Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Inverse Control of Piezoelectric Actuators Based on Segment Similarity

The hysteresis behavior of piezoelectric actuators is primarily responsible for the decrease of the precision and performance of the nanopositioning systems. To compensate for hysteresis, an inverse model is proposed in this paper as a segment similarity feedforward compensator. Furthermore, the inverse model is improved by using an adaptive method to eliminate the accumulated error of the similarity compensator. The proposed adaptive similarity compensator has the advantages of simple structure, less storage space, and fast calculation speed, and it is available for most engineering situations. Experiments are carried out with the proposed similarity compensator and adaptive similarity compensator. The experimental results show that the compensator can achieve good results and better performance, especially for high-frequency excitations.
Analysis of RNA localization and metabolism in single live bacterial cells: achievements and challenges

Studies of gene expression at the single cell level in live bacterial cells represent a new and fertile area of research providing real‐time, spatially specified information that cannot be obtained by techniques relying on large cell populations. Before recently, most single‐cell studies have been concerned with gene expression at the protein level and explored the spatiotemporal localization and dynamics of different bacterial proteins. However, to fully understand the complex process of gene expression, it is necessary to visualize and quantify RNA molecules in the cellular environment. The first studies analysing the kinetics of RNA transcription and the distribution of RNA in single bacterial cells in real time have recently been reported. Here, I discuss the methods allowing RNA detection in living bacterial cells, the results on RNA kinetics and RNA localization, and the challenges for future research in this area.
High‐density lipoproteins suppress chemokine expression and proliferation in human vascular smooth muscle cells

The inflammatory chemokines CCL2, CCL5, and CX3CL1 stimulate vascular smooth muscle cell (SMC) proliferation. High‐density lipoproteins (HDLs) exhibit potent cardioprotective and anti‐inflammatory properties. We therefore sought to determine the effect of reconstituted HDLs (rHDLs) on SMC chemokine expression and proliferation and elucidate the mechanisms. Preincubation of primary human SMCs with rHDLs containing apolipoprotein (apo)A‐I and phosphatidylcholine (20 μM, final apoA‐I concentration), before stimulation with TNF‐α, inhibited CCL2 (54%), CCL5 (38%), and CX3CL1 (33%) protein levels. The chemokine receptors CCR2 (29%) and CX3CR1 (22%) were also reduced by rHDLs. Incubation with rHDLs reduced the NF‐κB subunit p65 in the nucleus (39%) and phosphorylated IκBα (28%), both regulators of chemokine expression. Furthermore, rHDLs inhibited the upstream signaling proteins phosphoinositide 3‐kinase (37%) and phosphorylated Akt (pAkt, 49%). Incubation with rHDLs strikingly suppressed SMC proliferation (84%) and ERK phosphorylation (pERK, 29%). Finally, siRNA knockdown of the scavenger receptor SR‐B1 attenuated rHDL‐induced inhibition of SMC chemokine expression, p65, and proliferation, indicating that SR‐B1 plays a key role in mediating these effects. Thus, rHDLs reduce SMC chemokine expression (via NF‐κB/pAkt inhibition) and proliferation (via pERK inhibition). This has important implications for preventing the pathogenesis of neointimal hyperplasia, the main cause of early vein graft/stent failure. —Van der Vorst, E. P. C. , Vanags, L. Z. , Dunn, L. L. , Prosser, H. C. , Rye, K. ‐A. , Bursill, C. A. High‐density lipoproteins suppress chemokine expression and proliferation in human vascular smooth muscle cells. FASEB J. 27, 1413–1425 (2013). www.fasebj. org
An End-to-End System for Real-Time Dynamic Point Cloud Visualization

The growing availability of RGB-D data, as delivered by current depth sensing devices, forms the basis for a variety of mixed reality (MR) applications, in which real and synthetic scene content is combined for interaction in real-time. The processing of dynamic point clouds with possible fast and unconstrained movement poses special challenges to the surface reconstruction and rendering algorithms. We propose an end-to-end system for dynamic point cloud visualization from RGB-D input data that takes advantage of the Unity3D game engine for efficient state-of-the-art rendering and platform-independence. We discuss specific requirements and key components of the overall system along with selected aspects of its implementation. Our experimental evaluation demonstrates that high-quality and versatile visualization results can be obtained for datasets of up to 5 million points in real-time.
An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

