Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

—The detection of multiple curved lanes is still a challenge for Driver Assistance Systems (DAS) today, due to interferences such as road markings and shadows casted by road side structures and vehicles. The vanishing point ( V p ) contains the global information of the road image. Hence, V p based lane detection algorithms are quite insensitive to interference. However, when curved lanes are assumed, V p shifts with respect to the rows of the image. In this paper, a V p for each individual row of the image is estimated by ﬁrst extracting a V py (vertical position of the V p ) for each individual row of the image from v-disparity. Then, based on estimated V py s a 2D V px accumulator is efﬁciently formed. Thus, by globally optimizing this 2D V px accumulator, globally optimum V p s for road image is extracted. Then, estimated V p s are utilized for multiple curved lane detection on non-ﬂat surfaces. The resultant system achieves a detection rate of 99% in 1277 frames of 5 stereo vision test sequence.
TrafﬁcPredict: Trajectory Prediction for Heterogeneous Trafﬁc-Agents

To safely and efﬁciently navigate in complex urban trafﬁc, au- tonomous vehicles must make responsible predictions in relation to surrounding trafﬁc-agents (vehicles, bicycles, pedes- trians, etc.). A challenging and critical task is to explore the movement patterns of different trafﬁc-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime trafﬁc prediction algorithm, TrafﬁcPredict. Our ap- proach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the simi- larities of instances belonging to the same type to reﬁne the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and trafﬁc densities. The dataset includes many chal- lenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafﬁcPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior pre- diction methods.
Public Engagement and Organizational. Identity in U.S. Higher Education

The identities of U.S. colleges and universities are changing in response to the growing national movement promoting public engagement in higher education. Public engagement emphasizes a shift away from an expert model of delivering university knowledge to the public, toward a more collaborative model where stakeholders play a significant role in creating and sharing knowledge for the mutual benefit of institutions and society. This conceptual paper explores how U.S. colleges and universities are evolving in this emerging context. The paper begins with a brief history of U.S. higher education and the ways U.S. institutions traditionally have viewed their societal roles. Then, we discuss the emerging strategy of “public engagement” and how colleges and universities are institutionalizing engagement to reflect their unique missions, histories, and geographic settings. The section that follows conceptualizes ways in which institutions project their engagement identity to external stakeholders. We conclude by offering new directions for research to explore intersections of engagement and organizational identity in the U.S. context and around the world.
Going down the rabbit hole: a review on methods for population genomics in natural populations

1. Characterizing species history and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The ongoing and constant improvement of next-generation sequencing (NGS) techniques has facilitated the production of an increasingly growing amount of genetic markers across genomes of non-model species. 2. The study of variation at these markers across natural populations has deepened the understanding of how population history and selection act on genomes. However, this improvement has come with a burst of analytical tools that can confuse users. 3. This confusion can limit the amount of information effectively retrieved from complex genomic datasets. In addition, the lack of a unified analytical pipeline impairs the diffusion of the most recent analytical tools into fields like conservation biology. 4. This requires efforts be made in providing introduction to these methods. In this paper we describe possible analytical protocols and list more than 70 methods dealing with genome-scale datasets, clarify the strategy they use to infer demographic history and selection, and discuss some of their limitations.
Some Aspects of Inflationary Particle Production

Particle production by a varying mass term and by the background metric are considered. We derive a definition of particle number in kinetic theory for both, fermionic and scalar case, which we generalize to the situation of a flavour-mixing mass matrix. This allows us to endow the process of preheating with C and CP violation, leading to the coherent baryogenesis scenario. We present models where this mechanism is operative in the context of hybrid inflation and the grand unified theories Pati-Salam and SO(10). It is shown that a baryon asymmetry in accordance with observation may result. Moreover, we consider issues of quantum theory in curved space. Scalar fields in the expanding Universe and in Rindler space are discussed. It turns out that besides the particle detection rate, the Lamb shift of energy levels is an important effect experienced by Unruh's detector in these spacetimes.
The relative Mishchenko–Fomenko higher index and almost flat bundles. I. The relative Mishchenko–Fomenko index

In this paper, the first of two, we introduce an alternative definition of the Chang--Weinberger--Yu relative higher index, which is thought of as a relative analogue of the Mishchenko--Fomenko index pairing. A main result of this paper is that our map coincides with the existing relative higher index maps. We make use of this fact for understanding the relative higher index. First, we relate the relative higher index with the higher index of amalgamated free product groups. Second, we define the dual relative higher index map and show its rational surjectivity under certain assumptions.
Defying Gravity: The Complexity of the Hanano Puzzle

