Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing License Plates in Real-Time

License plate detection and recognition (LPDR) is of growing importance for enabling intelligent transportation and ensuring the security and safety of the cities. However, LPDR faces a big challenge in a practical environment. The license plates can have extremely diverse sizes, fonts and colors, and the plate images are usually of poor quality caused by skewed capturing angles, uneven lighting, occlusion, and blurring. In applications such as surveillance, it often requires fast processing. To enable real-time and accurate license plate recognition, in this work, we propose a set of techniques: 1) a contour reconstruction method along with edge-detection to quickly detect the candidate plates; 2) a simple zero-one-alternation scheme to effectively remove the fake top and bottom borders around plates to facilitate more accurate segmentation of characters on plates; 3) a set of techniques to augment the training data, incorporate SIFT features into the CNN network, and exploit transfer learning to obtain the initial parameters for more effective training; and 4) a two-phase verification procedure to determine the correct plate at low cost, a statistical filtering in the plate detection stage to quickly remove unwanted candidates, and the accurate CR results after the CR process to perform further plate verification without additional processing. We implement a complete LPDR system based on our algorithms. The experimental results demonstrate that our system can accurately recognize license plate in real-time. Additionally, it works robustly under various levels of illumination and noise, and in the presence of car movement. Compared to peer schemes, our system is not only among the most accurate ones but is also the fastest, and can be easily applied to other scenarios.
Microbially Induced Calcium Carbonate Precipitation (MICP) and Its Potential in Bioconcrete: Microbiological and Molecular Concepts

In this review, microbiological and molecular concepts of Microbially induced Calcium Carbonate Precipitation (MICP) and their role in bioconcrete are discussed. MICP is a widespread biochemical process in soils, caves, freshwater, marine sediments and hypersaline habitats. MICP is an outcome of metabolic interactions between diverse microbial communities with organic and/or inorganic compounds present in environment. Some of the major metabolic processes involved in MICP at different levels are urea hydrolysis, denitrification, dissimilatory sulfate reduction and photosynthesis. Currently, MICP directed by urea hydrolysis, denitrification and dissimilatory sulfate reduction has been reported to aid in development of bioconcrete and demonstrated improvement in mechanical and structural properties of concrete. Bioconcrete is a promising sustainable technology in reducing the negative environmental impacts due to CO2 emission from construction sector and as well as in terms of economic benefits by way of promoting self-healing process of the concrete structures. Among the metabolic processes mentioned above, urea hydrolysis is the most applied in concrete repair mechanisms. MICP by urea hydrolysis is induced by a series of reactions driven by urease (Ur) and carbonic anhydrase (CA). Catalytic activity of these two enzymes depends on diverse parameters, which are currently being studied under laboratory conditions to understand the biochemical mechanisms involved and their regulation in microorganisms. It is clearly evident that microbiological and molecular components are essential to improve the process and performance of bioconcrete.
Three product formulas for ratios of tiling counts of hexagons with collinear holes

Rosengren found an explicit formula for a certain weighted enumeration of lozenge tilings of a hexagon with an arbitrary triangular hole. He pointed out that a certain ratio corresponding to two such regions has a nice product formula. In this paper, we generalize this to hexagons with arbitrary collinear holes. It turns out that, by using same approach, we can also generalize Ciucu's work on the number and the number of centrally symmetric tilings of a hexagon with a fern removed from its center. This proves a recent conjecture of Ciucu.
Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep Learning

Computed tomography (CT) is a widely used imaging modality for medical diagnosis and treatment. In electroencephalography (EEG), CT imaging is necessary for co-registering with magnetic resonance imaging (MRI) and for creating more accurate head models for the brain electrical activity due to better representation of bone anatomy. Unfortunately, CT imaging exposes patients to potentially harmful sources of ionizing radiation. Image synthesis methods present a solution for avoiding extra radiation exposure. In this paper, we perform image synthesis to create a realistic, synthetic CT image from MRI of the same subject, and we present a comparison of different image synthesis techniques. Using a dataset of 30 paired MRI and CT image volumes, our results compare image synthesis using deep neural network regression, state-of-the-art adversarial deep learning, as well as atlas-based synthesis utilizing image registration. We also present a novel synthesis method that combines multi-atlas registration as a prior to deep learning algorithms, in which we perform a weighted addition of synthetic CT images, derived from atlases, to the output of a deep neural network to obtain a residual type of learning. In addition to evaluating the quality of the synthetic CT images, we also demonstrate that image synthesis methods allow for more accurate bone segmentation using the synthetic CT imaging than would otherwise be possible by segmenting the bone in the MRI directly.
Structural analysis of nuclear spin clusters via two-dimensional nanoscale nuclear magnetic resonance spectroscopy

