Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications

Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a two-stage detection pipeline paired with Vision API that provides real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP0.5 score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).
Random Walks with Variable Restarts for Negative-Example-Informed Label Propagation

Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively labeled samples generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily drive the walk away from regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable"variable restarts", that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. In addition to allowing variable restarts, CusTaRd samples negatively-labeled nodes from neighbors of positively-labeled nodes to better characterize the difference between positively and negatively labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on four network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn/readjust edge weights, and sampling of negatives from the close neighborhood of positives further improves predictive accuracy.
Fabrication of Efficient Single‐Emitter Plasmonic Patch Antennas by Deterministic In Situ Optical Lithography using Spatially Modulated Light

Single‐emitter plasmonic patch antennas are room‐temperature deterministic single‐photon sources, which exhibit highly accelerated and directed single‐photon emission. However, for efficient operation these structures require 3D nanoscale deterministic control of emitter positioning within the device, which is a demanding task, especially when emitter damage during fabrication is a major concern. To overcome this limitation, the deterministic room‐temperature in situ optical lithography protocol uses spatially modulated light to position a plasmonic structure nondestructively on any selected single‐emitter with 3D nanoscale control. Herein, the emission statistics of such plasmonic antennas that embed a deterministically positioned single colloidal CdSe/CdS quantum dot, which highlight acceleration and brightness of emission, are analyzed. It is demonstrated that the presented antenna induces a 1000‐fold effective increase in the absorption cross‐section, and, under high pumping, these antennas show nonlinearly enhanced emission.
On fractional Schrödinger equations with Hartree type nonlinearities

<abstract><p>Goal of this paper is to study the following doubly nonlocal equation</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document} $(- \Delta)^s u + \mu u = (I_\alpha*F(u))F'(u) \quad {\rm{in}}\;{\mathbb{R}^N}\qquad\qquad\qquad\qquad ({\rm{P}}) $ \end{document} </tex-math> </disp-formula></p> <p>in the case of general nonlinearities $ F \in C^1(\mathbb{R}) $ of Berestycki-Lions type, when $ N \geq 2 $ and $ \mu > 0 $ is fixed. Here $ (-\Delta)^s $, $ s \in (0, 1) $, denotes the fractional Laplacian, while the Hartree-type term is given by convolution with the Riesz potential $ I_{\alpha} $, $ \alpha \in (0, N) $. We prove existence of ground states of (P). Furthermore we obtain regularity and asymptotic decay of general solutions, extending some results contained in <sup>[<xref ref-type="bibr" rid="b23">23</xref>,<xref ref-type="bibr" rid="b61">61</xref>]</sup>. </p></abstract>
Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement

Reinforcement learning (RL) is a powerful data-driven control method that has been largely explored in autonomous driving tasks. However, conventional RL approaches learn control policies through trial-and-error interactions with the environment and therefore may cause disastrous consequences such as collisions when testing in real-world traffic. Offline RL has recently emerged as a promising framework to learn effective policies from previously-collected, static datasets without the requirement of active interactions, making it especially appealing for autonomous driving applications. Despite promising, existing offline RL algorithms such as Batch-Constrained deep Q-learning (BCQ) generally lead to rather conservative policies with limited exploration efficiency. To address such issues, this paper presents an enhanced BCQ algorithm by employing a learnable parameter noise scheme in the perturbation model to increase the diversity of observed actions. In addition, a Lyapunov-based safety enhancement strategy is incorporated to constrain the explorable state space within a safe region. Experimental results in highway and parking traffic scenarios show that our approach outperforms the conventional RL method, as well as state-of-the-art offline RL algorithms.
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

The common practice for training commonsense models has gone from–human–to–corpus–to–machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from–machine–to–corpus–to–machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge Distillation (Hinton et al. 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically–as text–in addition to the neural model. We distill only one aspect–the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model. Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity. In addition, it results in a neural commonsense model that surpasses the teacher model’s commonsense capabilities despite its 100x smaller size. We apply this to the ATOMIC resource, and will share our new symbolic knowledge graph and commonsense models.
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs

