Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Multi-Sensor System for Smart Quartier Mobility Applications

: The negative impact of global urbanization is most evident in the ﬁeld of mobility. Inner city individual trafﬁc, in particular, is on the rise. The mixed and increasingly unpredictable trafﬁc conditions result in more and more accidents, to the chagrin of vulnerable road users (VRUs). The use of existing infrastructures is unbalanced and no longer meets the needs of all road users. By transforming public areas into pedestrian-only zones, a balance is slowly being restored. The deployment of autonomous mobile systems (AMSs) creates new problems that exhibit similarities to those in automotive mobility. Within the project Test Area Intelligent Quartier Mobility (TIQ) at the Hamburg University of Applied Sciences (HAW Hamburg), a multi-sensor system (MSS) is developed. This platform aims to facilitate the integration of AMSs into quartier mobility.
Interactive comment on “Plant phenology evaluation of CRESCENDO land surface models – Part I: start and end of growing season” by Daniele Peano et al

Dear authors, Many thanks for your interesting evaluation of phenology in the CRESCENDO model ensemble. Please excuse the delay in my referee report. In my view, your study has some methodological issues (see below) that need to be resolved before I can recommend publication of the manuscript. In addition, your analysis and model-data comparisons could benefit from considering some other publications that benchmarked land surface models for phenology or that parametrized phenology
Segmentation of cell nuclei in 2D microscopy images with CNNs

The cell is the fundamental biological unit of life. Research in cellular biology and biomedical engineering, together with a wide range of other biological tasks, depend on the segmentation of cells, or parts of the cell, from microscopy images. A common segmentation objective is to separate the nuclei from the background in cell cultures. Often, identifying each nucleus individually, or instance segmentation, is the consequent target. This work will focus on nucleus instance segmentation from a fluorescent microscopy data set, consisting of 2-D images. The goal of this work is exemplified in Figure 1a. In recent years, a growing trend towards employing more complex methods from Machine Learning has been observed in the literature [5]. Most of the newly proposed approaches find themselves in deep learning, especially convolutional neural networks (CNNs) based architectures such as U-Net [7] and DeepCell[10]. This paper builds on the former example to engage in the instance segmentation of nuclei.
What drives the growth of start-up firms? A tool for mapping the state-of-the-art of the empirical literature

PurposeThis study aims to enrich the current theoretical debate on the growth of start-up firms by extensively investigating the ongoing empirical studies in this research stream. Moreover, this study identifies drivers whose support roles are confirmed in the literature and recommends further research opportunities.Design/methodology/approachIn this study, we analysed the results of 316 empirical studies on start-up firms and growth and also identified and categorised 66 growth drivers. We presented these drivers in three-dimensional charts: 1) the frequency of using each driver in the 316 studies, 2) the consistency of each driver as measured by the number of studies supporting its statistical significance and 3) the net effect (positive or negative) of each driver on growth.FindingsOur analysis compares extant studies on growth drivers and shows some under-explored growth factors of start-up firms.Practical implicationsBoth start-up managers and policymakers can benefit from this study. This study provided managers with a fine-grained tool on the main growth drivers and can guide policymakers in supporting policies for start-up firms.Originality/valueThis study provides a rich, fine-grained and coherent picture of several potential growth drivers of start-up firms. Moreover, we extended our analysis to various potential drivers more than previous studies on this topic, thereby providing fruitful insights into the critical growth factors for start-up firms.
Emotional robotics based on iT/spl I.bar/Media

Intelligence is thought to be related to interaction rather than a deep but passive thinking. Interactive tangible media "iTMedia" is proposed to explore these issues. Personal robotics is a major area to investigate these ideas. A new design methodology for personal and emotional robotics is proposed. Sciences of the artificial and intelligence have been investigated. A short history of artificial intelligence is presented in terms of logic, heuristics, and mobility; a science of intelligence is presented in terms of imitation and understanding; intelligence issues for robotics and intelligence measures are described. A design methodology for personal robots based on science of emotion is investigated. We investigate three different aspects of design: visceral, behavioral, and reflective. We also discuss affect and emotion in robots, robots that sense emotion, robots that induce emotion in people, and implications and ethical issues of emotional robots. Personal robotics for the elderly is investigated to explore these ideas.
A Fungus-Specific Ras Homolog Contributes to the Hyphal Growth and Virulence of <italic toggle='yes'>Aspergillus fumigatus</italic>