Radio-based indoor localization is currently a very vibrant scientific research field with many potential use cases. It offers high value for customers, for example, in the fields of robotics, logistics, and automation, or in context-aware IT services. Especially for autonomous systems, dynamic human–machine interaction, or augmented reality applications, precise localization coupled with a high update rate is a key. In this paper, we present a completely novel localization concept whereby received radio signal phase values that are fed into an extended Kalman filter (EKF) without any preprocessing are evaluated. Standard preprocessing steps, such as angle-of-arrival estimation, beamforming, and time-of-flight or time-difference-of-arrival estimations are not required with this approach. The innovative localization concept benefits from the high sensitivity of radio signals’ phase to distance changes and the fast and straightforward recursive computation offered by the EKF. It completely forgoes the computational burden of other phase-based high-precision localization techniques, such as synthetic aperture methods. To verify the proposed method, we use an exemplary setup employing a 24 GHz frequency-modulated continuous-wave (CW) single-input multiple-output secondary radar with 250 MHz bandwidth. A high-precision six-axis robotic arm serves as a 3D positioning reference. The test setup emulates a realistic industrial indoor environment with significant multipath reflections. Despite the challenging conditions and the rather low bandwidth, the results show an outstanding localization 3D RMSE of around 1.7 cm. The proposed method can easily be applied to nearly any type of radio signal with CW carrier and is an attractive alternative to common multilateration and multiangulation localization approaches. We think it is a quantum leap in wireless locating, as it has the potential for precise, simple, and low-cost wireless localization even with standard narrowband communication signals.
Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

Typical attempts to improve the capability of visual place recognition techniques include the use of multi-sensor fusion and the integration of information over time from image sequences. These approaches can improve performance but have disadvantages, including the need for multiple physical sensors and calibration processes, both for multiple sensors and for tuning the image matching sequence length. In this letter, we address these shortcomings with a novel “multi-sensor” fusion approach applied to multiple image processing methods for a single visual image stream, combined with a dynamic sequence matching length technique and an automatic weighting scheme. In contrast to conventional single method approaches, our approach reduces the performance requirements of a single image processing methodology, instead requiring that within the suite of image processing methods, at least one performs well in any particular environment. In comparison to static sequence length techniques, the dynamic sequence matching technique enables reduced localization latencies through analysis of recognition quality metrics when re-entering familiar locations. We evaluate our approach on multiple challenging benchmark datasets, achieving superior performance to two state-of-the-art visual place recognition systems across environmental changes including winter to summer, afternoon to morning, and night to day. Across the four benchmark datasets our proposed approach achieves an average F1 score of 0.95, compared to 0.87 for NetVLAD and 0.46 for SeqSLAM. We provide source code for the multi-fusion method and present analysis explaining how superior performance is achieved despite the multiple, disparate, image processing methods all being applied to a single source of imagery, rather than to multiple separate sensors.
Structural model of the gas vesicle protein GvpA and analysis of GvpA mutants in vivo

Gas vesicles are gas‐filled protein structures increasing the buoyancy of cells. The gas vesicle envelope is mainly constituted by the 8 kDa protein GvpA forming a wall with a water excluding inner surface. A structure of GvpA is not available; recent solid‐state NMR results suggest a coil‐α‐β‐β‐α‐coil fold. We obtained a first structural model of GvpA by high‐performance de novo modelling. Attenuated total reflection (ATR)‐Fourier transform infrared spectroscopy (FTIR) supported this structure. A dimer of GvpA was derived that could explain the formation of the protein monolayer in the gas vesicle wall. The hydrophobic inner surface is mainly constituted by anti‐parallel β‐strands. The proposed structure allows the pinpointing of contact sites that were mutated and tested for the ability to form gas vesicles in haloarchaea. Mutations in α‐helix I and α‐helix II, but also in the β‐turn affected the gas vesicle formation, whereas other alterations had no effect. All mutants supported the structural features deduced from the model. The proposed GvpA dimers allow the formation of a monolayer protein wall, also consistent with protease treatments of isolated gas vesicles.
Ubiquitin Ligase Smurf1 Mediates Tumor Necrosis Factor-induced Systemic Bone Loss by Promoting Proteasomal Degradation of Bone Morphogenetic Signaling Proteins*

