Miracle-dz/UniFAR-FTUs huggingface dataset:
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.
LOCAL FOOD PERCEPTIONS AND SHOPPING BEHAVIORS IN RURAL NORTH CAROLINA: A NOVEL, RETAIL-BASED APPROACH FOR PROMOTING HEALTH?

Background: The prevalence of obesity and chronic disease remains high in the United States despite decades of public health research aiming to improve dietary quality. People of lower socioeconomic status (SES) and residents of rural areas are particularly at risk. The food environment is a key mediator of diet-related health disparities. There is evidence associating local food purchasing with healthier eating behaviors. However, little is known about whether lower-SES and rural consumers have positive associations with local food and would preferentially buy it in the grocery store context. Objective: To understand the perceptions and values around locally grown foods among rural and lower-socioeconomic status residents of North Carolina. Methods: Semi-structured in-depth interviews were conducted with a convenience sample of 22 frequent shoppers at three rural grocery stores to (1) identify what factors influence their food purchasing decisions in general and local foods in particular, (2) characterize shoppers’ perceptions of “local” foods, and (3) identify types of store-based interventions that these consumers believe might have potential for increasing the proportion of local foods they purchase. Results: Participants varied in their definitions for “local” food; most used geographic or political boundaries, and some felt that the definition would change depending on the retail outlet. Farmers’ markets and farm stands were perceived to source produce “more locally” than
Maps of the Number of H i Clouds along the Line of Sight at High Galactic Latitude

Characterizing the structure of the Galactic interstellar medium (ISM) in three dimensions is of high importance for accurate modeling of dust emission as a foreground to the cosmic microwave background (CMB). At high Galactic latitude, where the total dust content is low, accurate maps of the 3D structure of the ISM are lacking. We develop a method to quantify the complexity of the distribution of dust along the line of sight with the use of H i line emission. The method relies on a Gaussian decomposition of the H i spectra to disentangle the emission from overlapping components in velocity. We use this information to create maps of the number of clouds along the line of sight. We apply the method to (a) the high Galactic latitude sky and (b) the region targeted by the BICEP/Keck experiment. In the north Galactic cap we find on average three clouds per 0.2 square degree pixel, while in the south the number falls to 2.5. The statistics of the number of clouds are affected by intermediate-velocity clouds (IVCs), primarily in the north. IVCs produce detectable features in the dust emission measured by Planck. We investigate the complexity of H i spectra in the BICEP/Keck region and find evidence for the existence of multiple components along the line of sight. The data (doi: 10.7910/DVN/8DA5LH) and software are made publicly available and can be used to inform CMB foreground modeling and 3D dust mapping.
Nass: A New Approach to Graph Similarity Search

In this paper, we study the problem of graph similarity search with graph edit distance (GED) constraints. Due to the NP-hardness of GED computation, existing solutions to this problem adopt the filtering-and-verification framework with a main focus on the filtering phase to generate a small number of candidate graphs. However, they have a limitation that the number of candidates grows extremely rapidly as a GED threshold increases. To address the limitation, we propose a new approach that utilizes GED computation results in generating candidate graphs. The main idea is that whenever we identify a result graph of the query, we immediately regenerate candidate graphs using a subset of pre-computed graphs similar to the identified result graph. To speed up GED computation, we also develop a novel GED computation algorithm. The proposed algorithm reduces the search space for GED computation by utilizing a series of filtering techniques, which have been used to generate candidates in existing solutions. Experimental results on real datasets demonstrate the proposed approach significantly outperforms the state-of-the art techniques.
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes

We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that combines two types of convolutions. The first type, Geodesic convolutions, defines the kernel weights over mesh surfaces or graphs. That is, the convolutional kernel weights are mapped to the local surface of a given mesh. The second type, Euclidean convolutions, is independent of any underlying mesh structure. The convolutional kernel is applied on a neighborhood obtained from a local affinity representation based on the Euclidean distance between 3D points. Intuitively, geodesic convolutions can easily separate objects that are spatially close but have disconnected surfaces, while Euclidean convolutions can represent interactions between nearby objects better, as they are oblivious to object surfaces. To realize a multi-resolution architecture, we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations. We experimentally show that combining both types of convolutions in our architecture leads to significant performance gains for 3D semantic segmentation, and we report competitive results on three scene segmentation benchmarks. Models and code will be made publicly available.
A note on partially-greedy bases in quasi-Banach spaces

