Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Spatial Representation: a unique representation of an environment based on an ontology for robotic applications

It is important, for human-robot interaction, to endow the robot with the knowledge necessary to understand human needs and to be able to respond to them. We present a formalized and unified representation for indoor environments using an ontology devised for a route description task in which a robot must provide explanations to a person. We show that this representation can be used to choose a route to explain to a human as well as to verbalize it using a route perspective. Based on ontology, this representation has a strong possibility of evolution to adapt to many other applications. With it, we get the semantics of the environment elements while keeping a description of the known connectivity of the environment. This representation and the illustration algorithms, to find and verbalize a route, have been tested in two environments of different scales.
The forward smile in stochastic local volatility models

We introduce an approximation of forward start options in a multi-factor local-stochastic volatility model. We derive explicit expansion formulas for the so-called forward implied volatility which can be useful to price complex path-dependent options, as cliquets. The expansion involves only polynomials and can be computed without the need for numerical procedures or special functions. Recent results on the exploding behaviour of the forward smile in the Heston model are confirmed and generalized to a wider class of local-stochastic volatility models. We illustrate the effectiveness of the technique through some numerical tests.
Complete genomic sequence of Epstein-Barr virus in nasopharyngeal carcinoma cell line C 666-1

Background: Nasopharyngeal carcinoma is a distinct type of head and neck cancer which is consistently associated with Epstein-Barr virus (EBV). The C666-1 cell line is the only in vitro native EBV-infected NPC cell model commonly used for study of the viral-host interaction. Nevertheless, the complete EBV genome sequence in this in vitro EBV-infected NPC model has not been characterized. Objective: To determine the complete EBV genome sequence in C666-1 cells. Methods: The C666-1 genome was sequenced by 100-bases pair-end massive parallel sequencing. Bioinformatics analysis was performed to extract the EBV sequences and construct an EBV consensus sequence map. PCR amplification and Sanger DNA sequencing were used for sequence validation and gap filling. A phylogenetic analysis of EBV strain in C666-1 cells and other reported EBV strains was performed. Results: A 171,317 bp complete EBV genome of C666-1 was successfully constructed (GenBank accession number: KC617875). Phylogenetic analysis of EBV genome in C666-1 revealed that the C666-1 EBV strain is closely related to the reported strains in NPC primary tumors. Conclusion: C666-1 contains a representative NPC-associated EBV genome and might serve as an important model for studying the roles or function of viral proteins in NPC tumorigenesis.
Analytical approaches to estimation of temporal frequency preference from visual evoked potentials

There are various ways to study neuronal processing of information about temporal frequency content of visual stimuli. The two most fundamental methods are 1) direct measurement of response amplitude, e.g. an amplitude of averaged visual evoked potential, and 2) assessment of response magnitude after transformation of electrophysiological signal from time to frequency domain. In our study we found it impossible to use the same paradigm to analyze the whole spectrum of temporal frequencies in local field potentials recorded during visual electrophysiology experiments performed on anesthetized rats. Visual responses were recorded from all layers of primary visual cortex in response to flashing light with temporal frequency in the range of 0.5 - 15 Hz. We found that for frequencies lower than 2 Hz it is difficult to draw conclusions based on power spectrum alone, while for high frequencies (> 2 Hz) the evoked potential in time domain could not be observed. We discuss possible physiological reasons of these difficulties and the advantages of the Welch method instead of the periodogram to analyze signals in the frequency domain.
EasyChair Preprint No 534 Exploring the Perceived Usefulness and Attitude Towards using Tesys e-Learning Platform

In this paper we present a study that aims exploring two components of the Technology Acceptance Model. As evaluation environment, we used Tesys e-Learning platform, for data processing R programming language and for data collection Google Forms. The study indented to explore if there are significant differences between genders regarding the perceived usefulness and attitude towards evaluation using online educational environments. The questionnaire that explored the above-mentioned components was administrated to a group of students enrolled in full time education which never used e-Learning platforms. The results showed that the questionnaire was reliable and there is no cross-gender difference regarding students perceived usefulness of e-Learning platforms and their attitude towards using them. The overall feedback gathered from each of the explored items is positive with an above fourth grade on a one to five Likert scale.
A TAE-BASED QUALITATIVE STUDY OF SUBJECTIVE WELL-BEING FOR AGED JAPANESE MEN