Two-dimensional Nuclear Magnetic Resonance (NMR) is essential in molecular structure determination. The Nitrogen-Vacancy (NV) center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale. In this work, we develop a scheme for two-dimensional nanoscale NMR spectroscopy based on quantum controls on an NV center. We carry out a proof of principle experiment on a target of two coupled $^{13}$C nuclear spins in diamond. A COSY-like sequences is used to acquire the data on time domain, which is then converted to frequency domain with the fast Fourier transform (FFT). With the two-dimensional NMR spectrum, the structure and location of the set of nuclear spin are resolved. This work marks a fundamental step towards resolving the structure of a single molecule.
Promoting Adherence to Influenza Vaccination Recommendations in Pediatric Practice

Objectives: In the United States, nonadherence to seasonal influenza vaccination guidelines for children and adolescents is common and results in unnecessary morbidity and mortality. We conducted a quality improvement project to improve vaccination rates and test effects of 2 interventions on vaccination guidelines adherence. Methods: We conducted a cluster randomized control trial with 11 primary care practices (PRACTICE) that provided care for 11 293 individual children and adolescents in a children’s health care system from September 2015 through April 2016. Practice sites (with their clinicians) were randomly assigned to 4 arms (no intervention [Control], computerized clinical decision support system [CCDSS], web-based training [WBT], or CCDSS and WBT [BOTH]). Results: During the study, 55.8% of children and adolescents received influenza vaccination, which improved modestly during the study period compared with the prior influenza season (P = .009). Actual adherence to recommendations, including dosing, timeliness, and avoidance of missed opportunities, was 46.4% of patients cared for by the PRACTICE. The WBT was most effective in promoting adherence with vaccination recommendations with an estimated average odds ratio = 1.26, P < . 05, to compare between preintervention and intervention periods. Over the influenza season, there was a significantly increasing trend in odds ratio in the WBT arm (P < .05). Encouraging process improvements and providing longitudinal feedback on monthly rate of vaccination sparked some practice changes but limited impact on outcomes. Conclusions: Web-based training at the start of influenza season with monthly reports of adherence can improve correct dose and timing of influenza vaccination with modest impact on overall vaccination rate.
General Sleep Disturbance Scale: Translation, cultural adaptation, and psychometric properties of the Italian version

ABSTRACT Objective: The aim of this study was to translate, culturally adapt, and test the psychometric characteristics of the General Sleep Disturbance Scale (GSDS) and to detect and explore the presence of sleep disorders in the healthy Italian population. Methods: Reliability was assessed through Cronbach’s alpha and intraclass correlation coefficient; concurrent validity was assessed with the Pittsburgh Sleep Quality Index (PSQI). Results: The scale was administered to 426 subjects recruited all over Italy. The Italian GSDS was found to have good internal consistency (Cronbach’s α was 0.77) and a good test-retest reliability (ICC of 0.78). The optimal cut-off point for the GSDS-IT was 38.5. Conclusion: The GSDS-IT is a valid and reliable tool, which can be completed in less than five minutes and is a noninvasive method for measuring sleep disturbances consistent with the DSM-V insomnia criteria, during a past week.
Principled Training of Neural Networks with Direct Feedback Alignment

The backpropagation algorithm has long been the canonical training method for neural networks. Modern paradigms are implicitly optimized for it, and numerous guidelines exist to ensure its proper use. Recently, synthetic gradients methods -where the error gradient is only roughly approximated - have garnered interest. These methods not only better portray how biological brains are learning, but also open new computational possibilities, such as updating layers asynchronously. Even so, they have failed to scale past simple tasks like MNIST or CIFAR-10. This is in part due to a lack of standards, leading to ill-suited models and practices forbidding such methods from performing to the best of their abilities. In this work, we focus on direct feedback alignment and present a set of best practices justified by observations of the alignment angles. We characterize a bottleneck effect that prevents alignment in narrow layers, and hypothesize it may explain why feedback alignment methods have yet to scale to large convolutional networks.
Gastrointestinal Sensing of Meal-Related Signals in Humans, and Dysregulations in Eating-Related Disorders