Using the notion of visibility representations, our paper establishes a new property of instances of the Nondeterministic Constraint Logic (NCL) problem (a PSPACE-complete problem that is very convenient to prove the PSPACE-hardness of reversible games with pushing blocks). Direct use of this property introduces an explosion in the number of gadgets needed to show PSPACE-hardness, but we show how to bring that number from 32 down to only three in general, and down to two in a specific case! We propose it as a step towards a broader and more general framework for studying games with irreversible gravity, and use this connection to guide an indirect polynomial-time many-one reduction from the NCL problem to the Hanano Puzzle -- which is NP-hard -- to prove it is in fact PSPACE-complete.
Fiber products and Morita duality for commutative rings

L’accès aux archives de la revue « Rendiconti del Seminario Matematico della Università di Padova » (http://rendiconti.math.unipd.it/) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
High-performance and low-loss plasmon waveguiding in graphene-coated nanowire with substrate

In this paper, we theoretically show that high-performance and low-loss mid-infrared plasmon waveguiding can be achieved by introducing a thin low-index buffer layer between a graphene-coated nanowire and a silicon substrate. It is found that the introduction of the buffer layer can mediate the coupling between the nanowire and substrate, and it can effectively suppress the leaky radiation to the substrate. According to the results, by choosing an ultra-small buffer thickness and gap distance and also by fine-tuning the nanowire and graphene parameters, a low propagation loss (0.312 dB/μm) with improved figure of merit can be realized simultaneously. Our findings are important for the optimum design of substrate-supported graphene-based cylindrical waveguides.
Effects of Bio and Chemical Nitrogen Fertilizer on Grain and Oil Yield of Sunflower(Helianthus annuus L.) under Different Rates of Plant Density

Considering the environmental problems arising from chemical fertilizers usage, many attentions has been drawn to the application of biological fertilizers in agriculture. In order to study the effects of chemical nitrogen fertilization and biofertilizer inoculation on grain yield, yield components and oil yield of sunflower (Helianthus annuus L.) under different rates of plant density, a Factorial experiment based on Randomized Complete Block design was conducted in three replications. Experimental factors were: (i). Four levels of chemical nitrogen fertilizer (0, 100, 150 and 200 kg N ha-1), (ii). Two levels of biofertilizer (with and without inoculation) containing of Azotobacter sp. and Azospirillum sp. and (iii). Three levels of plant density (8, 10 and 12 plant m-2). The highest plant height, stem and head diameter, number of grains per head, 1000-grains weight, grain yield, oil yield and biological yield were obtained from the highest level of nitrogen fertilizer (200 kg N ha-1) and biofertilizer inoculation. Application of 150 and 200 kg N ha-1 showed no significant difference in these traits. Furthermore, the highest rate of plant density (12 plant m-2) showed the maximum values of grain yield, oil yield and biological yield that was statistically in par with 10 plant m-2 density. The results pointed out that medium plant density (about 10 plant m-2) with moderate N rate (about 150 kg N ha-1) can be beneficial to improve growth, development and total yield of inoculated sunflower.
Acta Botanica Brasilica

A molecular phylogeny of the subfamily Ambavioideae (Annonaceae) was reconstructed using up to eight plastid DNA regions (matK, ndhF, and rbcL exons; trnL intron; atpB-rbcL, psbA-trnH, trnL-trnF, and trnS-trnG intergenic spacers). The results indicate that the subfamily is not monophyletic, with the monotypic genus Meiocarpidium resolved as the second diverging lineage of Annonaceae after Anaxagorea (the only genus of Anaxagoreoideae) and as the sister group of a large clade consisting of the rest of Annonaceae. Consequently, a new subfamily, Meiocarpidioideae, is established to accommodate the enigmatic African genus Meiocarpidium. In addition, the subfamily Ambavioideae is redefined to contain two major clades formally recognized as two tribes. The tribe Tetramerantheae consisting of only Tetrameranthus is enlarged to include Ambavia, Cleistopholis, and Mezzettia; and Canangeae, a new tribe comprising Cananga, Cyathocalyx, Drepananthus, and Lettowianthus, are erected. The two tribes are principally distinguishable from each other by differences in monoploid chromosome number, branching architecture, and average pollen size (monads). New relationships were retrieved within Tetramerantheae, with Mezzettia as the sister group of a clade containing Ambavia and Cleistopholis.
Acta Botanica Brasilica