We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs). This approach contrasts current approaches for"neural PDE solvers"that employ collocation-based methods to make point-wise predictions of solutions to PDEs. This approach has the advantage of naturally enforcing different boundary conditions as well as ease of invoking well-developed PDE theory -- including analysis of numerical stability and convergence -- to obtain capacity bounds for our proposed neural networks in discretized domains. We explore our mesh-based strategy, called NeuFENet, using a weighted Galerkin loss function based on the Finite Element Method (FEM) on a parametric elliptic PDE. The weighted Galerkin loss (FEM loss) is similar to an energy functional that produces improved solutions, satisfies a priori mesh convergence, and can model Dirichlet and Neumann boundary conditions. We prove theoretically, and illustrate with experiments, convergence results analogous to mesh convergence analysis deployed in finite element solutions to PDEs. These results suggest that a mesh-based neural network approach serves as a promising approach for solving parametric PDEs with theoretical bounds.
Impact of the TEAM Wheels eHealth manual wheelchair training program: Study protocol for a randomized controlled trial

Background Variable, and typically inadequate, delivery of skills training following manual wheelchair (MWC) provision has a detrimental impact on user mobility and participation. Traditional in-person delivery of training by rehabilitation therapists has diminished due to cost, travel time, and most recently social distancing restrictions due to COVID-19. Effective alternative training approaches include eHealth home training applications and interactive peer-led training using experienced and proficient MWC users. An innovative TEAM Wheels program integrates app-based self-training and teleconference peer-led training using a computer tablet platform. Objective This protocol outlines implementation and evaluation of the TEAM Wheels training program in a randomized control trial using a wait-list control group. Setting The study will be implemented in a community setting in three Canadian cities. Participants Individuals ≥ 18 years of age within one year of transitioning to use of a MWC. Intervention Using a computer tablet, participants engage in three peer-led teleconference training sessions and 75–150 minutes of weekly practice using a video-based training application over 4 weeks. Peer trainers individualize the participants’ training plans and monitor their tablet-based training activity online. Control group participants also receive the intervention following a 1-month wait-list period and data collection. Measurements Outcomes assessing participation; skill capacity and performance; self-efficacy; mobility; and quality of life will be measured at baseline and post-treatment, and at 6-month follow-up for the treatment group. Impact statement We anticipate that TEAM Wheels will be successfully carried out at all sites and participants will demonstrate statistically significant improvement in the outcome measures compared with the control group.
Imbalanced Activation of Wnt-/β-Catenin-Signaling in Liver Endothelium Alters Normal Sinusoidal Differentiation

Endothelial wingless-related integration site (Wnt)-/β-catenin signaling is a key regulator of the tightly sealed blood–brain barrier. In the hepatic vascular niche angiokine-mediated Wnt signaling was recently identified as an important regulator of hepatocyte function, including the determination of final adult liver size, liver regeneration, and metabolic liver zonation. Within the hepatic vasculature, the liver sinusoidal endothelial cells (LSECs) are morphologically unique and functionally specialized microvascular endothelial cells (ECs). Pathological changes of LSECs are involved in chronic liver diseases, hepatocarcinogenesis, and liver metastasis. To comprehensively analyze the effects of endothelial Wnt-/β-catenin signaling in the liver, we used endothelial subtype-specific Clec4g-iCre mice to generate hepatic ECs with overexpression of Ctnnb1. In the resultant Clec4g-iCretg/wt;Ctnnb1(Ex3)fl/wt (Ctnnb1OE−EC) mice, activation of endothelial Wnt-/β-catenin signaling resulted in sinusoidal transdifferentiation with disturbed endothelial zonation, that is, loss of midzonal LSEC marker lymphatic vessel endothelial hyaluronic acid receptor 1 (Lyve1) and enrichment of continuous EC genes, such as cluster of differentiation (CD)34 and Apln. Notably, gene set enrichment analysis revealed overrepresentation of brain endothelial transcripts. Activation of endothelial Wnt-/β-catenin signaling did not induce liver fibrosis or alter metabolic liver zonation, but Ctnnb1OE−EC mice exhibited significantly increased plasma triglyceride concentrations, while liver lipid content was slightly reduced. Ctnnb1 overexpression in arterial ECs of the heart has been reported previously to cause cardiomyopathy. As Clec4g-iCre is active in a subset of cardiac ECs, it was not unexpected that Ctnnb1OE−EC mice showed reduced overall survival and cardiac dysfunction. Altogether, balanced endothelial Wnt-/β-catenin signaling in the liver is required for normal LSEC differentiation and for maintenance of normal plasma triglyceride levels.
Recombinant CRAMP-producing Lactococcus lactis attenuates dextran sulfate sodium-induced colitis by colonic colonization and inhibiting p38/NF-κB signaling