The upper gastrointestinal (GI) tract plays a critical role in sensing the arrival of a meal, including its volume as well as nutrient and non-nutrient contents. The presence of the meal in the stomach generates a mechanical distension signal, and, as gastric emptying progresses, nutrients increasingly interact with receptors on enteroendocrine cells, triggering the release of gut hormones, with lipid and protein being particularly potent. Collectively, these signals are transmitted to the brain to regulate appetite and energy intake, or in a feedback loop relayed back to the upper GI tract to further adjust GI functions, including gastric emptying. The research in this area to date has provided important insights into how sensing of intraluminal meal-related stimuli acutely regulates appetite and energy intake in humans. However, disturbances in the detection of these stimuli have been described in a number of eating-related disorders. This paper will review the GI sensing of meal-related stimuli and the relationship with appetite and energy intake, and examine changes in GI responses to luminal stimuli in obesity, functional dyspepsia and anorexia of ageing, as examples of eating-related disorders. A much better understanding of the mechanisms underlying these dysregulations is still required to assist in the development of effective management and treatment strategies in the future.
LocLets: Localized Graph Wavelets for Processing Frequency Sparse Signals on Graphs

In this article, a new family of graph wavelets, abbreviated LocLets for Localized graph waveLets, is introduced. These wavelets are localized in the Fourier domain on subsets of the graph Laplacian spectrum. LocLets are built upon the Spectral Graph Wavelet Transform (SGWT) and adapt better to signals that are sparse in the Fourier domain than standard SGWT. In fact, as a refinement of SGWT, LocLets benefits from the Chebyshev's machinery to ensure the LocLets transform remains an efficient and scalable tool for signal processing on large graphs. In addition, LocLets exploits signals sparsity in various ways: compactness, efficiency and ease of use of the transform are improved for sparse signals in the Fourier domain. As typical examples of such sparse signals, there are smooth and highly non-smooth signals. For these latter signals, their mixtures or even a wider class of signals, it is shown in this paper that LocLets provide substantial improvements in standard noise reduction tasks compared to advanced graph-wavelet based methods.
Blood Pressure Change from Normal to 2017 ACC/AHA Defined Stage 1 Hypertension and Cardiovascular Risk

The purpose of this study was to investigate the clinical significance of the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) defined stage 1 hypertension (systolic blood pressure (SBP) 130–139 mmHg or diastolic blood pressure (DBP) 80–89 mmHg), and increase in BP from previously normal BP in Korean adults. We conducted a retrospective analysis of 60,866 participants from a nationally representative claims database. Study subjects had normal BP (SBP < 120 mmHg and DBP < 80 mmHg), no history of anti-hypertensive medication, and cardiovascular disease (CVD) in the first period (2002–2003). The BP change was defined according to the BP difference between the first and second period (2004–2005). We used time-dependent Cox proportional hazards models in order to evaluate the effect of BP elevation on mortality and CVD with a mean follow-up of 7.8 years. Compared to those who maintained normal BP during the second period, participants with BP elevation from normal BP to stage 1 hypertension had a higher risk for CVD (adjusted hazard ratio (aHR) 1.23; 95% confidence interval (CI), 1.08–1.40), and ischemic stroke (aHR 1.32; 95% CI, 1.06–1.64). BP elevation to 2017 ACC/AHA defined elevated BP (SBP 120–129 mmHg and DBP < 80 mmHg) was associated with an increased risk of CVD (aHR 1.26; 95% CI, 1.06–1.50), but stage 1 isolated diastolic hypertension (SBP < 130 and DBP 80–89 mmHg) was not significantly related with CVD risk (aHR 1.12; 95% CI, 0.95–1.31).
Topological derivative-based topology optimization of structures subject to multiple load-cases

The topological derivative measures the sensitivity of a shape functional with respect to an infinitesimal singular domain perturbation, such as the insertion of holes, inclusions or source-terms. The topological derivative has been successfully applied in obtaining the optimal topology for a large class of physics and engineering problems. In this paper the topological derivative is applied in the context of topology optimization of structures subject to multiple load-cases. In particular, the structural compliance under plane stress or plane strain assumptions is minimized under volume constraint. For the sake of completeness, the topological asymptotic analysis of the total potential energy with respect to the nucleation of a small circular inclusion is developed in all details. Since we are dealing with multiple load-cases, a multi-objective optimization problem is proposed and the topological sensitivity is obtained as a sum of the topological derivatives associated with each load-case. The volume constraint is imposed through the Augmented Lagrangian Method. The obtained result is used to devise a topology optimization algorithm based on the topological derivative together with a level-set domain representation method. Finally, several finite element-based examples of structural optimization are presented.
Generalized Lorentz reciprocal theorem in complex fluids and in non-isothermal systems

