Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Accurate Deep-Sea Localization in Structured Environments based on PerceptionQuality Cues Extended Abstract

In recent years, the number of maritime exploration and exploitation activities has rapidly increased, and with it the necessity to performmore complex tasks underwater, e.g., floatingmanipulation and mapping with Remote Operated Vehicles (ROVs). The first step to perform these activities in a reliable manner, is to obtain an accurate robot localization estimate. Localization approaches based on multi-robot systems or complex acoustic infrastructures have been favored in the literature, but alternatively visual modalities are pursued when these options are not feasible. In this work, we present a two-stage navigation scheme that initially generates a coarse probabilistic map of the workspace that is used to refine localization accuracy and filter noise in the second stage. Additionally, an adaptive decision-making approach is introduced that determines which perception cues to incorporate into the localization filter, i.e., tracked 2D features or plane representations, to ensure high accuracy and reduce computation times. Our approach is thoroughly investigated in simulation and validated with deep-sea field trial data originated from oil & gas commercial operations.
THE NATIVE BEES, MELLIPONAS (HYMENOPTERA: MELLIPONIDAE) ASSOCIATED TO CAMU CAMU Myrciaria dubia

From April 2005 to February 2006 the inflorescences of camu camu were recorded in five communities of the Amazon river ́s basin, with the objective to get to know the species and the abundance of native bees associated to this vegetable species. Six genders and sixteen species of native bees were registered and Aphis mellifera as an introduced species. The greater richness of species were presented by the genders Trigona with 43.75% and Melipona with 25%. The most abundant species were Trigona sp.1 with 42.40 % and Melipona eburnea with 22.12%. For each bee that was found it is followed by characterization and photos.
Preparation and characterization of tapioca starch-chitosan hybrid material by non-solvent precipitation method

Tapioca starch has potential application in agriculture, foods, pharmaceuticals, and cosmetics. However, tapioca starch has high moisture absorption and microbial contamination. Hybridization of tapioca starch with chitosan is an alternative method for modification of its properties. In this work, tapioca starch-chitosan hybrid material was prepared by precipitation method. Ethanol was used as anti-solvent agent. The characteristics of tapioca starch-chitosan hybrid material (TS-CSP) were investigated by DSC, FT-IR, TGA, XRD, and SEM techniques. TS-CSP exhibited quite similar thermal stability to tapioca starch evaluated by TGA. DSC result showed new compound was formed. FT-IR techniques showed the interaction between hydroxyl group of tapioca starch and amino group of chitosan molecule. SEM image of the TS-CSP showed round and polygonal shape with thin layer of chitosan film on its surface. XRD pattern of TS-CSP showed an A-type starch diffraction peak with less crystalline structure after being mixed with amorphous chitosan. Moreover, TS-CSP has anti-bacterial property studied by disk diffusion method. It showed inhibition effect against S. aureus TISTR 746 , E. coli TISTR 527 , and P. aeruginosa TISTR 1287 with inhibition zone of 8, 8, and 9 mm, respectively.
Decompositions into spanning rainbow structures

A subgraph of an edge‐coloured graph is called rainbow if all its edges have distinct colours. The study of rainbow subgraphs goes back more than 200 years to the work of Euler on Latin squares and has been the focus of extensive research ever since. Euler posed a problem equivalent to finding properly n ‐edge‐coloured complete bipartite graphs Kn,n which can be decomposed into rainbow perfect matchings. While there are proper edge‐colourings of Kn,n without even a single rainbow perfect matching, the theme of this paper is to show that with some very weak additional constraints one can find many disjoint rainbow perfect matchings. In particular, we prove that if some fraction of the colour classes have at most (1−o(1))n edges, then one can nearly‐decompose the edges of Kn,n into edge‐disjoint perfect rainbow matchings. As an application of this, we establish in a very strong form a conjecture of Akbari and Alipour and asymptotically prove a conjecture of Barat and Nagy. Both these conjectures concern rainbow perfect matchings in edge‐colourings of Kn,n with quadratically many colours. The above result also has implications to some conjectures of Snevily about subsquares of multiplication tables of groups.
Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client's encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.
Chapter 1 FERTILITY , LIVING ARRANGEMENTS , CARE AND MOBILITY