Background Inflammatory bowel diseases (IBDs) are generally characterized by persistent abdominal pain and diarrhea caused by chronic inflammation in the intestine. Cathelicidins are antimicrobial peptides with pleiotropic roles in anti-infection, wound healing, and immune modulation. However, the sensitivity to the acidic environment and short half-life of cathelicidins limit their application in IBD treatment. Recombinant cathelicidin-related antimicrobial peptide (CRAMP)-producing Lactococcus lactis may represent a potential approach for IBD therapy. Objective The aim of this study was to develop recombinant CRAMP-producing L. lactis NZ9000 and explore the role and mechanism of recombinant L. lactis NZ9000 expressing CRAMP in colitis. Design We constructed two strains of CRAMP-producing L. lactis NZ9000 with different plasmids pMG36e (L.L-pMU45CR) or pNZ8148 (L.L-pNU45CR), which use a Usp45 secretion signal to drive the secretion of CRAMP. Bacterial suspensions were orally supplemented to mice with a syringe for 4 days after dextran sodium sulfate (DSS) treatment. Body weight change, disease active score, colon length, and colonic histology were determined. The expression of tight junction (ZO-1, ZO-2, and Occludin) and cytokines (IL-6, IL-1β, TNF-α, and IL-10) in colon was performed by qPCR. The expression of p-ERK, p-p38, and p-p65 was determined by Western blot analysis. Results Both CRAMP-producing L. lactis NZ9000 strains protected against colitis, as shown by reduced weight loss and disease activity score, improved colon shortening, and histopathological injury. In addition, CRAMP-producing L. lactis NZ9000 restored gut barrier by upregulating ZO-1, ZO-2, and occludin. Moreover, CRAMP-producing L. lactis NZ9000 regulated the colonic cytokines profile with reduced IL-6, IL-1β, and TNF-α production, and increased IL-10 production. By further analysis, we found that CRAMP-producing L. lactis NZ9000 reduced the expression of p-p38 and p-p65. Conclusions Together, our data suggested that CRAMP-secreting L. lactis NZ9000 attenuated dextran sulfate sodium-induced colitis by colonic colonization and inhibiting p38/NF-κB signaling. Orally administered recombinant CRAMP-secreting L. lactis NZ9000 represents a potential strategy for colitis therapy.
Measuring In-Vivo Foot Perfusion Distal to a Near-Circumferential Negative Pressure Wound Therapy Dressing via Thermal Imaging

