stimulate pea growth and plant defense mechanisms.
Double-blind digital in-line holography from multiple near-field intensities

We present a phase retrieval technique for the recovery of complex-valued wave-fields from multiple near-field diffraction measurements. The proposed method does neither rely on any a priori knowledge about the sample nor on knowledge about an external reference wave, but instead uses multiple self-referencing object exit surface waves that are iteratively recovered. The key ingredient to our approach is a system of relaxed coupled waves that allow for the incorporation of holographic data. We use diffraction measurements of multiple exit surface waves as well as their holograms at multiple sample-detector distances to provide sufficient data redundancy to successfully reconstruct the complex-valued wave field. Parameters for stable performance are investigated. Numerical reconstruction is shown by simulation and experiment to be robust against systematic errors such as position uncertainty and noise. The method proposed is realizable at low cost with instrumentation available in typical optical laboratories.
The Influence of Ewom on The Purchase Intention of Young Consumers at Online Travel Agents Through the Expansion on the Information Adoption Model (Case Study on Traveloka, Tiket.Com, Agoda, Pegipegi, Airbnb)

e-WOM has become very popular in recent years due to the increasing number of contributors and the proliferation of mobile platforms for applicatios on social media. In online travel agents, it is currently known that tourist is obtain information from online review websites rather than hotel websites to decide their hotel choices, therefore eWOM significantly influences consumers purchasing decisions. This research contributes to IAM and the extended model will be tested in eWOM research on five online travel egents (Traveloka, Tiket.com, Agoda, Pegipegi, Airbnb). This study aims to examine the effect of argument quality, source credibility, information quantity, and emotional word understanding on the percived usefulness of eWOM, to analyze the effect of perceived usefulness on information adoption and to investigate the effect of information adoption on young consumers purchase intentions. The type of research used in this study is descriptive analysis with quantitative approach with CB-SEM data analysis techniques using the Amos24 program with a minimum sample of 385 respondents.The results show that argument quality, source credibility, information quantity and emotive word comprehension have positive effect on the perceived usefulness of eWOM. Perceived usefulness has positive influence on the information adoption of eWOM, which in turn predicts the young consumers’ purchase intentions. This study makes several contributions to the literature on marketing communications, in particular to eWOM research and IAM theory. Practically, this research provides knowledge and insight to online travel agent site marketers in order to increase the usability and effectiveness of eWOM to attract more young consumers.