There are four main interconnecting themes around which the contributions in this book are based. This introductory chapter aims to establish the broad context for the chapters that follow by discussing each of the themes. It does so by setting these themes within the overarching demographic challenge of the twenty-first century – demographic ageing. Each chapter is introduced in the context of the specific theme to which it primarily relates and there is a summary of the data sets used by the contributors to illustrate the wide range of cross-sectional and longitudinal data analysed.
Band recoveries reveal alternative migration strategies in

Migration strategies may change in response to climate change with consequences for conservation efforts. We used 80 years (1934−2014) of band recovery data (N = 1,057) to describe spatial and temporal patterns in the migration behavior of American Robins. The distribution of recoveries suggests strong continental scale connectivity with distinct separation between eastern and western North America, with a more moderate degree of connectivity within these regions. We also found little evidence of differential migration between males and females. Despite previous studies that suggest the winter distribution of robins has shifted northward, our analysis shows no obvious change in migration distance over time. Surprisingly, we found that a significant proportion of across season band recoveries occurred locally (20%), in close proximity to the original banding locations. It’s well known that large numbers of robins linger in northern breeding grounds well into the winter of some years, but the proximity of these birds to breeding areas was previously unknown. We found little evidence that the winter latitude of migrants or local recoveries shifted over time. However, there was a trend for increased frequency of local recoveries in recent decades, providing an alternative hypothesis for the northward shift in winter distribution.
God, Sex, and Especially Politics: Disentangling the Dimensions of Discrimination

How rampant is political discrimination, and how does it compare to other important sources of bias in apolitical interactions? We employ a novel conjoint experimental paradigm to compare the magnitude of various salient social categories in generating discrimination by partisans across a variety of largely apolitical scenarios. The conjoint framework is well suited to this purpose, as it enables one to simultaneously vary and identify the distinct causal effects of several factors, ceteris paribus. We find pronounced, consistent discrimination along the lines of religion (especially against Atheists, Muslims and Evangelical Protestants) and sexual orientation. We also see substantial desire for homophily and avoidance of out-group members along several dimensions. Perhaps most striking, though, is just how much discrimination emerges along political lines. Notably, that discrimination is not limited to partisan divides. In fact, counter-stereotypic ideological labels can moderate, and even erase, the discriminatory consequences of party.
F L ] 3 0 Se p 20 17 Two-way Two-tape Automata

In this article we consider two-way two-tape (alternating) automata accepting pairs of words and we study some closure properties of this model. Our main result is that such alternating automata are not closed under complementation for non-unary alphabets. This improves a similar result of Kari and Moore for picture languages. We also show that these deterministic, non-deterministic and alternating automata are not closed under composition.
Nutrient uptake and regeneration as a means of initiating and sustaining algal blooms in the James River estuary Abtrsact

Abtrsact Measurements of nutrient concentrations and nutrient uptake rates were collected from three stations in the James River/Elizabeth River complex. In the tidal fresh James River, monthly experiments were conducted to determine the dominant nutrients controlling cyanobacteria growth in the summer months. Weekly experiments were conducted in the mesohaline James River and in the headwaters of the Lafayette River (a tributary of the Elizabeth River) to determine nutrient controls on the annual dinoflagellate bloom, Cochlodinium polykrikoides. In 2012, there was no mono-specific cyanobacteria bloom of Microcystis as had been seen in the past, however increased chlorophyll a concentrations appeared to be fueled by forms of regenerated nitrogen such as ammonium, urea, and dissolved organic nitrogen. Although di-nitrogen fixation was measured, it was a very small percentage of the total nitrogen uptake for the tidal fresh station. The dinoflagellate bloom, Cochlodinium polykrikoides, initiated in the headwaters of the Lafayette River earlier than had been seen in years past (end of June) and lasted through August. The bloom spread to the polyand mesohaline James River, and no particular nitrogen compound is sited as being a trigger for the bloom, however the bloom sustained itself on regenerated nitrogen compounds.
FLUIDS: A First-Order Local Urban Intersection Driving Simulator

—To facilitate automation of urban driving, we present an efﬁcient, lightweight, open-source, ﬁrst-order simulator with associated graphical display and algorithmic super-visors. The goal of FLUIDS is to allow for large-scale data collection of challenging simulated trafﬁc intersections with varying state conﬁgurations and large-scale evaluation for early stage development of planning algorithms. FLUIDS supports an image-based birds-eye state space and a lower dimensional quasi-LIDAR representation. FLUIDS additionally provides an implemented algorithmic planner as a baseline controller. We ﬁnd that FLUIDS can gather data in parallel from the baseline controller at 4000 state-action pairs per minute and evaluate in parallel an imitation learned policy on the baseline at 20K evaluations per minute. We demonstrate the usefulness of FLUIDS’ data collection speeds by training a velocity controller for avoiding collisions and obeying trafﬁc laws using imitation learning on the provided baseline controller as the supervisor. We also use FLUIDS to automate an extensive sensitivity analysis of the learned model to various simulation parameters. FLUIDS 1.0 is available at https://berkeleyautomation.
Merolla Stereotypes in the 2008 Presidential Election What Is the Difference Between a Hockey Mom and a Pit Bull ? : Presentations of Palin and Gender