The present study applied a TAE-based qualitative approach to assess subjective wellbeing for aged Japanese, with the focus on a retired male high school teacher. The analysis revealed a contrasting picture of a new-type of individualistic elderly person, on one hand, and the conventional mindsets of the Japanese community, on the other. The TAE-based approach contributed to visualizing the inner world of an individual.
EFFICIENT MODELING OF LASERWAKEFIELD ACCELERATION THROUGH THE PIC CODE SMILEI IN CILEX PROJECT

The design of plasma acceleration facilities requires considerable simulation effort for each part of the machine, from the plasma injector and/or accelerator stage(s), to the beam transport stage, from which the accelerated beams will be brought to the users or possibly to another plasma stage. The urgent issues and challenges in simulation of multi-stage acceleration with the Apollon laser of CILEX facility will be addressed. To simulate the beam injection in the second plasma stage, additional physical models have been introduced and tested in the open source Particle in Cell collaborative code Smilei. The efficient initialisation of arbitrary relativistic particle beam distributions through a Python interface allowing code coupling and the self consistent initialisation of their electromagnetic fields will be presented. The comparison between a full PIC simulation and a simulation with a recently developed envelope model, which allows to drastically reduce the computational time, will be also shown for a test case of laser wakefield acceleration of an externally injected electron beam.
Review of Leaf Classification Techniques using Machine Learning

: Plants form an indispensable part of our lives and as such the need for their identification and conservation is very important. A leaf is an organ of vascular plant and is the key parallel member of the stem. Each leaf has an arrangement of highlights that separate it from alternate leaves, for example, edge and shape. This paper proposes a comparative study of different classification approaches of supervised learning, based on different orientation and description of leaves and chosen algorithms. Starting with orientation of leaves, we exhibited leaves by a fine-scale edge feature histogram, by a Centroid Contour Distance Curve shape signature. At that point we assess the characterization utilizing cross validation. The acquired outcomes are extremely intriguing and demonstrate the significance of each component. The plant leaf image is classified with biometric features. Customarily, the prepared ordered play out this procedure by following different undertakings. Leaf biometric include are examined utilizing computer based strategy like morphological element examination and artificial neural system based classifier. KNN show take contribution as the leaf venation morphological element and group them into four distinct species.Keywords:
CATS 2: Color And Thermal Stereo Scenes with Semantic Labels

The CATS dataset introduced a new set of diverse indoor and outdoor scenes with ground truth disparity information for testing stereo matching algorithms in color and thermal imagery. These scenes included nighttime, foggy, low light, and complex lighting in scenes. To extend the usefulness of the CATS dataset we add pixel-and instance-level semantic labels. This includes labels for both color and thermal imagery, and the labels also apply to 3D point clouds as a result of the existing 2D-3D alignment. We compare the new CATS 2.0 dataset against other similar datasets and show it is similar in scope to the KITTI-360 and WildDash datasets, but with the addition of both thermal and 3D information. Additionally, we run a benchmark pedestrian detection al-gorithm on a set of scenes containing pedestrians.
Moment Properties Of Generalized Order Statistics From Weibull-Geometric Distribution ∗

The concept of generalized order statistics was introduced by Kamps [21]. Generalized order statistics is a unified approach of other ordered random schemes, like order statistics, record values, sequential order statistics, progressively type II censored order statistics and Pfeifers records. Therefore, the study of moments and recurrence relations between moments of generalized order statistics are of special interest. In this paper, an attempt has been made to derive some recurrence relations for single and product moments of generalized order statistics from Weibull-geometric distribution, which was proposed by Barreto-Souza et al. [15]. Further, order statistics and record values are studied as special cases. At the end, some characterization results are also presented.
Body Balancing Control of A Six-Legged Robot