We continue with the study of greedy-type bases in quasi-Banach spaces started in [3]. In this paper, we study partially-greedy bases focusing our attention in two main results: -Characterization of partially-greedy bases in quasi-Banach spaces in terms of quasi-greediness and different conservative-like properties. -Given a $C$-partially-greedy basis in a quasi-Banach space, there exists a "renorming" such that the basis is $1$-partially-greedy.
Photodynamic therapy platform for glioblastoma and intrabronchial tumors

Photodynamic therapy (PDT) is bringing new, effective, and less invasive, possibilities for cancer treatment. ML7710 (Modulight Inc.) medical laser system offers a platform for performing PDT for multiple indications and drugs. Latest avenue is glioblastoma treatment with 5-Aminolevulinic acid (ALA-5) and 635-nm light, where clinical trials are about to begin. Preliminary work suggests major advantages in treatment control, including active in-situ feedback. ML7710 platform has already proven itself for clinical work with intrabronchial obstructive tumors. Preliminary result with 10 patients show that intrabronchial tumors, that strongly affect both the survival and the performance of the patient, can be significantly reduced with ML7710 operated at 665 nm and sodium chlorine E6 photosensitizer. The aim in most of the patients has been a palliative recanalization of the bronchial lumen in order to alleviate the symptoms such as breathlessness and hemoptysis. The illumination dose for the target area was 50–75 J/cm2. All the patients have received multimodality cancer treatment using other intrabronchial interventions, radiotherapy and chemotherapy as needed. In most of the patients, satisfactory treatment results were achieved and it was possible to restart chemotherapy in several patients. In one patient with local cancer a complete remission was established. PDT has also the advantage that it is possible to give PDT after a maximum dose of radiation therapy has already been used and fewer side effects if used in locally advanced intraluminar lung cancer.
Geometry of variational methods: dynamics of closed quantum systems

We present a systematic geometric framework to study closed quantum systems based on suitably chosen variational families. For the purpose of (A) real time evolution, (B) excitation spectra, (C) spectral functions and (D) imaginary time evolution, we show how the geometric approach highlights the necessity to distinguish between two classes of manifolds: Kahler and non-Kahler. Traditional variational methods typically require the variational family to be a Kahler manifold, where multiplication by the imaginary unit preserves the tangent spaces. This covers the vast majority of cases studied in the literature. However, recently proposed classes of generalized Gaussian states make it necessary to also include the non-Kahler case, which has already been encountered occasionally. We illustrate our approach in detail with a range of concrete examples where the geometric structures of the considered manifolds are particularly relevant. These go from Gaussian states and group theoretic coherent states to generalized Gaussian states.
Deflection of the hypervelocity stars by the dance of the Milky Way and Large Magellanic Cloud

Stars slingshotted by the supermassive black hole at the Galactic centre will escape the Milky Way so quickly that their trajectories will be almost straight lines. Previous works have shown how these `hypervelocity stars' are subsequently deflected by the gravitational field of the Milky Way and the Large Magellanic Cloud (LMC), but have neglected to account for the reflex motion of the Milky Way in response to the fly by of the LMC. A consequence of this motion is that the hypervelocity stars we see on the outskirts of the Milky Way today were ejected from where the Milky Way centre was hundreds of millions of years ago. This change in perspective causes large apparent deflections in the trajectories of the hypervelocity stars, which are of the same order as the deflections caused by the gravitational force of the Milky Way and LMC. We quantify these deflections by simulating the production of hypervelocity stars in an isolated Milky Way (with a spherical or flattened dark matter halo), in a fixed-in-place Milky Way with a passing LMC, and in a Milky Way which responds to the passage of the LMC. The proper motion precision necessary to measure these deflections will be possible with the combination of Gaia with the proposed GaiaNIR successor mission, and these measurements will unlock the hypervelocity stars as probes of the shape of the Milky Way, the mass of the LMC, and of the dance of these two galaxies.
An introduction to nutrition for an optometrist

So what is a healthy balanced diet? To maintain our general health, weight and bodily functions we must have a steady inflow of oxygen, water, energy and nutrients balanced with an outflow of carbon dioxide, water, solutes and solids, as shown in Figure 1. It is important to remember, however, that the level of each input and output needed to maintain health and weight can vary between individuals due to numerous factors.
Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network