The Ras family of GTPase proteins has been shown to control morphogenesis in many organisms, including several species of pathogenic fungi. In a previous study, we identified a gene encoding a fungus-specific Ras subfamily homolog, rasB, in Aspergillus fumigatus. Here we report that deletion of A. fumigatus rasB caused decreased germination and growth rates on solid media but had no effect on total biomass accumulation after 24 h of growth in liquid culture. The rasB mutant had an irregular hyphal morphology characterized by increased branching. Expression of rasB 113–135, a mutant transgene lacking the conserved rasB internal amino acid insertion, did not complement the deletion phenotype of delayed growth and germination rates and abnormal hyphal morphology. Virulence of the rasB deletion strain was diminished; mice infected with this strain exhibited 65% survival compared to 10% with wild-type and reconstituted strains. These data support the hypothesis that rasB homologs, which are highly conserved among fungi that undergo hyphal growth, control signaling modules important to the directional growth of fungal hyphae.
Towards Visual Analytics for Web Security Data

The continuing expansion in number and impact of cyber attacks, data breaches and other forms of cybercrime reminds us how fragile security on the Internet remains. The web ecosystem, in which these incidents occur, therefore warrants analysis to obtain an overview of the illicit operations and actors involved, ultimately in order to create better defenses. For this purpose, security analysts collect vast amounts of data on the systems and networks they oversee. In addition, researchers set up large-scale measurements to investigate various security issues, e.g. across the IPv4 address space [2]. Finally, organizations such as CERTs and security companies monitor the cyber world and publicly share their data. Combining all of these sources provides a wealth of data to explore, but without proper analysis tools, the task of extracting insights proves to be insurmountable. The field of visual analytics integrates visualization and interaction into the analysis process [9], using the former to leverage the increased data processing power of the human perception [6] and the latter to encourage data exploration. These methods speed up and improve data analysis and allow to handle the large quantities of data. Unfortunately, cyber analysts who feel unfamiliar with using visualizations may be reluctant to deploy them in their work environment [3]. The benefits that visualization and interactive exploration are known to have for understanding cyber security data [5] are then lost. We propose the design of a visualization application that addresses four challenges we identified in the little explored field of web security visualization. These challenges consider the role and expertise of cyber analysts as well as the large scale and diversity of available data. We strive to create a tool based on this design that analysts will want to use to enhance their insight gathering through visual exploration.
Modelling laser-matter interactions for simple molecules

We consider the interaction of a molecule with an ultrashort laser pulse within the mean field approximation. A model has been generalized to treat the interaction of atomic hydrogen with an electromagnetic pulse. The kernel of the Coulomb potential in the momentum space is replaced by a sum of products of separable potentials supporting a bound state of the atom and built from the corresponding bound state wave function. The molecular orbitals are expressed in terms of gaussians and generated by means of a quantum chemistry software. We analyze the ionization yield as a function of the size of the gaussian basis for H2 and H2O molecules and the results are compared to those already existing in the literature.
Moderate deviations of generalized N-urn Ehrenfest models

This paper is a further investigation of the generalized N-urn Ehrenfest model introduced in Xue [Hydrodynamics of the generalized N-urn Ehrenfest model, Potential Anal. 2022. Online. Available at https://rdcu.be/cFzrj]. A moderate deviations principle from the hydrodynamic limit of the model is derived. The proof of this main result follows an exponential martingale approach introduced in C. Kipnis et al. [Hydrodynamics and large deviation for simple exclusion processes, Commun. Pure Appl. Math. 42 (1989), pp. 115–137], where a replacement lemma plays the key role. To prove the replacement lemma, the large deviations principle of the model given in Xue [Hydrodynamics of the generalized N-urn Ehrenfest model, Potential Anal. 2022. Online. Available at https://rdcu.be/cFzrj] is utilized. As applications of our main result, we further give moderate deviations of hitting times of our process and moderate deviations of our process at given moments.
AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

Architecture performance predictors have been widely used in neural architecture search (NAS). Although they are shown to be simple and effective, the optimization objectives in previous arts (e.g., precise accuracy estimation or perfect ranking of all architectures in the space) did not capture the ranking nature of NAS. In addition, a large number of ground-truth architecture-accuracy pairs are usually required to build a reliable predictor, making the process too computationally expensive. To overcome these, in this paper, we look at NAS from a novel point of view and introduce Learning to Rank (LTR) methods to select the best (ace) architectures from a space. Speciﬁcally, we propose to use Normalized Discounted Cumulative Gain (NDCG) as the target metric and LambdaRank as the training algo-rithm. We also propose to leverage weak supervision from weight sharing by pretraining architecture representation on weak labels obtained from the super-net and then ﬁne-tuning the ranking model using a small number of architectures trained from scratch. Extensive experiments on NAS benchmarks and large-scale search spaces demonstrate that our approach outperforms SOTA with a signiﬁcantly reduced search cost.
Analysis of Driving Scenario Trajectories with Active Learning

