Miracle-dz/UniFAR-FTUs huggingface dataset:
Social media, open innovation ampamp; HRM_ Implications for performance

Firms are increasingly leveraging social media tools to access knowledge from external actors, particularly customers and other users, to facilitate the innovation process and firm performance. Yet empirical research investigating the impact of external knowledge sourced via social media tools is scant; empirical studies that do exist are mixed, leading to calls for research into the conditions under which knowledge flows via social media from external actors contribute to innovation and firm performance. Using a large-scale survey of firms in Tasmania, Australia, this study examines how external knowledge flows from market-based actors sourced by social media influence innovation and business performance, and the extent to which modern human resource management (HRM) practices moderate this relationship. We find that while knowledge flows from marketbased actors are positively related to innovativeness, the relationship between external knowledge flows via social media and innovativeness depends on the importance a firm places on modern HRM practices: a significant positive relationship exists between knowledge sourced via social media and innovativeness when firms attach high importance to modern HRM practices. In contrast, there is no significant relationship in firms in which modern HRM practices are of low importance. The study also shows that social media serves as a mediator for the effect of external knowledge flows on firm innovativeness when firms attach high importance to modern HRM practices. Furthermore, while the results demonstrate that innovativeness and firm performance are positively related, innovativeness does not translate into improved firm performance in firms that attach low importance to modern HRM practices. Taken together, the findings underscore the importance of modern HRM practices to enable knowledge inflows via social media to influence innovativeness, and innovativeness to translate into productivity benefits.
Learning Situational Driving

Human drivers have a remarkable ability to drive in diverse visual conditions and situations, e.g., from maneuvering in rainy, limited visibility conditions with no lane markings to turning in a busy intersection while yielding to pedestrians. In contrast, we find that state-of-the-art sensorimotor driving models struggle when encountering diverse settings with varying relationships between observation and action. To generalize when making decisions across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios. Our key idea is to learn a mixture model with a set of policies that can capture multiple driving modes. We first optimize the mixture model through behavior cloning and show it to result in significant gains in terms of driving performance in diverse conditions. We then refine the model by directly optimizing for the driving task itself, i.e., supervised with the navigation task reward. Our method is more scalable than methods assuming access to privileged information, e.g., perception labels, as it only assumes demonstration and reward-based supervision. We achieve over 98% success rate on the CARLA driving benchmark as well as state-of-the-art performance on a newly introduced generalization benchmark.
Near Infrared Spectroscopy alike Magnetic Resonance Imaging: Complementary Data in Rat Brain after Cocaine Treatment

Magnetic Resonance Imaging (MRI) and Near Infrared Spectroscopy (NIRS) are two major in vivo non invasive methodologies more and more applied in research. The first more than the second is largely used also in clinical domain. Both techniques are more or less related to the effectiveness of oxygen levels and/or functionality in blood and this can be exploited to monitor the influence of various factors and conditions upon the living tissue, in particular the brain. Here the complementarity of these two methodologies is challenged via comparison of the effect of cocaine treatment upon NIRS as well as MRI parameters monitored in vivo in rat brain. The aim of the study is to further support recent data obtained with our early introduced NIRS prototype to monitor hematic changes in CNS showing that NIRS is allowing evaluating rat blood brain barrier penetration of exogenous agents and demonstrating parallel alteration of brain metabolism following alcohol intake in rodents and man. Positive evidence will further confirm the utility of such prototype for real time translational rodent-man in vivo non invasive studies. The parallel MRI-NIRS data monitored confirm previous results obtained with these two non invasive methodologies and further support NIRS as a valuable tool for non invasive in vivo real time analysis of brain metabolism AND of drug treatments in the CNS.
Clinical Potential of miRNAs in Human and Infectious Diseases

MicroRNAs (miRNAs) are small non-coding RNA molecules that play critical roles in human disease. Several miRnome profiling studies have identified miRNAs deregulated in cancer and infectious diseases and miRNAs are also involved in regulation of the host response to infection. Thereby, the usage of miRNAs as biomarkers and potential treatments for both human and infectious diseases is under development. This review will provide insights into the contribution of miRNAs to pathogenesis and disease development and will present a general outline of the potential use of miRNAs as therapeutic tools.
Characterizing the global ocean ambient noise as recorded by the dense seismo-acoustic Kazakh network