This study explores what effect different campaign information frames have on voters’ evaluations of Sarah Palin using data from an online experiment. We show that descriptions highlighting more stereotypically feminine attributes cause individuals to perceive Palin as holding more feminine traits, while those highlighting more masculine attributes lead to higher assessments of her masculine traits. Those that mix feminine and masculine attributes lead to higher assessments of Palin’s masculine traits and null effects on her feminine traits. We show that Palin benefits the most in overall evaluations by being perceived as high on both masculine and feminine traits.
Towards A Unified Framework for Event Detection Applications

Event detection applications have gained significant attention with the rise of user-generated spatio-temporal data over the past decade. However, building event detection applications still encounter high cost and effort due to lack of support in existing data management systems. This paper envisions a holistic system approach to support an efficient and easy-to-use system infrastructure for building event detection applications. We outline our vision for representing event detection applications as a set of layered abstractions and discuss potential pathways to realize these abstractions at the system level.
Prognostic significance of long non-coding RNA AFAP 1-AS 1 in human gastrointestinal cancers : a meta-analysis

Background: AFAP1-AS1, as a newly discovered lncRNA, is aberrantly expressed in gastrointestinal cancer tissues and is related with clinical progression and prognosis. This meta-analysis was performed to systematically assess the clinical value of AFAP1-AS1 in gastrointestinal cancers. Methods: A systematic search was performed in Pubmed, Web of Science, Springer together with Wanfang database, and Chinese National Knowledge Infrastructure (CNKI) until August 15, 2017. We collected relevant publications to explore the prognostic significance of AFAP1-AS1 in gastrointestinal cancers. Results: A total of 831 patients from 10 studies were included in the meta-analysis. The pooled results showed that there was a significant association between high AFAP1AS1 expression and worse overall survival (OS) (HR = 1.82, 95% CI = 1.44-2.19, p < 0.001), shorter disease-free survival (DFS) (HR = 2.21; 95% CI = 1.08-3.34; p < 0.001), and poorer progression-free survival (PFS) (HR = 1.73; 95% CI = 1.11-2.35; p < 0.001) in gastrointestinal cancers. For OS, subgroup analyses were also conducted to confirm the prognostic value of AFAP1-AS1. Additionally, high AFAP1-AS1 expression was correlated with unfavorable features regarding lymph node metastasis, distant metastasis, and clinical stage in gastrointestinal cancers. Conclusions: AFAP1-AS1 might serve as a novel prognostic biomarker for patients with gastrointestinal cancers.
College English Flipped Classroom in Applied Undergraduate Colleges Based on Rain Classroom

To meet the information needs of flipped classroom, the rain classroom was developed. The threshold of information technology was low, the function was strong, and the operation was simple. It provided a convenient path for the implementation of flipped classroom. The concept of the rain classroom and flipped classroom was elaborated. The flipped classroom teaching mode of the rain classroom was studied. The rain classroom application was studied and summarized in the model of college English flipped classroom. The results showed that the rain classroom ensured the effective interaction between teachers and students in pre-class preparation, classroom teaching and after-school review. Therefore, this method not only improves the quality of teaching, but also cultivates students' self-learning habits and abilities. It is of great significance to apply the rain classroom to the college English flipped classroom.
Learning Data-Adaptive Interest Points through Epipolar Adaptation

Interest point detection and description have been cornerstones of many computer vision applications. Handcrafted methods like SIFT and ORB focus on generic interest points and do not lend themselves to data-driven adaptation. Recent deep learning models are generally either supervised using expensive 3D information or with synthetic 2D transformations such as homographies that lead to improper handling of nuisance features such as occlusion junctions. In this paper, we propose an alternative form of supervision that leverages the epipolar constraint associated with the fundamental matrix. This approach brings useful 3D information to bear without requiring full depth estimation of all points in the scene. Our proposed approach, Epipolar Adaptation, fine-tunes both the interest point detector and descriptor using a supervision signal provided by the epipolar constraint. We show that our method can improve upon the baseline in a target dataset annotated with epipolar constraints, and the epipolar adapted models learn to remove correspondence involving occlusion junctions correctly.
Uncertainty in Deep Learning : The goal σ 2 ොy