Annotating the driving scenario trajectories based only on explicit rules (i.e., knowledge-based methods) can be subject to errors, such as false positive/negative classiﬁcation of scenarios that lie on the border of two scenario classes, missing unknown scenario classes and also anomalies. On the other side, verifying the labels by the annotators is not cost-efﬁcient. For this purpose, active learning (AL) could potentially improve the annotation procedure by inclusion of an annotator/expert in an efﬁcient way. In this study, we develop an active learning framework to annotate driving trajectory time-series data. At the ﬁrst step, we compute an embedding of the time-series trajectories into a latent space in order to extract the temporal nature. For this purpose, we study three different latent space representations: multivariate Time Series t-Distributed Stochastic Neighbor Embed-ding (mTSNE), Recurrent Auto-Encoder (RAE) and Variational Recurrent Auto-Encoder (VRAE). We then apply different active learning paradigms with different classiﬁcation models to the embedded data. In particular, we study the two classiﬁers Neural Network (NN) and Support Vector Machines (SVM), with three active learning query strategies (i.e., entropy, margin and random). In the following, we explore the possibilities of the framework to discover unknown classes and demonstrate how it can be used to identify the out-of-class trajectories.
Sampling-based multi-agent choreography planning: a metric space approach

—We present a metric space approach for high-dimensional sample-based trajectory planning. Sample-based methods such as RRT and its variants have been widely used in robotic applications and beyond, but the convergence of such methods is known only for the speciﬁc cases of holonomic systems and sub-Riemannian non-holonomic systems. Here, we present a more general theory using a metric-based approach and prove the algorithm’s convergence for Euclidean and non-Euclidean spaces. The extended convergence theory is valid for joint planning of multiple heterogeneous holonomic or non-holonomic agents in a crowded environment in the presence of obstacles. We demonstrate the method both using abstract metric spaces ( (cid:96) p geometries and fractal Sierpinski gasket) and using a multi-vehicle Reeds-Shepp vehicle system. For multi-vehicle systems, the degree of simultaneous motion can be adjusted by varying t.he metric on the joint state space, and we demonstrate the effects of this choice on the resulting choreographies.
On the Exponential Sample Complexity of the Quantum State Sign Estimation Problem

We demonstrate that the ability to estimate the relative sign of an arbitrary $n$-qubit quantum state (with real amplitudes), given only $k$ copies of that state, would yield a $kn$-query algorithm for unstructured search. Thus the quantum sample complexity of sign estimation must be exponential: $\Omega(2^{n/2}/n)$. In particular, we show that an efficient procedure for solving the sign estimation problem would allow for a polynomial time solution to the NP-complete problem 3-SAT.
Characterization of Laser-Induced Ionization Dynamics in Solid Dielectrics

Peter Jürgens, Benjamin Liewehr, Björn Kruse, Christian Peltz, Tobias Witting, Anton Husakou, Arnaud Rouzeé, Mikhail Ivanov, Thomas Fennel, Marc J. J. Vrakking, and Alexandre Mermillod-Blondin Max-Born-Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin, Germany Institute of Physics, University of Rostock, Albert-Einstein-Str. 23, D-18059 Rostock, Germany
GLASS: Geometric Latent Augmentation for Shape Spaces

We investigate the problem of training generative models on very sparse collections of 3D models. Particularly, instead of using difficult-to-obtain large sets of 3D models, we demonstrate that geometrically-motivated energy functions can be used to effectively augment and boost only a sparse collection of example (training) models. Technically, we analyze the Hessian of the as-rigid-as-possible (ARAP) energy to adaptively sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process, of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. We evaluate our method against a set of strong baselines, provide ablation studies, and demonstrate application towards establishing shape correspondences. Glassproduces multiple interesting and meaningful shape variations even when starting from as few as 3–10 training shapes. Our code is available at https://sanjeevmk.github. io/glass_webpage/.
TS4Net: Two-Stage Sample Selective Strategy for Rotating Object Detection