The dense seismo-acoustic network of the Institute of Geophysical Research (IGR), National Nuclear Center of the Republic of Kazakhstan, has been operating in Kazakhstan since the late nineties of the last century. It consists of four seismic and three infrasonic arrays. The IGR network includes stations part of several national and global monitoring systems. Infrasonic and seismic data are processed using the Progressive Multi-Channel Correlation (PMCC) detector to characterize 15 the temporal variability of microbarom and microseism signals from 2014 to 2017. The non-linear interaction of ocean waves is simulated using the microseism source model distributed by the French Research Institute for Exploitation of the Sea (IFREMER). The wave attenuation is calculated using a semi-empirical propagation law in a range independent atmosphere. Comparing the observed and predicted seismic and infrasonic signals reveals the dominating directions of arrivals at each station of the IGR network and the associated source regions. Both multi-year and intra-seasonal parameter variations are 20 analysed. The level of low-frequency noise is significantly higher in winter than in summer. In winter, sources of infrasound ambient noise are mainly located in the North Atlantic and in the North Pacific during Sudden Stratospheric Warming (SSW) events. Signals observed in summer, likely originating from source regions in the southern hemisphere, are discussed.
Arthrospira platensis improves macrophages arginine metabolism and prevents insulin resistance in Psammomys obesus

Introduction: Chronic low grade inflammation is generally linked to the development of obesity-related metabolic diseases and tissue-resident macrophages play a major role in the regulation of the inflammation markers in the tissues. The cyanobacterium Arthrospira platensis also named spirulina is a blue green microalgae which has anti-inflammatory and anti diabetic properties. This study aimed to evaluate its potential therapeutic benefits on insulin resistance and peritoneal macrophages arginine metabolism in experimental gerbils named Psammomys obesus. Methods: A control group was fed its vegetable -based low caloric diet (halophilic plants). Two experimental groups were fed a high caloric diet for 12 weeks. Spirulina, or its vehicle (0.9% NaCl solution) were administrated daily by gastric intubation during 12 weeks. At the end of experiments, serum was collected to assess biochemical parameters and arginine metabolism was studied from isolated peritoneal macrophages. Results: Spirulina attenuated hyperlipidemia and prevented hyperinsulinemia, avoiding metabolic disturbances. Psammomys fed a high caloric diet displayed macrophages arginine metabolism toward pro-inflammatory phenotype while spirulina supplementation maintained peritoneal macrophages’ iNOS activity similar to that of the control group. Conclusion: Our study suggests that Arthrospira platensis is effective in improving insulin sensitivity by regulating peritoneal macrophages’ arginine metabolism.
From Algebraic Word Problem to Program: A Formalized Approach

In this paper, we propose a pipeline to convert grade school level algebraic word problem into program of a formal languageA-IMP. Using natural language processing tools, we break the problem into sentence fragments which can then be reduced to functions. The functions are categorized by the head verb of the sentence and its structure, as defined by (Hosseini et al., 2014). We define the function signature and extract its arguments from the text using dependency parsing. We have a working implementation of the entire pipeline which can be found on our github repository.
Extension of the total mass of log-concave functions and related inequalities

The aim of this paper is to deal with the log-concave functions in $\mathbb R^n$, endowed with a suitable algebraic structure corresponds to the structure of convex bodies in $\mathbb R^n$, when restricted to the subclass of characteristic functions. In this paper, the functional Quermassintegrals of log-concave functions in $\mathbb R^n$ are discussed, we express the functional mixed Quermassintegrals as the integral of the support function of $f$ on some measures. We obtain a functional counterpart of the mixed Quermassintegrals inequality for convex bodies. Moreover, as a special case a weak log Quermassintegral inequality is obtained.
BlockMarkchain: A Secure Decentralized Data Market with a Constant Load on the Blockchain

In this paper, we develop BlockMarkchain, as a secure data market place, where individual data sellers can exchange certified data with buyers, in a secure environment, without any mutual trust among the parties, and without trusting on a third party, as a mediator. To develop this platform, we rely on a smart contract, deployed on a secure public blockchain. The main challenges here are to verify the validity of data and to prevent malicious behavior of the parties, while preserving the privacy of the data and taking into account the limited computing and storage resources available on the blockchain. In BlockMarkchain, the buyer has the option to dispute the honesty of the seller and prove the invalidity of the data to the smart contract. The smart contract evaluates the buyer's claim and punishes the dishonest party by forfeiting his/her deposit in favor of the honest party. BlockMarkchain enjoys several salient features including (i) the certified data has never been revealed on the public blockchain, (ii) the size of data posted on the blockchain, the load of computation on the blockchain, and the cost of communication with the blockchain is constant and negligible, and (iii) the computation cost of verifications on the parties is not expensive.
Fundamentals of quantum mechanics in Liouville space