With the robot SiLVIA (Six Legged Vehicle with Intelligent Articulation) built in Romela Lab, we achieve the function of balancing the body at various operating conditions (standing, wall climbing, etc.) by implementing robotic motion control. Decentralized control with position feedback for each joint is used to control 18 joints. Decentralized feedforward compensation is tested to improve the performance of whole system. Experiment result is showed at the end.
Learning Object-Wise Semantic Representation for Detection in Remote Sensing Imagery

With the upgrade of remote sensing technology, object detection in remote sensing imagery becomes a critical but also challenging problem in the field of computer vision. To deal with highly complex background and extreme variation of object scales, we propose to learn a novel object-wise semantic representation for boosting the performance of detection task in remote sensing imagery. An enhanced feature pyramid network is first designed to better extract hierarchical discriminative visual features. To suppress background clutter as well as better estimate proposals, next we specifically introduce a semantic segmentation module to guide horizontal proposals detection. Finally, a ROI module which can fuses multiple-level features is proposed to further promote object detection performance for both horizontal and rotate bounding boxes. With the proposed approach, we achieve 79.5% mAP and 76.6% mAP in horizontal bounding boxes (HBB) and oriented bounding boxes (OBB) tasks of DOTA-v1.5 dataset, which takes the first and second place in the DOAI2019 challenge, respectively.
L umos : Dependency-Driven Disk-based Graph Processing

Out-of-core graph processing systems are well-optimized to maintain sequential locality on disk and minimize the amount of disk I/O per iteration. Even though the sparsity in realworld graphs provides opportunities for out-of-order execution, these systems often process graphs iteration-by-iteration, hence providing Bulk Synchronous Parallel (synchronous for short) mode of processing which is also a preferred choice for easier programmability. Since out-of-core setting limits the view of entire graph and constrains the processing order to maintain disk locality, exploiting out-of-order execution while simultaneously providing synchronous processing guarantees is challenging. In this paper we develop a generic dependencydriven out-of-core graph processing technique, called LUMOS, that performs out-of-order execution to proactively propagate values across iterations while simultaneously providing synchronous processing guarantees. Our cross-iteration value propagation technique identifies future dependencies that can be safely satisfied, and actively computes values across those dependencies without sacrificing disk locality. This eliminates the need to load the corresponding portions of graph in future iterations, hence reducing disk I/O and accelerating the overall processing.
Image Detection Using Exemplar-SVM with Augmented Features

Exemplar-SVM (E-SVM) was first introduced by Malisiewicz et al, and achieved good performance in the application of object detection and Content-Based Image Retrieval. E-SVM is about training a linear SVM with a single positive sample and many negative samples. All the E-SVMs thus form an ensemble that performs well. Training a good E-SVM requires the negative set to be very large. In this paper we propose to apply ESVM with augmented features to the area of image detection by softly forcing it to be constructed form existing classifier parts cropped from previously trained classifiers. Our method helps to improve the generalization of the original E-SVM.The proposed method is evaluated on the subset of PASCAL VOC 2007 to detect different orientation of a specific object, and it achieves better performance over E-SVM whilst helps reducing the size of negative set.
Performance Comparison of Classification Algorithms for Medical Diagnosis