Rotating object detection has wide applications in aerial photographs, remote sensing images, UAVs, etc. At present, most of the rotating object detection datasets focus on the field of remote sensing, and these images are usually shot in high-altitude scenes. However, image datasets captured at low-altitude areas also should be concerned, such as drone-based datasets. So we present a low-altitude dronebased dataset, named UAV-ROD, aiming to promote the research and development in rotating object detection and UAV applications. The UAV-ROD consists of 1577 images and 30,090 instances of car category annotated by oriented bounding boxes. In particular, The UAV-ROD can be utilized for the rotating object detection, vehicle orientation recognition and object counting tasks. Compared with horizontal object detection, the regression stage of the rotation detection is a tricky problem. In this paper, we propose a rotating object detector TS4Net, which contains anchor refinement module (ARM) and two-stage sample selective strategy (TS4). The ARM can convert preseted horizontal anchors into high-quality rotated anchors through twostage anchor refinement. The TS4 module utilizes different constrained sample selective strategies to allocate positive and negative samples, which is adaptive to the regression task in different stages. Benefiting from the ARM and TS4, the TS4Net can achieve superior performance for rotating object detection solely with one preseted horizontal anchor. Extensive experimental results on UAV-ROD dataset and three remote sensing datasets DOTA, HRSC2016 and UCAS-AOD demonstrate that our method achieves competitive performance against most state-of-the-art methods.
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints

Abstract Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive Logic Programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints à la IFF, possibly annotated with a probability value. We first present the overall abductive language and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.
Two Basic Queueing Models of Service Platforms in Digital Sharing Economy

This paper describes two basic queueing models of service platforms in digital sharing economy by means of two different policies of platform matching information. We show that the two queueing models of service platforms can be expressed as the level-independent quasi birth-and-death (QBD) processes. Using the proposed QBD processes, we provide a detailed analysis for the two queueing models of service platforms, including the system stability, the average stationary numbers of seekers and of idle owners, the expected sojourn time of an arriving seeker, and the expected profits for both the service platform and each owner. Finally, numerical examples are employed to verify our theoretical results, and demonstrate how the performance measures of service platforms are influenced by some key system parameters. We believe that the methodology and results developed in this paper not only can be applied to develop a broad class of queuing models of service platforms, but also will open a series of promising innovative research on performance evaluation, optimal control and queueing-game of service platforms and digital sharing economy.
I-DLV-sr: A Stream Reasoning System based on I-DLV

Abstract We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the $${{\mathcal I}^2}$$ -DLV system. The architecture allows to take advantage from both the powerful distributed stream processing capabilities of Flink and the incremental reasoning capabilities of $${{\mathcal I}^2}$$ -DLV, based on overgrounding techniques. Besides the system architecture, we illustrate the supported input language and its modeling capabilities, and discuss the results of an experimental activity aimed at assessing the viability of the approach.
Towards a Semantics for Hybrid ASP systems

Over the last decades the development of ASP has brought about an expressive modeling language powered by highly performant systems. At the same time, it gets more and more difficult to provide semantic underpinnings capturing the resulting constructs and inferences. This is even more severe when it comes to hybrid ASP languages and systems that are often needed to handle real-world applications. We address this challenge and introduce the concept of abstract and structured theories that allow us to formally elaborate upon their integration with ASP. We then use this concept to make precise the semantic characterization of CLINGO's theory-reasoning framework and establish its correspondence to the logic of Here-and-there with constraints. This provides us with a formal framework in which we can elaborate formal properties of existing hybridizations of CLINGO such as CLINGCON, CLINGOM[DL], and CLINGO[LP].
The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks

This paper proposes the importance of age and gender information in the diagnosis of diabetic retinopathy. We utilized Deep Residual Neural Networks (ResNet) and Densely Connected Convolutional Networks (DenseNet), which are proven effective on image classification problems and the diagnosis of diabetic retinopathy using the retinal fundus images. We used the ensemble of several classical networks and decentralized the training so that the network was simple and avoided overfitting. To observe whether the age and gender information could help enhance the performance, we added the information before the dense layer and compared the results with the results that did not add age and gender information. We found that the test accuracy of the network with age and gender information was 2.67% higher than that of the network without age and gender information. Meanwhile, compared with gender information, age information had a better help for the results.Clinical Relevance— The additional information in the dataset (such as age, gender, time of illness, etc.) can improve the accuracy of automatic diagnosis. Therefore, we strongly recommend that researchers add these different kinds of additional information when creating the dataset.
How the Analyzer can Help the User Help the Analyzer

The automation offered by modern program proof tools goes hand in hand with the capability to interact with the tool when the verification fails. The SPARK proof tool tries to help the user by providing the right information, so that the user can help the tool complete the proof. In this article, we present these mechanisms and how they work concretely on a simple running example.