The purpose of this paper is to articulate a coherent and easy-to-understand way of doing quantum mechanics in any finite-dimensional Liouville space, based on the use of a Kronecker product and what we have termed the ‘bra-flipper’ operator. One of the greater strengths of the formalism expatiated on here is the striking similarities it bears with Dirac’s bra-ket notation. For the purpose of illustrating how the formalism can be effectively employed, we use it to solve a quantum optical master equation for a two-level quantum system and find its Kraus operator sum representation. The paper is addressed to students and researchers with some basic knowledge of linear algebra who want to acquire a deeper understanding of the Liouville space formalism. The concepts are conveyed so as to make the application of the formalism to more complex problems in quantum physics straightforward and unencumbered.
Visual-Inertial Telepresence for Aerial Manipulation

This paper presents a novel telepresence system for enhancing aerial manipulation capabilities. It involves not only a haptic device, but also a virtual reality that provides a 3D visual feedback to a remotely-located teleoperator in real-time. We achieve this by utilizing onboard visual and inertial sensors, an object tracking algorithm and a pregenerated object database. As the virtual reality has to closely match the real remote scene, we propose an extension of a marker tracking algorithm with visual-inertial odometry. Both indoor and outdoor experiments show benefits of our proposed system in achieving advanced aerial manipulation tasks, namely grasping, placing, force exertion and peg-in-hole insertion.
Global μ-stability and finite-time control of octonion-valued neural networks with unbounded delays

Octonion-valued neural networks (OVNNs) are a type of neural networks for which the states and weights are octonions. In this paper, the global $\mu$-stability and finite-time stability problems for octonion-valued neural networks are considered under unbounded and asynchronous time-varying delays. To avoid the non-communicative and non-associative multiplication feature of the octonions, we firstly decompose the OVNNs into eight real-valued neural networks (RVNNs) equivalently. Through the use of generalized norm and the Cauchy convergence principle, we obtain the sufficient criteria which assure the existence, uniqueness of the equilibrium point and global $\mu$-stability of OVNNs. By adding controllers, the criteria to ensure the finite-time stability for OVNNs are presented by dividing the analysis of finite-time stability process into two phases. Furthermore, we also prove the adaptive finite time stability theory of above networks. At last, the simulation results of specified examples is given to substantiate the effectiveness and correctness of the theoretical results.
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibility to the underlying distributed computing frameworks. These challenges include frequent interaction with simulations, the need for dynamic scaling, and the need for a user interface with low adoption cost and consistency across different backends. In this paper we address these challenges while still retaining development efficiency and flexibility for both research and practical applications by introducing Fiber, a scalable distributed computing framework for RL and population-based methods. Fiber aims to significantly expand the accessibility of large-scale parallel computation to users of otherwise complicated RL and population-based approaches without the need to for specialized computational expertise.
What Deep CNNs Benefit From Global Covariance Pooling: An Optimization Perspective

Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task. Despite considerable advance, the reasons on effectiveness of GCP on deep CNNs have not been well studied. In this paper, we make an attempt to understand what deep CNNs benefit from GCP in a viewpoint of optimization. Specifically, we explore the effect of GCP on deep CNNs in terms of the Lipschitzness of optimization loss and the predictiveness of gradients, and show that GCP can make the optimization landscape more smooth and the gradients more predictive. Furthermore, we discuss the connection between GCP and second-order optimization for deep CNNs. More importantly, above findings can account for several merits of covariance pooling for training deep CNNs that have not been recognized previously or fully explored, including significant acceleration of network convergence (i.e., the networks trained with GCP can support rapid decay of learning rates, achieving favorable performance while significantly reducing number of training epochs), stronger robustness to distorted examples generated by image corruptions and perturbations, and good generalization ability to different vision tasks, e.g., object detection and instance segmentation. We conduct extensive experiments using various deep CNN architectures on diversified tasks, and the results provide strong support to our findings.
Monopole Floer Homology, Eigenform Multiplicities, and the Seifert–Weber Dodecahedral Space

We show that the Seifert-Weber dodecahedral space $\mathsf{SW}$ is an $L$-space. The proof builds on our work relating Floer homology and spectral geometry of hyperbolic three-manifolds. A direct application of our previous techniques runs into difficulties arising from the computational complexity of the problem. We overcome this by exploiting the large symmetry group and the arithmetic and tetrahedral group structure of $\mathsf{SW}$ to prove that small eigenvalues on coexact $1$-forms must have large multiplicity.
A Unified Framework for Multiclass and Multilabel Support Vector Machines

