Miracle-dz/UniFAR-FTUs huggingface dataset:
Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR

The estimation of the geometric structure of objects located underwater underpins a plethora of applications such as mapping shipwrecks for archaeology, monitoring the health of coral reefs, detecting faults in offshore oil rigs and pipelines, detection and identification of potential threats on the seabed, etc. Acoustic imaging is the most popular choice for underwater sensing. Underwater exploratory vehicles typically employ wide‐aperture Sound Navigation and Ranging (SONAR) imaging sensors. Although their wide aperture enables scouring large volumes of water ahead of them for obstacles, the resulting images produced are blurry due to integration over the aperture. Performing three‐dimensional (3D) reconstruction from this blurry data is notoriously difficult. This challenging inverse problem is further exacerbated by the presence of speckle noise and reverberations. The state‐of‐the‐art methods in 3D reconstruction from sonar either require bulky and expensive matrix‐arrays of sonar sensors or additional narrow‐aperture sensors. Due to its low footprint, the latter induces gaps between reconstructed scans. Avoiding such gaps requires slow and cumbersome scanning by the vehicles that carry the scanners. In this paper, we present two reconstruction methods enabling on‐site 3D reconstruction from imaging sonars of any aperture. The first of these presents an elegant linear formulation of the problem, as a blind deconvolution with a spatially varying kernel. The second method is a simple algorithmic approach for approximate reconstruction, using a nonlinear formulation. We demonstrate that our simple approximation algorithms perform 3D reconstruction directly from the data recorded by wide‐aperture systems, thus eliminating the need for multiple sensors to be mounted on underwater vehicles for this purpose. Additionally, we observe that the wide aperture may be exploited to improve the coverage of the reconstructed samples (on the scanned object's surface). We demonstrate the efficacy of our algorithms on simulated as well as real data acquired using two sensors, and we compare our work to the state of the art in sonar reconstruction. Finally, we show the employability of our reconstruction methods on field data gathered by an autonomous underwater vehicle.
Epidermal γδ T cells originate from yolk sac hematopoiesis and clonally self-renew in the adult

The murine epidermis harbors two immune cell lineages, Langerhans cells (LCs) and &ggr;&dgr; T cells known as dendritic epidermal T cells (DETCs). LCs develop from both early yolk sac (YS) progenitors and fetal liver monocytes before locally self-renewing in the adult. For DETCs, the mechanisms of homeostatic maintenance and their hematopoietic origin are largely unknown. Here, we exploited multicolor fate mapping systems to reveal that DETCs slowly turn over at steady state. Like for LCs, homeostatic maintenance of DETCs is achieved by clonal expansion of tissue-resident cells assembled in proliferative units. The same mechanism, albeit accelerated, facilitates DETC replenishment upon injury. Hematopoietic lineage tracing uncovered that DETCs are established independently of definitive hematopoietic stem cells and instead originate from YS hematopoiesis, again reminiscent of LCs. DETCs thus resemble LCs concerning their maintenance, replenishment mechanisms, and hematopoietic development, suggesting that the epidermal microenvironment exerts a lineage-independent influence on the initial seeding and homeostatic maintenance of its resident immune cells.
Data2Text Studio: Automated Text Generation from Structured Data

Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically, which improves the interactivity and interpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models, and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on RotoWire datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.
De novo design of self-assembling helical protein filaments

Built to be reversible There has been some success in designing stable peptide filaments; however, mimicking the reversible assembly of many natural protein filaments is challenging. Dynamic filaments usually comprise independently folded and asymmetric proteins and using such building blocks requires the design of multiple intermonomer interfaces. Shen et al. report the design of self-assembling helical filaments based on previously designed stable repeat proteins. The filaments are micron scale, and their diameter can be tuned by varying the number of repeats in the monomer. Anchor and capping units, built from monomers that lack an interaction interface, can be used to control assembly and disassembly. Science, this issue p. 705 A general computational approach allows design of self-assembling helical filaments from monomeric proteins. We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo–electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units. The assembly and disassembly of the filaments can be controlled by engineered anchor and capping units built from monomers lacking one of the interaction surfaces. The ability to generate dynamic, highly ordered structures that span micrometers from protein monomers opens up possibilities for the fabrication of new multiscale metamaterials.
Effect of high volume stretch-shortening cycle exercise on vertical leg stiffness and jump performance