Background Using deep learning techniques in image analysis is a dynamically emerging field. This study aims to use a convolutional neural network (CNN), a deep learning approach, to automatically classify esophageal cancer (EC) and distinguish it from premalignant lesions. Methods A total of 1,272 white-light images were adopted from 748 subjects, including normal cases, premalignant lesions, and cancerous lesions; 1,017 images were used to train the CNN, and another 255 images were examined to evaluate the CNN architecture. Our proposed CNN structure consists of two subnetworks (O-stream and P-stream). The original images were used as the inputs of the O-stream to extract the color and global features, and the pre-processed esophageal images were used as the inputs of the P-stream to extract the texture and detail features. Results The CNN system we developed achieved an accuracy of 85.83%, a sensitivity of 94.23%, and a specificity of 94.67% after the fusion of the 2 streams was accomplished. The classification accuracy of normal esophagus, premalignant lesion, and EC were 94.23%, 82.5%, and 77.14%, respectively, which shows a better performance than the Local Binary Patterns (LBP) + Support Vector Machine (SVM) and Histogram of Gradient (HOG) + SVM methods. A total of 8 of the 35 (22.85%) EC lesions were categorized as premalignant lesions because of their slightly reddish and flat lesions. Conclusions The CNN system, with 2 streams, demonstrated high sensitivity and specificity with the endoscopic images. It obtained better detection performance than the currently used methods based on the same datasets and has great application prospects in assisting endoscopists to distinguish esophageal lesion subclasses.
The Influence Of Public Service Motivation On The Civil Servant Work Commitment

The results of this study are part of the results of a dissertation study with the theme of the influence of the work environment and empowerment on public service motivation and employee work commitments within the scope of the Southeast Sulawesi Provincial Office, Indonesia. This study aims to explore the effect of public service motivation (hereinafter abbreviated as PSM) on the work commitment of civil servants. To determine the effect of PSM (Attraction to Public Service, commitment to public values, compassion, and self-sacrifice) on work commitments (work engagement and job involvement) the structural equation modeling (SEM) model is used. The results found that PSM has a positive and significant effect on work commitment. These findings also have important implications for the development of the PSM concept in relation to work commitments, especially in the public sector.
A Unified Design & Development Framework for Mixed Interactive Systems

: Mixed reality, natural user interfaces and the internet of things are complementary computing paradigms. They converge towards news form of interactive systems named mixed interactive systems. Because of their exploding complexity, mixed interactive systems induce new challenges for designers and developers. We need new abstractions of these systems in order to describe their real-virtual interplay. We also need to break mixed interactive systems down into pieces in order to segment their complexity into comprehensible sub-systems. This paper presents a framework to enhance the design and development of these systems. We propose a model unifying the paradigms of mixed reality, natural user interfaces and the internet of things. Our model decomposes a mixed interactive system into a graph of mixed entities. Our framework implements this model, which facilitates interactions between users, mixed reality devices and connected objects. In order to demonstrate our approach, we present how designers and developers can use this framework to develop a mixed interactive system dedicated to smart building occupants.
Hospital Inpatient Caring By Markov Decision Process

Many challenges have been faced by the health care system involving high rates of drug-resistant and hospital-acquired disease, failures of care delivery leading to preventable adverse health events and skyrocketing costs. Over treatment, substandard implementation of health care and lack of success to get the premium practice for prophylactic supervision and safety of patient has enormous and has straight impact or measures on one and other outcome and costs of health care. Contradictorily, both the increase in electronic health data of patient’s availability and the development occurring of scientific approach for analyzing the details suggests its probability for making its utilization in terms of machine learning methods and artificial intelligence for improving standards and cost reduction for taking care of patient. It enable important application for patients to become primary aware about their disorders and provide doctors to have a viewing care on their patient even when they are out of sight from patient. IndexTerms: Electronic Health Record, Clinical Decision Support System, Transaction Success Probability, Markov Decision Process (MDP), Health Inpatient Care (HIC)
NAD(P)H: Quinone Oxidoreductase 1 (NQO1) C609T Gene Polymorphism Association with Digestive Tract Cancer: A