We propose a novel integrated formulation for multiclass and multilabel support vector machines (SVMs). A number of approaches have been proposed to extend the original binary SVM to an all-in-one multiclass SVM. However, its direct extension to a unified multilabel SVM has not been widely investigated. We propose a straightforward extension to the SVM to cope with multiclass and multilabel classification problems within a unified framework. Our framework deviates from the conventional soft margin SVM framework with its direct oppositional structure. In our formulation, class-specific weight vectors (normal vectors) are learned by maximizing their margin with respect to an origin and penalizing patterns when they get too close to this origin. As a result, each weight vector chooses an orientation and a magnitude with respect to this origin in such a way that it best represents the patterns belonging to its corresponding class. Opposition between classes is introduced into the formulation via the minimization of pairwise inner products of weight vectors. We also extend our framework to cope with nonlinear separability via standard reproducing kernel Hilbert spaces (RKHS). Biases which are closely related to the origin need to be treated properly in both the original feature space and Hilbert space. We have the flexibility to incorporate constraints into the formulation (if they better reflect the underlying geometry) and improve the performance of the classifier. To this end, specifics and technicalities such as the origin in RKHS are addressed. Results demonstrates a competitive classifier for both multiclass and multilabel classification problems.
Fisheye Distortion Rectification from Deep Straight Lines

This paper presents a novel line-aware rectification network (LaRecNet) to address the problem of fisheye distortion rectification based on the classical observation that straight lines in 3D space should be still straight in image planes. Specifically, the proposed LaRecNet contains three sequential modules to (1) learn the distorted straight lines from fisheye images; (2) estimate the distortion parameters from the learned heatmaps and the image appearance; and (3) rectify the input images via a proposed differentiable rectification layer. To better train and evaluate the proposed model, we create a synthetic line-rich fisheye (SLF) dataset that contains the distortion parameters and well-annotated distorted straight lines of fisheye images. The proposed method enables us to simultaneously calibrate the geometric distortion parameters and rectify fisheye images. Extensive experiments demonstrate that our model achieves state-of-the-art performance in terms of both geometric accuracy and image quality on several evaluation metrics. In particular, the images rectified by LaRecNet achieve an average reprojection error of 0.33 pixels on the SLF dataset and produce the highest peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM) compared with the groundtruth.
Branching fractions and polarizations of $D\to V(\omega,\rho, K^*) \ell \nu_\ell$ within QCD LCSR

In the paper, the physical observables for $D \to V(\omega,\rho, K^*)\ell^+\nu_\ell$ semileptonic decays are investigated thoroughly with the SSE extrapolated HFFs, which are calculated within the framework of QCD LCSR and up to twist-4. For the decay width parts, we find that $1/|V_{cq}|^2\times \Gamma^{\rm L,T,total} (D \to V\ell^+\nu_\ell)$ decrease with the increase of final meson mass, and the absolute change number of the transverse and longitudinal are almost the same. The transverse differential decay width dominates in the small $q^2$ region, while the longitudinal differential decay width dominates in the large $q^2$ region, and the dominant alternate point is near the midpoint of the whole physically feasible region. We then calculate the branching fractions by applying the $D^0(D^+)$ meson lifetime from the Particle Data Group. All those branching fraction results are lower compared to other theories, but fits well with BES-III predictions within errors. For the polarization parts, we study in detail the $q^2$ dependence of longitudinal and transverse polarizations fractions of the charged lepton and the vector meson in the final state, i.e. $P_{\rm L,T}^\ell$ and $F_{\rm L,T}^\ell$, the forward-backward asymmetry ${\cal A}_{\rm FB}^\ell$ and the lepton-side convexity parameters ${\cal C}_{\rm F}^\ell$. In the small $q^2$ region, all those polarization observations have a singularity due to the $\delta_\ell$ factor, expect for $F_{\rm {L, T}}^\ell$. With the increase of $q^2$, all polarization values tend to be more stable, thus the polarization dependence on $q^2$ is smaller. Our predictions for their corresponding average values have agreement with the theoretical group for Covariant Confining Quark Model results within the errors.
A Hybrid MPI+Threads Approach to Particle Group Finding Using Union-Find

The Friends-of-Friends (FoF) algorithm is a standard technique used in cosmological $N$-body simulations to identify structures. Its goal is to find clusters of particles (called groups) that are separated by at most a cut-off radius. $N$-body simulations typically use most of the memory present on a node, leaving very little free for a FoF algorithm to run on-the-fly. We propose a new method that utilises the common Union-Find data structure and a hybrid MPI+threads approach. The algorithm can also be expressed elegantly in a task-based formalism if such a framework is used in the rest of the application. We have implemented our algorithm in the open-source cosmological code, SWIFT. Our implementation displays excellent strong- and weak-scaling behaviour on realistic problems and compares favourably (speed-up of 18x) over other methods commonly used in the $N$-body community.