The classical Lorentz reciprocal theorem (LRT) was originally derived for slow viscous flows of incompressible Newtonian fluids under the isothermal condition. In the present work, we extend the LRT from simple to complex fluids with open or moving boundaries that maintain non-equilibrium stationary states. In complex fluids, the hydrodynamic flow is coupled with the evolution of internal degrees of freedom such as the solute concentration in two-phase binary fluids and the spin in micropolar fluids. The dynamics of complex fluids can be described by local conservation laws supplemented with local constitutive equations satisfying Onsager’s reciprocal relations (ORR). We consider systems in quasi-stationary states close to equilibrium, controlled by the boundary variables whose evolution is much slower than the relaxation in the system. For these quasi-stationary states, we derive the generalized LRT and global Onsager’s reciprocal relations (GORR) for the slow variables at boundaries. This establishes the connection between ORR for local constitutive equations and GORR for constitutive equations at boundaries. Finally, we show that the LRT can be further extended to non-isothermal systems by considering as an example the thermal conduction in solids and still fluids.
Two-dimensional partial cubes

We investigate the structure of two-dimensional partial cubes, i.e., of isometric subgraphs of hypercubes whose vertex set defines a set family of VC-dimension at most 2. Equivalently, those are the partial cubes which are not contractible to the 3-cube $Q_3$ (here contraction means contracting the edges corresponding to the same coordinate of the hypercube). We show that our graphs can be obtained from two types of combinatorial cells (gated cycles and gated full subdivisions of complete graphs) via amalgams. The cell structure of two-dimensional partial cubes enables us to establish a variety of results. In particular, we prove that all partial cubes of VC-dimension 2 can be extended to ample aka lopsided partial cubes of VC-dimension 2, yielding that the set families defined by such graphs satisfy the sample compression conjecture by Littlestone and Warmuth (1986) in a strong sense. The latter is a central conjecture of the area of computational machine learning, that is far from being solved even for general set systems of VC-dimension 2. Moreover, we point out relations to tope graphs of COMs of low rank and region graphs of pseudoline arrangements.
Increasing Image Memorability with Neural Style Transfer

Recent works in computer vision and multimedia have shown that image memorability can be automatically inferred exploiting powerful deep-learning models. This article advances the state of the art in this area by addressing a novel and more challenging issue: “Given an arbitrary input image, can we make it more memorable? ” To tackle this problem, we introduce an approach based on an editing-by-applying-filters paradigm: given an input image, we propose to automatically retrieve a set of “style seeds,” i.e., a set of style images that, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state-of-the-art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability.
Post-silking Phosphorus Recycling and Carbon Partitioning in Maize Under Low to High Phosphorus Inputs and Their Effects on Grain Yield

Phosphorus (P) recycling and carbon partitioning are crucial determinants of P-use efficiency and grain yield in maize (Zea mays), while a full understanding of how differences in P availability/plant P status affect these two processes underlying yield formation remains elusive. Field experiments were conducted for 3 years to investigate the maize growth, P remobilization, and carbohydrate accumulation in leaves and developing ears of plants receiving low to high P inputs. In plots that 75 kg P2O5 ha-1 and above was applied (corresponding to 7.5 mg kg-1 and higher Olsen-P concentration in 0–20 cm soil layer), no additional response occurred in leaf area, ear growth, and grain yield. Despite the higher P uptake with P fertilization above this threshold, the lack of additional plant growth and yield resulted in decreased P-use efficiency. Regardless of P application rates, P remobilization to the ear during the first half of the grain filling phase preferentially came from the stem (50–76%) rather than from leaves (30–44%), and with a greater proportion in the inorganic P (Pi) form over organic P fractions. Leaf photosynthesis was maintained under P-limiting conditions due to the greater P investment in organic P pools than Pi. More and larger starch granules were found in the bundle sheath cells at silking or 21 days after silking (DAS) than under P-sufficient conditions. The amount of total carbohydrate production and export was lower in the P-deficient plants than the high-P plants, corresponding to decreased leaf size and lifespan. Nonetheless, similar or significantly greater starch levels were observed in both cob and kernels at silking and 21 DAS, implying there was an adequate carbohydrate supply to the developing ears under the diminished kernel sink of the P starvation. In addition, there was a strong correlation between the accumulation rates of carbon and remobilized P in the developing kernels, as well as between carbon and total P. Overall, the results indicated that diminished sink size and lower capacity of carbon deposition may limit yield formation in P-deficient maize, which in turn imposes a feedback regulation on reducing carbon and P remobilization from source leaves. An integrated framework considering post-silking P recycling and carbon partitioning in maize and their effects on grain yield has been proposed.
Extracting Interpretable Concept-Based Decision Trees from CNNs

In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer activations and interpret the concepts through a shallow decision tree. The decision tree can provide information about which concepts a model deems important, as well as provide an understanding of how the concepts interact with each other. Experiments demonstrate that the extracted decision tree is capable of accurately representing the original CNN's classifications at low tree depths, thus encouraging human-in-the-loop understanding of discriminative concepts.