Digestive tract cancers are the most common malignant tumors worldwide, with three million new cases each year (Parkin et al., 2005; Kanavos, 2006), of which Esophageal carcinoma is the sixth leading cause of cancer death in the world (Enzinger et al., 2003), Gastric cancer is the fourth commonest cancer and the second commonest cause of cancer death, globally (Ferlay et al., 2008), colorectal cancer is also the third most common cancer in males and the second in females (Jemal et al., 2011). Despite advances in surgery and chemotherapy, it still has a higher mortality rates. The mechanism of DTC is still unclear. The carcinogenesis of DTC is a complex, multifactorial, and multistep event, in which many factors are implicated, such as genetic factors, cigarette smoking, heavy alcohol drinking, and poor dietary pattern (Compare et al., 2010). Except for these shared risk factors, different primary sites of DTC cancers have different risk factors and thus different etiologies. For example, Helicobacter pylori infection is involved in gastric cancer (Asombang et al., 2012), Barrett’s esophagus is a key risk factor for
Diagnosis Of Autism Using Voxel Based Morphometry Features Of Brain Images

— The advancements in neuroimaging have resulted in huge amount of data and hence automatic tools are required to identify patterns, reduce noise and enhance the knowledge about the functioning of brain. This paper also proposes has a new approach for detecting major differences in brain structures and activities between autistic and healthy individuals using Autism Spectrum Disorder (ASD) from structural Magnetic Resonance Imaging (sMRI) and functional MRI (fMRI) magnetic Resonance Imaging MRI. The preprocessing of sMRI and fMRI is carried out using Statistical Parametric Mapping (SPM) toolbox. The voxel based morphometry analysis results obtained from preprocessing are used as features which help in differentiating the autistic and healthy individuals. These features are given as input to Support Vector Machine or Random Forests (RF) to classify the autistic and healthy individuals. The sMRI and fMRI images used in this research work are obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results prove that the proposed computer aided diagnosis (CAD) system gives good performance in terms of classification accuracy.
Customer Segmentation Based on GRFM: Case Study

in the last decades' firms which have directly or indirectly contact with a customer migrate from product-oriented to be a customer-oriented, hence, some products and customers are not profitable in the same way and some of them bring detriment to the firm. In this regard, firms should recognize loyal, profitable and potential customers with a glance of impressive product which brings added value for them. In order to distinguish profitable customers, they supposed to cluster customers and study their behavior’s group for the sake of having the best investment in the best segment. In this paper, we utilize customize GRFM (Group RFM) to cluster customers based on proposed APC (account-pattern constraint clustering) algorithm. Hence, we calculate the cluster RFM value which could aid the bank to explore both profitable accounts and customers. Keywords-Component; Data Mining; Constraint Clustering Algorithms; Segmentation; RFM.
Correlation of carotid intimal media thickness with activity and duration of Rheumatoid arthritis using carotid Doppler Ultrasonography

Background: Cardiovascular cause is the leading cause of mortality in RA and this has been attributed to accelerated atherosclerosis. Indirect evidence of accelerated atherosclerosis in RA comes from studies using carotid artery intima media thickness (CIMT) as a marker of atherosclerotic burden and cardiovascular risk. Aims: To assess carotid intima-media thickness in patients with rheumatoid arthritis by using Doppler ultrasound and to study the correlation between carotid intima-media thickness and the duration and severity of rheumatoid arthritis. Materials and Methods: A total of 30 rheumatoid arthritis patients were enrolled during 2 year study. Patients satisfying the modified American Rheumatology Association criteria (1987) were included . Those with hypertension, cardiac disease, and diabetes mellitus were excluded. Subjects were divided into three groups (each group consist of 10 patients) based on disease duration. For measurement of carotid intimal medial thickness B-mode USG scan using 7.5 MHz probe is used. Results: The mean value of common carotid intima media thickness (CCIMT) was significantly higher in the study group (0.8 mm) when compared to control group (0.59 mm) (p value < 0.001). Total carotid intima media thickness (i.e., mean of total CIMT of CCA, ICA, and ECA) was significantly higher in the study group (0.76 mm) when compared to control group (0.57 mm) (p value < 0.001). Conclusion: The study shows a significant directly proportional relation between carotid intima media thickness to longer duration of disease.
Identification Sus scrofa and Mus musculus as Potential Parasitifers of SARS-CoV-2 via Phylogenetic and Homologous Recombination Analysis

Wuhan Huanan seafood wholesale market was highly suspected as the original place for the outbreak of SARS-CoV-2 previously. Most study focus on the wild animals being sold in the market, neglecting the livestock around the city Wuhan could be also reasonable suspect zero. Implementing phylogenetic and recombination analysis, both porcine and murine coronavirus may attend the reorganization and evolution of SARS-CoV-2. Overall, it's the first time to illustrate that swine and mice would be the probable reservoir for the SARS-CoV-2.