ABSTRACT The study aims were to investigate the effects of muscle damage induced by high-volume stretch-shortening cycle exercise on countermovement jump (CMJ) performance and vertical leg stiffness during squat and landing phases. Changes in the peak torque of knee extensor muscles, soreness, plasma CK activity, vertical leg stiffness, and CMJ characteristics were measured in recreationally active men after 50, 100, 150 and 200 drop jumps (DJs) and again 24 h later. The isometric voluntary peak torque after 50 DJs decreased by 22 ± 12% (p < 0.01). However, during the squat phase of CMJ eccentric peak power and rate of force development together with the concentric mean force during the push-off phase increased after 50 DJs (all p < 0.05). After 200 DJs, jump height and concentric peak power during push-off were decreased (p < 0.05). At 24 h after exercise, jump height, peak eccentric force, the rate of eccentric force development and the mean concentric force in CMJ and vertical leg stiffness in the squat all remained decreased (p < 0.05), while vertical leg stiffness in the landing phase was increased (p < 0.05). Muscle damaging exercise volume is associated with changes in vertical leg stiffness and CMJ performance.
Importance of flagella in acute and chronic Pseudomonas aeruginosa infections

Pseudomonas aeruginosa is an environmental microorganism and a causative agent of diverse acute and chronic, biofilm-associated infections. Advancing research-based knowledge on its adaptation to conditions within the human host is bound to reveal novel strategies and targets for therapeutic intervention. Here, we investigated the traits that P. aeruginosa PA14 as well as a virulence attenuated ΔlasR mutant need to survive in selected murine infection models. Experimentally, the genetic programs that the bacteria use to adapt to biofilm-associated versus acute infections were dissected by passaging transposon mutant libraries through mouse lungs (acute) or mouse tumours (biofilm-infection). Adaptive metabolic changes of P. aeruginosa were generally required during both infection processes. Counter-selection against flagella expression was observed during acute lung infections. Obviously, avoidance of flagella-mediated activation of host immunity is advantageous for the wildtype bacteria. For the ΔlasR mutant, loss of flagella did not confer a selective advantage. Apparently, other pathogenesis mechanisms are active in this virulence attenuated strain. In contrast, the infective process of P. aeruginosa in the chronic biofilm model apparently required expression of flagellin. Together, our findings imply that the host immune reactions against the infectious agent are very decisive for acuteness and duration of the infectious disease. They direct disease outcome.
Spatial phylogenetics reveals evolutionary constraints on the assembly of a large regional flora.

PREMISE OF THE STUDY We used spatial phylogenetics to analyze the assembly of the Wisconsin flora, linking processes of dispersal and niche evolution to spatial patterns of floristic and phylogenetic diversity and testing whether phylogenetic niche conservatism can account for these patterns. METHODS We used digitized records and a new molecular phylogeny for 93% of vascular plants in Wisconsin to estimate spatial variation in species richness and phylogenetic α and β diversity in a native flora shaped mainly by postglacial dispersal and response to environmental gradients. We developed distribution models for all species and used these to infer fine-scale variation in potential diversity, phylogenetic distance, and interspecific range overlaps. We identified 11 bioregions based on floristic composition, mapped areas of neo- and paleo-endemism to establish new conservation priorities and predict how community-assembly patterns should shift with climatic change. KEY RESULTS Spatial phylogenetic turnover most strongly reflects differences in temperature and spatial distance. For all vascular plants, assemblages shift from phylogenetically clustered to overdispersed northward, contrary to most other studies. This pattern is lost for angiosperms alone, illustrating the importance of phylogenetic scale. CONCLUSIONS Species ranges and assemblage composition appear driven primarily by phylogenetic niche conservatism. Closely related species are ecologically similar and occupy similar territories. The average level and geographic structure of plant phylogenetic diversity within Wisconsin are expected to greatly decline over the next half century, while potential species richness will increase throughout the state. Our methods can be applied to allochthonous communities throughout the world.
Synthesizing a Contrast-Enhancement Map in Patients with High-Grade Gliomas Based on a Postcontrast MR Imaging Quantification Only