End-to-end learning has recently emerged as a promising technique to tackle the problem of autonomous driving. Existing works show that learning a navigation policy from raw sensor data may reduce the system’s reliance on external sensing systems, (e.g. GPS), and/or outperform traditional methods based on state estimation and planning. However, existing end-to-end methods generally trade off performance for safety, hindering their diffusion to real-life applications. For example, when confronted with an input which is radically different from the training data, end-to-end autonomous driving systems are likely to fail, compromising the safety of the vehicle. To detect such failure cases, this work proposes a general framework for uncertainty estimation which enables a policy trained end-toend to predict not only action commands, but also a confidence about its own predictions. In contrast to previous works, our framework can be applied to any existing neural network and task, without the need to change the network’s architecture or loss, or to train the network. In order to do so, we generate confidence levels by forward propagation of input and model uncertainties using Bayesian inference. We test our framework on the task of steering angle regression for an autonomous car, and compare our approach to existing methods with both qualitative and quantitative results on a real dataset. Finally, we show an interesting by-product of our framework: robustness against adversarial attacks. SUPPLEMENTARY MATERIAL For supplementary video see: https://youtu.be/1JtU78Heceg. The project’s code is available at: https://github.com/ mattiasegu/A General Framework for Uncertainty Estimation in Deep Learning.
Astaxanthin Attenuates Oxidative Damage in Retina by Potentiation of the Antioxidant Capacity in Experimental Model of Diabetic Retinopathy

Background: Astaxanthin is a natural carotenoid, which is belonged to the Xanthophylls family. Previously, the powerful antioxidant properties of this compound have been demonstrated in several biological environments. Objectives: As overproduction of reactive oxygen species (ROS) has a critical role in the pathophysiology of diabetic retinopathy (DR), we aimed to evaluate the effects of astaxanthin on the antioxidant capacity of retina in experimental model of DR. Methods: Forty eight rats were randomly divided into four groups (n=12 for each group); normal, diabetic, and two treated normal and diabetic groups. Diabetes was induced by a single intravenous injection of streptozotocin (50 mg/kg). Rats with blood glucose >500 mg/dL were selected as diabetic animals. Treatment groups were treated with astaxanthin (20 mg/kg/day, orally) for six weeks. At the end, the eyeballs were removed under deep anesthesia and then the retinal tissues were quickly frozen for assessment of the glutathione and malondialdehyde contents as well as superoxide dismutase (SOD) activity. Results: Malondialdehyde levels of the retina in diabetic rats increased significantly (16%), whereas the glutathione levels decreased (85%), (P<0.05). Astaxanthin significantly increased the glutathione content of retina in treated diabetic rats by 77% compared to untreated rats (P<0.05). Diabetes also increased the activity of SOD in retina. Treatment with astaxanthin enhanced the SOD activity both in treated normal and diabetic rats (P<0.05). Conclusion: Our findings reveal that astaxanthin can decrease oxidative damage of retina by improving the antioxidant capacity in diabetic rats, which may ultimately delay the appearance of DR.
Customizable Architecture Search for Semantic Segmentation

In this paper, we propose a Customizable Architecture Search (CAS) approach to automatically generate a network architecture for semantic image segmentation. The generated network consists of a sequence of stacked computation cells. A computation cell is represented as a directed acyclic graph, in which each node is a hidden representation (i.e., feature map) and each edge is associated with an operation (e.g., convolution and pooling), which transforms data to a new layer. During the training, the CAS algorithm explores the search space for an optimized computation cell to build a network. The cells of the same type share one architecture but with different weights. In real applications, however, an optimization may need to be conducted under some constraints such as GPU time and model size. To this end, a cost corresponding to the constraint will be assigned to each operation. When an operation is selected during the search, its associated cost will be added to the objective. As a result, our CAS is able to search an optimized architecture with customized constraints. The approach has been thoroughly evaluated on Cityscapes and CamVid datasets, and demonstrates superior performance over several state-of-the-art techniques. More remarkably, our CAS achieves 72.3% mIoU on the Cityscapes dataset with speed of 108 FPS on an Nvidia TitanXp GPU.