Knowledge extraction from medical datasets is a challenging task. Medical datasets are known for their complexity in terms of noise, missing values and imbalanced class distribution. Classification algorithms can assist medical experts in disease diagnosis provided that a rigorous and methodological evaluation of classification models is applied by selecting appropriate sampling techniques, performance metrics and statistical tests. An ad hoc approach in this regard can result in unexpectedly high misclassification rates, which may prove very costly in terms of people’s health and lives. In this paper, we illustrate a methodology to evaluate and compare multiple classification algorithms on multiple medical datasets. The example experiment is conducted by applying five well-known machine learning algorithms i.e. the Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Ant Colony Optimisation (ACO) and Genetic Algorithm (GA) for discovering classification models for disease diagnosis from 11 publicly available medical datasets from the UCI machine learning laboratory. We conclude through a stepwise evaluation process that the performance of the Random Forest classifier was significantly better in diagnosing various diseases. The paper also addresses the issue of class imbalance and non-uniform misclassification costs, usually prevalent in datasets for disease diagnosis.
Cumulative Effects of a Severe Windstorm and Subsequent Silvicultural Treatments on Plant and Insect Diversity in The Gunflint Corridor of the Superior National Forest in Northern Minnesota : Five-Year Results

On July 4 1999, unprecedented derechos, also known as thunderstorm downbursts (wind speeds of 75 to 110 mph), caused damage to approximately 195,000 hectares (~477,000 acres) of sub-boreal forest in the Superior National Forest, including the Boundary Waters Canoe Area Wilderness (BWCAW). No harvesting or salvage activity is permitted in the BWCAW although prescribed burns to reduce fuel loadings have been conducted. Alternative fuel-reduction treatments were conducted to reduce the risk of fire and insect and disease outbreaks in the Gunflint Corridor that is surrounded by the BWCAW. These treatments included: (a) prescribed burning; (b) salvage-harvesting; and (c) piling and burning of wind thrown trees. We collected preand post-treatment data on plots established to monitor plant succession, and insect diversity and change, within each of two forest cover types (aspen-birchconifer and jack pine), on 1) undisturbed sites; 2) severely wind disturbed sites that were not treated; 3) severely wind disturbed sites that were salvaged logged; 4) severely wind disturbed sites that were salvaged logged where the salvaged material was machine piled, and 5) severely wind disturbed sites that were treated with prescribed burns. We present 5-yr results on the effects of the windstorm and subsequent fuel reduction treatments on fuel loadings, shrub community dynamics (biomass, species richness, species turn-over, and species dominance), and diversity and impact of ground beetles and woodborers. Prescribed fire reduced fine fuels but was less successful in reducing total fuels than salvage logging. The shrub community included shrubs and trees smaller than saplings. There was no change in total shrub biomass, species richness and shrub species turn-over. Shrub species constancy was fairly stable within treatments. Changes in shrub species dominance as determined by biomass varied by treatment and cover type. Of sixty-one species of ground beetles (Carabidae), an exotic species, Pterostichus melanarius, was the most abundant ground beetle on all plots and preferentially colonized burned plots. Roundheaded woodborer (Monochamus spp.) populations increased in wind disturbed jack pine between 1999 and 2000, and attacked living but stressed jack pine trees surrounding the wind disturbed sites in 2001 and 2002.
D S ] 1 2 Ju l 2 01 9 Online-Learning for min-max discrete problems

We study various discrete nonlinear combinatorial optimization problems in an online learning framework. In the first part, we address the question of whether there are negative results showing that getting a vanishing (or even vanishing approximate) regret is computational hard. We provide a general reduction showing that many (min-max) polynomial time solvable problems not only do not have a vanishing regret, but also no vanishing approximation α-regret, for some α (unless NP = BPP ). Then, we focus on a particular min-max problem, the min-max version of the vertex cover problem which is solvable in polynomial time in the offline case. The previous reduction proves that there is no (2 − ǫ)-regret online algorithm, unless Unique Game is in BPP ; we prove a matching upper bound providing an online algorithm based on the online gradient descent method. Then, we turn our attention to online learning algorithms that are based on an offline optimization oracle that, given a set of instances of the problem, is able to compute the optimum static solution. We show that for different nonlinear discrete optimization problems, it is strongly NP -hard to solve the offline optimization oracle, even for problems that can be solved in polynomial time in the static case (e.g. min-max vertex cover, min-max perfect matching, etc.). On the positive side, we present an online algorithm with vanishing regret that is based on the follow the perturbed leader algorithm for a generalized knapsack problem. Keywords— Online learning, min-max problems, hardness, follow the leader, online gradient descent
Projected Surface Finite Elements for Elliptic Equations