Paubrasilia echinata presents three morphotypes that differ from each other in leaf morphology (small morphotypeSM, medium morphotype-MM, and large morphotype-LM) with indications of divergence between them regarding irradiance tolerance. The present study aimed to identify possible physiological and growth patterns in P. echinata morphotypes under contrasting irradiance. The growth and photosynthesis of SM were inhibited in the full sun, and those of MM and LM, in the shade. The auxin contents of the three morphotypes increased in the full sun. However, in MM and LM, this increase was 5 times greater. The oxidative damage (hydrogen peroxide-H2O2 and lipid peroxidation-MDA content) of SM were higher in the full sun and of MM and LM in the shade. The stem hemicelluloses and CO2 assimilation decreased in shade for SM, and sun for MM and LM. The shade tolerance of SM and the full sun tolerance of MM and LM were associated with lower oxidative damage and cell wall loosening (lower hemicelluloses content) in the photo-promoted treatments. The integration of the lower oxidative damage with cell wall loosening seems to represent an essential part of the tolerance mechanism of P. echinata morphotypes in the shade and full sun.
DNA fingerprinting of self-Compatible and self-Incompatible genotypes of sunflower (Helianthus Annuuns L.)

1. Title of thesis : DNA Fingerprinting of Self Compatible and Self Incompatible Genotypes of Sunflower (Helianthus annuus L.) 2. Full name of degree holder : DEEPA MEHTA 3. Admission No. : 2003BS146M 4. Title of degree : Master of Science 5. Name and address of Major Advisor : Dr. K. S. Boora Senior Scientist and Head Dept. of Biotechnology and Molecular Biology College of Basic Science and Humanities CCS Haryana Agricultural University Hisar-125004 6. Degree awarding University/ Institute : CCS Haryana Agricultural University, Hisar-125004 7. Year of award of degree : 2005 8. Major subject : Biotechnology and Molecular Biology 9. Total No. of pages in thesis : 58+xii 10. No. of words in the abstract : Approx 270 (An abstract of the thesis presented to the CCS Haryana Agricultural University in partial fulfilment of the requirements for the degree of M.Sc. in the subject of Biotechnology and Molecular Biology) __________________________________________________________________
Real locations Obfuscated locations LPPM Estimated locations User Adversary Attack

Attacks and defenses in the location privacy literature largely consider that users’ data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their models. We show that, in practice, training information cannot capture users’ behavior with perfect certainty, and hence state-of-the-art defenses overestimate the level of privacy they provide. To tackle this problem, we propose a new model for user mobility in sporadic scenarios, that we call the blank-slate model. This model acknowledges the uncertainty of designers about real user behavior: it starts as a “blank page”, and learns the actual user behavior as she queries the service provider. We use the blank-slate model to develop both an attack, the Profile Estimation Based Attack (PEBA), and a design technique, called output-feedback, that can be used to create LPPMs that progressively learn the user behavior. Using real data, we empirically show that our proposals outperform stateof-the-art mechanisms designed based on previous hardwired models. Our learning LPPMs do not require bootstrapping with training data and are easy to compute, so they could be easily deployed in current location privacy solutions.
Ingroup projection as a challenge of diversity: Consensus about and complexity of superordinate categories

A theoretical perspective that may help explain conflict between diverse groups is the ingroup projection model (Mummendey & Wenzel, 1999). Ingroup projection is an ethnocentric representation of a superordinate group that renders the ingroup relatively more prototypical (thus more normative, positive and deserving) than an outgroup believes is the case. Ingroup projection can lead to ingroup bias and prejudice against outgroups by portraying them as deviant. We review theory and research on the ingroup projection model, demonstrating its applicability to a wide range of intergroup contexts. We specifically discuss research on the multi-faceted cognitive, motivational, strategic and ideological underpinnings of ingroup projection, as well as threats resulting from beliefs that outgroups engages in such projection. We then focus on representational properties of superordinate identities that might limit ingroup projection, such as complexity or diversity, which however in themselves can become matters of contention between the involved groups. We argue that a precondition for positive intergroup relations is not only one of consensus between groups about their relative prototypicalities, which would foster harmony, but also acceptance of more equal prototypicality, which implies social change. Despite limitations and obstacles, superordinate identity complexity or diversity may hold the key for obtaining this goal.
Discovering Human-Object Interaction Concepts via Self-Compositional Learning (Appendix)

In this appendix, we provide detailed analysis in Section A, and illustrate the annotation in Section B. Quantitative illustration of discovered concepts are provided in Section C. More experimental re-sults (including ablation studies) in Section D and Section E. We also demonstrate the performance of HOI detection ( e.g. , Qpic, Table 11 and Table 12) in Section F, the visualization results on V-COCO in Section G, and additional concept discovery approaches (e.g., language embedding, Re-Training) in Section H. Extensive experiments demonstrate 1) different hyper-parameters has an obvious impact on concept discovery; 2) self-training concept discovery achieves a large relative improvement by over 190% on novel object unknown concept detection; 3) self-training effectively improves two lines of HOI detection method, particularly for rare category.
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing

Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "kernel mean embeddings", are leading methods for two-sample and independence tests from the machine learning community. A fixed-point transformation was previously proposed to connect the distance methods and kernel methods for the population statistics. In this paper, we propose a new bijective transformation between metrics and kernels. It simplifies the fixed-point transformation, inherits similar theoretical properties, allows distance methods to be exactly the same as kernel methods for sample statistics and p-value, and better preserves the data structure upon transformation. Our results further advance the understanding in distance and kernel-based tests, streamline the code base for implementing these tests, and enable a rich literature of distance-based and kernel-based methodologies to directly communicate with each other.
Digging Into Self-Supervised Monocular Depth Estimation

Depth-sensing is important for both navigation and scene understanding. However, procuring RGB images with corresponding depth data for training deep models is challenging; large-scale, varied, datasets with ground truth training data are scarce. Consequently, several recent methods have proposed treating the training of monocular color-to-depth estimation networks as an image reconstruction problem, thus forgoing the need for ground truth depth. There are multiple concepts and design decisions for these networks that seem sensible, but give mixed or surprising results when tested. For example, binocular stereo as the source of self-supervision seems cumbersome and hard to scale, yet results are less blurry compared to training with monocular videos. Such decisions also interplay with questions about architectures, loss functions, image scales, and motion handling. In this paper, we propose a simple yet effective model, with several general architectural and loss innovations, that surpasses all other self-supervised depth estimation approaches on KITTI.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

Similarity learning which is useful for the purpose of comparing various characteristics of images in the computer vision field has been often applied for deep metric learning (DML). Also, a lot of combinations of pairwise similarity metrics such as Euclidean distance and cosine similarity have been studied actively. However, such a local similarity-based approach can be rather a bottleneck for a retrieval task in which global characteristics of images must be considered important. Therefore, this paper proposes a new similarity metric structure that considers the local similarity as well as the global characteristic on the representation space, i.e., class variability. Also, based on an insight that better class variability analysis can be accomplished on the Stiefel (or Riemannian) manifold, manifold geometry is employed to generate class variability information. Finally, we show that the proposed method designed through in-depth analysis of generalization bound of DML outperforms conventional DML methods theoretically and experimentally. INDEX TERMS Deep metric learning, Image retrieval, Stiefel manifold, Non-linear mapping.
Accelerated Stochastic Algorithms for Nonconvex Finite-Sum and Multiblock Optimization

In this paper, we present new stochastic methods for solving two important classes of nonconvex optimization problems. We first introduce a randomized accelerated proximal gradient (RapGrad) method for solving a class of nonconvex optimization problems consisting of the sum of $m$ component functions, and show that it can significantly reduce the number of gradient computations especially when the condition number $L/\mu$ (i.e., the ratio between the Lipschitz constant and negative curvature) is large. More specifically, RapGrad can save up to ${\cal O}(\sqrt{m})$ gradient computations than existing deterministic nonconvex accelerated gradient methods. Moreover, the number of gradient computations required by RapGrad can be ${\cal O}(m^\frac{1}{6} L^\frac{1}{2} / \mu^\frac{1}{2})$ (at least ${\cal O}(m^\frac{2}{3})$) times smaller than the best-known randomized nonconvex gradient methods when $L/\mu \ge m$. Inspired by RapGrad, we also develop a new randomized accelerated proximal dual (RapDual) method for solving a class of multi-block nonconvex optimization problems coupled with linear constraints. We demonstrate that RapDual can also save up to a factor of ${\cal O}(\sqrt{m})$ projection subproblems than its deterministic counterpart, where $m$ denotes the number of blocks. To the best of our knowledge, all these complexity results associated with RapGrad and RapDual seem to be new in the literature. We also illustrate potential advantages of these algorithms through our preliminary numerical experiments.
Comptes Rendus Géoscience Sciences de la Planète

Mainland France is part of a plate interior with a strong structural heritage, undergoing a low rate of deformation, where destructive earthquakes can nevertheless occur. In this paper, we emphasize that the knowledge of active faults is still largely fragmentary, and that signiﬁcant e ﬀ orts are needed to generate robust data, in particular on the numerous faults, that still lack any study. This is the aim of the “Failles ACTives France” (FACT) axis launched in the framework of the Transverse Seismicity Action (ATS) of the Resif-Epos consortium. We present some recent investigations carried out along suspected active faults in mountainous areas, their forelands and remote lowlands, which implement new approaches and new tools, and allow characterizing their Quaternary activity.
Wake characteristics of a screen cylinder and a screen shrouded cylinder

i ACKNOWLEDGEMENTS . v TABLE OF CONTENTS . vii LIST OF TABLES . x LIST OF FIGURES . xi LIST OF SYMBOLS . xix ABBREVIATIONS . xxii .