Background Negative pressure wound therapy (NPWT) has been shown to promote the healing of acute and chronic wounds. In our previous study, we demonstrated that a near-circumferential NPWT dressing provided “lift-off” on an in-vitro extremity model resulting in decreased pressure. We hypothesized that this decrease in pressure may increase perfusion distal to the NPWT dressing by increasing lymphatic drainage and venous flow. Methods In this study, we tested if a near-circumferential NPWT dressing caused any appreciable skin movement around the dressing. We then used a thermal imaging camera to test if there was an increase in perfusion to the foot when a near-circumferential NPWT dressing was placed around the lower leg and tested at various negative pressures. Finally, we wanted to see if an artificial “lift-off” mechanism would lead to an increase in perfusion. Results The skin was noted to stretch between the short ends of the NPWT dressing, consistent with our previously described “lift-off” mechanism. However, there was no correlation between negative pressure and perfusion to the foot in the other experiments. Conclusion This study demonstrated that a near-circumferential NPWT dressing may not have any appreciable effects on perfusion when applied on a healthy patient, however, future studies are needed to determine if similar results would be seen on a traumatized or otherwise compromised extremity.
New insights from the MESA study: increased high-sensitivity troponins as a cardiovascular risk factor

Abstract The most recent high-sensitivity assays for troponins I and T (hs-TnI and hs-TnT) have made it possible to detect blood concentrations up to 10 times lower than previous assays, making troponins detectable even in asymptomatic subjects without manifest cardiovascular disease. For this reason, hs-Tn, initially introduced as markers of myocardial damage in an acute setting, have also become possible markers of subclinical myocardial damage in baseline conditions. In fact, recent evidence suggests that hs-TnT and hs-TnI predict the risk of future cardiovascular events also in the context of primary prevention, and offer incremental information when added to current risk stratification models. The different association highlighted with different outcome measures, such as coronary heart disease, atherosclerotic cardiovascular disease, heart failure, and death from all causes, seems to indicate that the risk observed in asymptomatic subjects with high levels of hs-Tn is an expression of subclinical damage secondary to multiple pathophysiological mechanisms, and not only to atherothrombosis. However, the ability of hs-TnT and hs-TnI (until now used interchangeably), to provide differential predictive information, and not redundant with respect to more traditional factors, remains to be definitively clarified, both for the purpose of predicting specific outcomes and for the implementation of specific preventive strategies. To date, evidences available allow us to hypothesize their role more as markers than as risk factors.
Malaria in the postpartum period causes damage to the mammary gland

Mastitis is an inflammation of the mammary gland in the breast and is typically due to bacterial infection. In malaria-endemic areas, mastitis with accompanying fever can be challenging to differentiate from malaria. At the same time, it is unclear whether malaria infection is directly involved in the development of mastitis. In the present study, whether mastitis develops during infection with malaria parasites was investigated using a rodent malaria model with Plasmodium berghei (P. berghei; Pb) ANKA. The course of parasitemia in postpartum mice infected with Pb ANKA was similar to the course in infected virgin mice. However, infected postpartum mice died earlier than did infected virgin mice. In addition, the weight of pups from mice infected with Pb ANKA was significantly reduced compared with pups from uninfected mice. The macroscopic and histological analyses showed apparent changes, such as destruction of the alveolus wall and extensive presence of leukocytes, in mammary gland tissue in mice infected during the postpartum period. The findings suggest that women during the postpartum period are more vulnerable to complications when infected with malaria parasites, particularly women who do not acquire protective immunity against malaria parasites. Based on the proteomic analysis, IFN-γ signaling pathway-related proteins in mammary gland tissue of the infected postpartum mice were increased. Our results indicate that inflammation induced by IFN-γ, a proinflammatory cytokine, may contribute to negative histological changes in mammary gland tissue of postpartum mice infected with Pb ANKA. In IFN-γ receptor 1-deficient (IFNGR1-KO) mice, the histological changes in mammary gland tissue of the infected postpartum wild-type mice were improved to almost normal mammary gland structure. Furthermore, weight loss in pups delivered by infected IFNGR1-KO postpartum mice was not observed. Taken together, these findings indicate that inflammation induced by IFN-γ is associated with development of mastitis in postpartum mice infected with Pb ANKA. The present study results may increase our understanding of how disease aggravation occurs during postpartum malaria.
Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3–1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300–54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2–0.9) additional cases and hospitalizations peaking at 12,000 (3,700–27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA’s ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.