In this article, we define a new finite element method for numerically approximating solutions of elliptic partial differential equations defined on “arbitrary” smooth surfaces S in R. By “arbitrary” smooth surfaces, we mean surfaces that can be implicitly represented as level sets of smooth functions. The key idea is to first approximate the surface S by a polyhedral surface Sh, which is a union of planar triangles whose vertices lie on S; then to project Sh onto S. With this method, we can also approximate the eigenvalues and eigenfunctions of th Laplace-Beltrami operator on these “arbitrary” surfaces.
ffi cient Implementation of Data Objects in the OSD +-Based Fusion Parallel File System

OSD+s are enhanced object-based storage devices (OSDs) able to deal with both data and metadata operations via data and directory objects, respectively. So far, we have focused on designing and implementing efficient directory objects in OSD+s. This paper, however, presents our work on also supporting data objects, and describes how the coexistence of both kinds of objects in OSD+s is profited to efficiently implement data objects and to speed up some common file operations. We compare our OSD+-based Fusion Parallel File System (FPFS) with Lustre and OrangeFS. Results show that FPFS provides a performance up to 37× better than Lustre, and up to 95× better than OrangeFS, for metadata workloads. FPFS also provides 34% more bandwidth than OrangeFS for data workloads, and competes with Lustre for data writes. Results also show serious scalability problems in Lustre and OrangeFS.
Molecular Cloning and Expression Characterization of Three Suppressors of Cytokine Signaling Genes (SOCS5, SOCS6, SOCS7) from the Mealworm Beetle, Tenebrio molitor

Suppressors of cytokine signaling (SOCS) influence cytokine and growth factor signaling by negatively regulating the JAK-STAT pathway. This maintains homeostasis during host immune response. However, functional characterization of SOCS family members in invertebrates is limited. In this study, we discovered the Type-I subfamily of the SOCS genes in the mealworm beetle, T. molitor. The full-length ORFs of TmSOCS5, TmSOCS6, and TmSOCS7 consisted of 1,389, 897 and 1,458 nucleotides, encoding polypeptides of 462, 297 and 485 amino acids, respectively, The C-terminal region of TmSOCS was highly conserved in the SH2 and SOCS box domains. Phylogenetic analysis revealed that the three SOCS genes clustered within the same sub-family and the highest amino acid identity was with the Tribolium castaneum SOCS genes (TcSOCS). While the expression of TmSOCS5 and TmSOCS6 was low in larval, pupal, and adult stages of the insect, TmSOCS7 showed higher expression. The expression of TmSOCS5 and TmSOCS6 was higher in larval hemocytes and adult ovary. The microbes expressed the three TmSOCS genes to varying degrees. C. albicans elicited the strongest response in the host with highest 15-fold expression in TmSOCS7 3 h post-inoculation. Collectively, these data suggest that the Type I TmSOCS could play a role in eliciting host immunity.
Active structural vibration control using temperature constrained shape memory alloy actuation

This paper presents a modeling method for shape memory alloy-based actuators in vibration control and proposes a model predictive controller with separate deformation and thermal models to constrain temperatures levels in the actuator. A clamped cantilever beam equipped with actuators using pre-tensioned shape memory alloy wires is used as a demonstration device. The controller designed for this system aims to minimize beam tip deflections, while simultaneously constraining the input voltage of the actuators to limit values, in order to maximize the actuator efficiency. To solve this control problem, the paper proposes to use model predictive control with two separate models: one model for deformation dynamics and one for thermal dynamics. The deformation model is used in the cost function and input constraints, while predictions of the thermal model figure in the state constraints. The proposed first-principle models are identified based on experimental data and the input constrained model predictive control scheme is demonstrated in experiment with good results. The feasibility of the input and thermal constrained controller concept is estabilished in simulation.