Chronic inflammatory disorders, such as rheumatoid arthritis, are often accompanied by systemic bone loss, which is thought to occur through inflammatory cytokine-mediated stimulation of osteoclast resorption and inhibition of osteoblast function. However, the mechanisms involved in osteoblast inhibition remain poorly understood. Here we test the hypothesis that increased Smad ubiquitin regulatory factor 1 (Smurf1)-mediated degradation of the bone morphogenetic protein pathway signaling proteins mediates reduced bone formation in inflammatory disorders. Osteoblasts derived from bone marrow or long bone samples of adult tumor necrosis factor (TNF) transgenic (TNF-Tg) mice were used in this study. TNF decreased the steady-state levels of Smad1 and Runx2 protein similarly to those in long bones of TNF-Tg mice. In the presence of the proteasome inhibitor MG132, TNF increased accumulation of ubiquitinated Smad1 protein. TNF administration over calvarial bones caused decreases in Smad1 and Runx2 protein levels and mRNA expression of osteoblast marker genes in wild-type, but not in Smurf1-/- mice. Vertebral bone volume and strength of TNF-Tg/Smurf1-/- mice were examined by a combination of micro-CT, bone histomorphometry, and biomechanical testing and compared with those from TNF-Tg littermates. TNF-Tg mice had significantly decreased bone volume and biomechanical properties, which were partially rescued in TNF-Tg/Smurf1-/- mice. We conclude that in chronic inflammatory disorders where TNF is increased, TNF induces the expression of ubiquitin ligase Smurf1 and promotes ubiquitination and proteasomal degradation of Smad1 and Runx2, leading to systemic bone loss. Inhibition of ubiquitin-mediated Smad1 and Runx2 degradation in osteoblasts could help to treat inflammation-induced osteoporosis.
Using Bayesian Analysis to Refine the Measurement of the Innovative Capacities of Engineers

In this research-to-practice paper we provide the context for our research and review our process for conducting our engineering innovativeness factor analysis using Bayesian statistical approaches rather than using the traditional principal components analysis (PCA). Our purpose in discussing this analysis approach is to demonstrate that we found the Bayesian analysis approach to be a superior way to refine factors and reduce survey items when developing our instrument for assessment of engineering innovativeness characteristics.
Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are known as two critical building blocks on the intersection point of things and human-based techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this paper, we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence, we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.
Unconventional Association of the Polycomb Group Proteins with Cytokine Genes in Differentiated T Helper Cells*

The cytokine transcription profiles of developing T helper 1 and T helper 2 cells are imprinted and induced appropriately following stimulation of differentiated cells. Epigenetic regulation combines several mechanisms to ensure the inheritance of transcriptional programs. We found that the expression of the polycomb group proteins, whose role in maintaining gene silencing is well documented, was induced during development in both T helper lineages. Nevertheless, the polycomb proteins, YY1, Mel-18, Ring1A, Ezh2, and Eed, bound to the Il4 and Ifng loci in a differential pattern. In contrast to the prevailing dogma, the binding activity of the polycomb proteins in differentiated T helper cells was associated with cytokine transcription. The polycomb proteins bound to the cytokine genes under resting conditions, and their binding was induced dynamically following stimulation. The recruitment of the polycomb proteins Mel-18 and Ezh2 to the cytokine promoters was inhibited in the presence of cyclosporine A, suggesting the involvement of NFAT. Considering their binding pattern at the cytokine genes and their known function in higher order folding of regulatory elements, we propose a model whereby the polycomb proteins, in some contexts, positively regulate gene expression by mediating long-distance chromosomal interactions.
GRU based Named Entity Recognition System for Bangla Online Newspapers

Information Extraction (IE) from textual documents locates important entities and their underlying connections using automated systems which are crucial to different applications including Data Mining (DM), Question Answering (QA), Machine Translation (MT) and so on. Named Entity Recognition (NER) being a sub-component of Natural Language Processing (NLP) is an IE task which aims at locating the textual presence of entities belonging to a prescribed set of classes. Due to its political and geographical influence, Bangla language is widely spoken around the globe and it is important to enrich its linguistic knowledge through NLP tools where NER is a common pre- processing step. The expeditiously growing World Wide Web (WWW) containing Bangla textual documents is in a formative stage with the proliferation of Bangla online newspapers and researchers have applied traditional classic learning algorithms for Bangla NER task while few researchers have used hand- crafted rules. Technological improvements show that with the capability of Deep Learning technique, NER performance can be boosted and hence this work is an effort to apply a variation of Recurrent Neural Network (RNN); especially a Gated Recurrent Unit (GRU) model for developing a Bangla NER task with a manually annotated dataset. The evaluation of our experimental results discovers how our approach can perform better when applied on a large scale dataset.
MoS2/Silicon-on-Insulator Heterojunction Field-Effect-Transistor for High-Performance Photodetection