BACKGROUND AND PURPOSE: Administration of a gadolinium-based contrast agent is an important diagnostic biomarker for blood-brain barrier damage. In clinical use, detection is based on subjective comparison of native and postgadolinium-based contrast agent T1-weighted images. Quantitative MR imaging studies have suggested a relation between the longitudinal relaxation rate and proton-density in the brain parenchyma, which is disturbed by gadolinium-based contrast agents. This discrepancy can be used to synthesize a contrast-enhancement map based solely on the postgadolinium-based contrast agent acquisition. The aim of this study was to compare synthetic enhancement maps with subtraction maps of native and postgadolinium-based contrast agent images. MATERIALS AND METHODS: For 14 patients with high-grade gliomas, quantitative MR imaging was performed before and after gadolinium-based contrast agent administration. The quantification sequence was multidynamic and multiecho, with a scan time of 6 minutes. The 2 image stacks were coregistered using in-plane transformation. The longitudinal relaxation maps were subtracted and correlated with the synthetic longitudinal relaxation enhancement maps on the basis of the postgadolinium-based contrast agent images only. ROIs were drawn for tumor delineation. RESULTS: Linear regression of the subtraction and synthetic longitudinal relaxation enhancement maps showed a slope of 1.02 ± 0.19 and an intercept of 0.05 ± 0.12. The Pearson correlation coefficient was 0.861 ± 0.059, and the coefficient of variation was 0.18 ± 0.04. On average, a volume of 1.71 ± 1.28 mL of low-intensity enhancement was detected in the synthetic enhancement maps outside the borders of the drawn ROI. CONCLUSIONS: The study shows that there was a good correlation between subtraction longitudinal relaxation enhancement maps and synthetic longitudinal relaxation enhancement maps in patients with high-grade gliomas. The method may improve the sensitivity and objectivity for the detection of gadolinium-based contrast agent enhancement.
A Comparative Evaluation of Order-Preserving and Order-Revealing Schemes and Protocols

Database query evaluation over encrypted data has received a lot of attention recently. Order Preserving Encryption (OPE) and Order Revealing Encryption (ORE) are two important encryption schemes that have been proposed in this area. These schemes can provide very efficient query execution but at the same time may leak some information to adversaries. In this paper, we present the first comprehensive comparison among a number of important OPE and ORE schemes using a framework that we developed. We evaluate protocols that are based on these schemes as well. We analyze and compare them both theoretically and experimentally and measure their performance over database indexing and query evaluation techniques using not only execution time but also I/O performance and usage of cryptographic primitive operations. Our comparison reveals some interesting insights concerning the relative security and performance of these approaches in database settings. Furthermore, we propose a number of improvements for some of these scheme and protocols. Finally, we provide a number of suggestions and recommendations that can be valuable to database researchers and users.
Bag of local features for person re-identification on large-scale datasets

Abstract. In recent years, large-scale person re-identification has attracted a lot of attention from video surveillance. Usual approaches addressing this task either learn the effective feature embeddings or design the learning architectures to obtain discriminative metrics. Most of them only focus on improving the accuracy of recognition but neglect retrieval efficiency. To improve the accuracy and efficiency of person re-identification simultaneously, an accurate and fast method is proposed based on the bag of visual words (BoVW) model, which has widely been applied in image retrieval. A bag of local features is developed to simplify feature representation for person re-identification. Cross-view dictionary learning is used to eliminate the redundancy of these local features. These local features consist of scale invariant feature transform and local maximal occurrence representation (LOMO) that are invariant in scale and color, respectively. Finally, integrated BoVW histograms are obtained, which encode the images by k-means clustering. Experiments conducted on the CUHK03, Market1501, and MARS datasets show that the proposed method performs favorably against existing approaches.
Quantifying the Reservoir Quality using Dimensionality Reduction Techniques

Echo State Network is a particular type of Recurrent Neural Networks that combines principles from kernels, linear regression and dynamical systems. The neural network has a random initialized hidden-hidden weights (reservoir) that keeps ﬁxed during the training. The reservoir projects the input patterns onto a feature map. Here, we present a correlation analysis between the input space and the feature map. We use a dimensionality reduction technique (Sammon Mapping) for representing the input space. We show a correlation between the Sammon energy and the model accuracy, which can be useful for deﬁning good reservoir topologies.
The darkLight rises: visible light communication in the dark

Visible Light Communication (VLC) emerges as a new wireless communication technology with appealing benefits not present in radio communication. However, current VLC designs commonly require LED lights to emit shining light beams, which greatly limits the applicable scenarios of VLC (e.g., in a sunny day when indoor lighting is not needed). It also entails high energy overhead and unpleasant visual experiences for mobile devices to transmit data using VLC. We design and develop DarkLight, a new VLC primitive that allows light-based communication to be sustained even when LEDs emit extremely-low luminance. The key idea is to encode data into ultra-short, imperceptible light pulses. We tackle challenges in circuit designs, data encoding/decoding schemes, and DarkLight networking, to efficiently generate and reliably detect ultra-short light pulses using off-the-shelf, low-cost LEDs and photodiodes. Our DarkLight prototype supports 1.3-m distance with 1.6-Kbps data rate. By loosening up VLC's reliance on visible light beams, DarkLight presents an unconventional direction of VLC design and fundamentally broadens VLC's application scenarios.
DL-RSIM: A Simulation Framework to Enable Reliable ReRAM-based Accelerators for Deep Learning

Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. To enable reliable memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit and device properties on the inference accuracy. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. DL-RSIM simulates the error rates of every sum-of-products computation in the memristor-based accelerator and injects the errors in the targeted TensorFlow-based neural network model. A rich set of reliability impact factors are explored by DL-RSIM, and it can be incorporated with any deep learning neural network implemented by TensorFlow. Using three representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and develop reliability optimization techniques.
How to Size Intracranial Aneurysms: A Phantom Study of Invasive and Noninvasive Methods

BACKGROUND AND PURPOSE: Endovascular treatment of intracranial aneurysms has relevantly changed over the past decades. Multiple new devices such as intrasaccular flow diverters have broadened the treatment spectrum but require very exact aneurysm sizing. In this study, we investigated multidetector and flat panel angiographic CT and digital subtraction imaging as well as different postprocessing methods (multiplanar reconstruction, volume-rendering technique, 3D DSA, and conventional 2D angiography) for their ability to exactly size 2 aneurysm models. MATERIALS AND METHODS: Two aneurysm models with known aneurysm sizes were placed inside a human skull. After injection of iodine contrast media, imaging was performed using a 128-slice CT scanner or an Artis Q biplane angiosuite, respectively. Aneurysms were measured for width, neck, and height, and the mean difference from the known sizes was calculated for each technique. The technique with the most exact measurement was defined as the criterion standard. We performed Bland-Altman plots comparing all techniques against the criterion standard. RESULTS: Angiograms adjusted according a previous 3D run with a short object-to-detector distance resulted in the most exact aneurysm measurement: −0.07 ± 0.61 mm for aneurysm 1 and 0.17 ± 0.39 mm for aneurysm 2. Measurements of conventional DSA images were similar, and CT-based images were significantly inferior to the criterion standard. CONCLUSIONS: 2D DSA with a short objective-to-detector distance adjusted according to a previous 3D run resulted in the most exact aneurysm measurement and should therefore be performed before all endovascular aneurysm treatments.
Correlation of Vascular Endothelial Growth Factor subtypes and their receptors with melanoma progression: A next-generation Tissue Microarray (ngTMA) automated analysis

Introduction Finding new markers to assess prognosis of melanoma without the necessity to perform a surgical interventions is an important goal in melanoma research. The current study aimed to assess the correlation of clinical course and prognosis of primary and metastatic melanoma with expression of VEGF family and their receptors. Methods A ngTMA block was made from the randomly selected paraffin tissue blocks of the patients with melanocytic nevi, primary and metastatic melanoma. Then sections cut from ngTMA-block were immunohistochemically stained with proper antibodies. Expression of these proteins was investigated using automated image analysis and compared among the study groups. Results We analyzed the tissue of 238 patients with following diagnoses: 101 (42.4%) with a diagnosis of nevus, 86 (36.1%) Malignant melanoma and 51 (21.4%) metastasis. Median follow-up time for the malignant lesions was 5.71 years. Among the tested antigen, VEGF-C (p = 0.016), VEGF-R2 (p<0.001) and VEGF-R3 (p = 0.002) were significantly higher expressed in the metastatic tissues. When these scores were assessed in multiple regression models, the only independent factor linked to patient’s diagnosis was VEGF-R2 (p<0.001). In addition, groups of highly correlated variables (VEGF-C and VEGF-R3, VEGF-A and VEGF-R1) were found to form separate sub-clusters. On the other side, high values of VEGF-C were associated with both overall and disease-free survival with a statically significant HR of 2.76 (95% CI: 1.27, 5.98; p = 0.01) and 2.82 (95%CI: 1.62, 4.91; p<0.001), respectively. Conclusions This study shows that VEGF-C and VEGF-R2 might represent new prognostic marker in MM. However, further prospective studies are warranted to test their real efficacy as a prognostic marker.
FNFT: A Software Library for Computing Nonlinear Fourier Transforms

The conventional Fourier transform was originally developed in order to solve the heat equation, which is a standard example for a linear evolution equation. Nonlinear Fourier transforms (NFTs)1 are generalizations of the conventional Fourier transform that can be used to solve certain nonlinear evolution equations in a similar way (Ablowitz et al. 1974). An important difference to the conventional Fourier transform is that NFTs are equationspecific. The Korteweg-de Vries (KdV) equation (Gardner et al. 1967) and the nonlinear Schroedinger equation (NSE) (Shabat and Zakharov 1972) are two popular examples for nonlinear evolution equations that can be solved using appropriate NFTs.