In this letter, we demonstrate a novel junction field-effect transistor by transferring MoS<sub>2</sub> onto a silicon-on-insulator (SOI) substrate to control the thin Si channel. By combining high light absorption coefficient in MoS<sub>2</sub> with high internal gain in thin Si channel, the device can be used for photodetection and can achieve high responsivity up to <inline-formula> <tex-math notation="LaTeX">$\sim {1.78}\times {10}^{4}$ </tex-math></inline-formula> A/W, high detectivity over <inline-formula> <tex-math notation="LaTeX">${3}\times {10}^{13}$ </tex-math></inline-formula> Jones, and short response time down to 1.44 ms. Furthermore, unlike the conventional SOI photodetector, which is only sensitive to UV light, the response spectrum of our proposed device peaks in visible/near-infrared region, which is interesting for imaging and optical communication applications.
Investigation of Semi-Coupled Dictionary Learning in 3-D Super Resolution HR-pQCT Imaging

High resolution peripheral quantitative CT (HR-pQCT) permits to investigate bone micro-architecture in vivo. While it is a considerable progress over standard CT for this specific application, the spatial resolution remains so far limited for the investigation of bone topology. In this paper, we investigate super resolution techniques to improve 3-D HR-pQCT images via semi-coupled dictionary learning based on the knowledge of high resolution micro-CT images. We propose a method to select the number of atoms and study the impact of patch sizes. To handle the anisotropy of the 3-D bone structure, we propose a 2.5-D strategy learning low and high resolution dictionaries in a semi-coupled way on the three different directions. The results show that this strategy is superior to the application of the method in 2-D. The bone volume fraction is successfully recovered and the estimation of the connectivity is improved in most test samples. The images have a better quality compared with previously studied methods based on total variation (TV) regularization or a combination of TV and a double-well potential. In the future, we expect to further evaluate the method on a larger data set.
High Dynamic Range Image Generation Based on Convolutional Weight Optimization Robust to Mixed Noise Removal

Exposure blending generates a high dynamic range (HDR) image from multiple exposure images. However, when photographing in a low-light scene, these images are deteriorated due to various types of noise, and then it brings down the dynamic range. Existing methods based on pixel-wise blending cannot sufficiently reduce noise, especially in the case of a few input images. This report proposes a convolutional weight optimization method for exposure blending that robustly removes mixed noise and under/over-exposed pixels with a few inputs. In blending each pixel, the proposed method convolves neighboring pixels and generates a noise-free HDR image. The convolution of local regions enables to enhance the denoising capability of image blending. To find a set of weight maps for convolution, we introduce a weight optimization problem as a convex optimization problem, in which Huber loss function is utilized as a fidelity measure in blending to make the method robust to outliers, and solve the optimization problem by using the primal-dual splitting method. The weighted sum of the noisy input images with the estimated weight maps makes a noise-free HDR image. Experimental results show the validity of the proposed method compared with several conventional methods.
Selection of Empirical Mode Decomposition Techniques for Extracting Breathing Rate From PPG

Breathing rate (BR) is a significant bio marker that provides both prognostic and diagnostic information for monitoring physiological condition. In addition to vital bio markers, such as blood oxygen saturation and pulse rate, BR can be extracted from non-invasive and wearable pulse oximeter based photoplethysmogram (PPG). Empirical mode decomposition (EMD) and its noise-assisted variants are widely used for decomposing non-linear and non-stationary signals. In this work, the effect of all variants of EMD in extracting BR from PPG has been investigated. We have used an EMD family PCA based hybrid model in extracting BR from PPG, which is a natural extension of our previously developed ensemble EMD (EEMD) PCA hybrid model. The performance of each model has been tested using two different datasets: MIMIC and Capnobase. Median absolute error varied from 0 to 5.03 and from 2.47 to 10.55 breaths/min for MIMIC and Capnobase dataset, respectively. Among all the EMD variants, EEMD-PCA and improved complete EEMD with adaptive noise (ICEEMDAN) PCA hybrid model present better performance for both datasets. This is the first study to compare EMD variants performance for decomposing real world signal and determine that from current methods, ICEEMDAN and EEMD are optimal for estimating BR from PPG.
Deep Reinforcement Learning in Soft Viscoelastic Actuator of Dielectric Elastomer

Dielectric elastomer actuators (DEAs) have been widely employed as artificial muscles in soft robots. Due to material viscoelasticity and nonlinear electromechanical coupling, it is challenging to accurately model a viscoelastic DEA, especially when the actuator is of a complex or irregular configuration. Control of DEAs is thus challenging but significant. In this letter, we propose a model-free method for control of DEAs, based on deep reinforcement learning. We perform dynamic feedback control by considering the time-dependent behavior of DEAs. Our method is generic in that it does not require task-specific knowledge about the structure or material parameters of the DEA. The experiments show that our method is robust to achieve accurate control for the DEAs of different configurations, different prestretches, and at different times (the material property usually changes due to viscoelasticity effects). To the best of our knowledge, this letter is the first effort to explore deep reinforcement learning for control of DEAs.