Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation

For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex. In this work, we equip the adversarial network with a "significance-aware information bottleneck (SIB)", to address the above problem. The new network structure, called SIBAN, enables a significance-aware feature purification before the adversarial adaptation, which eases the feature alignment and stabilizes the adversarial training course. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method can yield leading results compared with other feature-space alternatives. Moreover, SIBAN can even match the state-of-the-art output-space methods in segmentation accuracy, while the latter are often considered to be better choices for domain adaptive segmentation task.
Giant radio galaxies in the LOFAR Two-metre Sky Survey

Giant radio galaxies (GRGs) are a subclass of radio galaxies, which have grown to megaparsec scales. GRGs are much rarer than normal-sized radio galaxies (< 0.7 Mpc) and the reason for their gigantic sizes is still debated. Here, we report on the biggest sample of GRGs identified to date. These objects were found in the LOFAR Two-metre Sky Survey first data release images, which cover a 424 deg2 region. Of the 239 GRGs found, 225 are new discoveries. The GRGs in our sample have sizes ranging from 0.7 Mpc to 3.5 Mpc and have redshifts (z) between 0.1 and 2.3. Seven GRGs have sizes above 2 Mpc and one has a size of ∼3.5 Mpc. The sample contains 40 GRGs hosted by spectroscopically confirmed quasars. Here, we present the search techniques employed and the resulting catalogue of the newly discovered large sample of GRGs along with their radio properties. In this paper, we also show for the first time that the spectral index of GRGs is similar to that of normal-sized radio galaxies, indicating that most of the GRG population is not dead or is not similar to a remnant-type radio galaxy. We find that 20 out of 239 GRGs in our sample are located at the centres of clusters and we present our analysis on their cluster environment and radio morphology.
Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings

Abbreviations often have several distinct meanings, often making their use in text ambiguous. Expanding them to their intended meaning in context is important for Machine Reading tasks such as document search, recommendation and question answering. Existing approaches mostly rely on manually labeled examples of abbreviations and their correct long-forms. Such data sets are costly to create and result in trained models with limited applicability and flexibility. Importantly, most current methods must be subjected to a full empirical evaluation in order to understand their limitations, which is cumbersome in practice. In this paper, we present an entirely unsupervised abbreviation disambiguation method (called UAD) that picks up abbreviation definitions from unstructured text. Creating distinct tokens per meaning, we learn context representations as word vectors. We demonstrate how to further boost abbreviation disambiguation performance by obtaining better context representations using additional unstructured text. Our method is the first abbreviation disambiguation approach with a transparent model that allows performance analysis without requiring full-scale evaluation, making it highly relevant for real-world deployments. In our thorough empirical evaluation, UAD achieves high performance on large real-world data sets from different domains and outperforms both baseline and state-of-the-art methods. UAD scales well and supports thousands of abbreviations with multiple different meanings within a single model. In order to spur more research into abbreviation disambiguation, we publish a new data set, that we also use in our experiments.
Consensus of Nonlinear Multi-agent Systems with Event-triggered Control

We propose an event-triggered control strategy for the consensus problem of nonlinear multiple agents connected by a network. Considering a network with fixed topology,we design an event-triggered consensus protocol and give the sufficient conditions for consensus. Using Lyapunov stability theory,the nonlinear multiple agents in the network can converge to the given objective state. The corresponding theoretical results are extended to the situation of a network with simultaneously switching topology. The validity of proposed control strategy is verified by simulation examples.
The Effect of Managerial Competencies on Employee Engagement in Multinational IT Industries

-In the work environment and the competitive world demands that the managers should use their competencies to perform a variety of behavior and tasks that result in products and services catered to different stakeholders. For any successful organization, team of talented dynamic executioners, passionate leaders in right roles will make sure effective execution of strategies and correction if needed as per the situation. The quality of the inputs in the form of behavior and tasks has an influence on the people who receive them which results positive and negative outcomes of an organization. Effective organizations anticipate the leadership and talent requirement to succeed in the future. This means that the success of an organization is primarily indebted to the competencies of their managers. The core idea of this research is to carry out an empirical investigation on the different factors of managerial competencies, their role and effectiveness on employee engagement.
The semicircle law for matrices with ergodic entries

We study the empirical spectral distribution (ESD) of symmetric random matrices with ergodic entries on the diagonals. We observe that for entries with correlations that decay to 0, when the distance of the diagonal entries becomes large the limiting ESD is the well known semicircle law. If it does not decay to 0 (and have the same sign) the semicircle law cannot be the limit of the ESD. This is good agreement with results on exchangeable processes analysed in Friesen and Löwe (2013a) and Hochstättler et al. (2016).
Research and Perspective on Shape Matching

Shape matching and classification are important issues in computer vision. In recent years, contour-based shape matching approaches (e.g., shape context) and skeleton-based shape matching methods (e.g., shock graph) both have a lot of developments. In this paper, we introduce the basic concept of shape matching, give the di? culties of this topic, and provide a detailed review on the most recent approaches about shape representation and matching for both contour-based and skeleton-based methods. We also give a brief introduction about metric learning based shape retrieval. Moreover, we introduce some widely used benchmarks for shape matching in details, along with some hot topics including partial shape matching, shape classification, etc. Finally, this paper concludes with the whole framework of shape matching and the application perspective of this topic.
An Application of Liaison Theory to Zero-dimensional Schemes

Given a 0-dimensional scheme X in a n-dimensional projective space P^n_K over an arbitrary field K, we use Liaison theory to characterize the Cayley-Bacharach property of X. Our result extends the result for sets of K-rational points given in [7]. In addition, we examine and bound the Hilbert function and regularity index of the Dedekind different of X when X has the Cayley-Bacharach property.
Lattice QCD calculations of the quark and gluon contributions to the proton spin

A review of the calculations of the proton's spin using lattice QCD is presented. Results for the three contributions, the quark contribution $\sum_{q=u,d,s,c} (\frac{1}{2} {\Delta q})$, the total angular momentum of the quarks $J_q$ and of the gluons $J_g$, and the orbital angular momentum of the quarks are discussed. The best measured is the the quark contribution $\sum_{q=u,d,s,c} (\frac{1}{2} {\Delta q})$, and its analysis is used to discuss the relative merits of calculations by the PNDME, ETMC and $\chi$QCD collaborations and the level of control over systematic errors achieved in each. The result by the PNMDE collaboration, $\sum_{q=u,d,s} \left[ \frac{1}{2} {\Delta q} \right] = 0.143(31)(36) $, is consistent with the COMPASS analysis $0.13 < \frac{1}{2} \Delta \Sigma < 0.18$. Results for $J_q$ and $J_g$ by the ETMC collaborations are also consistent with phenomenology. Lastly, I review first results from the LHPC collaboration for the calculation of the orbital angular momentum of the quarks. With much larger computing resources anticipated over the next five years, high precision results for all three will become available and provide a detailed description of their relative contributions to the nucleon spin.
Are H0 and σ8 Tensions Generic to Present Cosmological Data?

Yes, for a wide range of cosmological models (ΛCDM, non-interacting wzCDM, wzWDM, or a class of interacting DMDE). Recently there have been attempts to solve the tension between direct measurements of H0 and from respective low-redshift observables and indirect measurements of these quantities from observations of the cosmic microwave background (CMB). In this work we construct a quasi-model-independent framework that reduces to different classes of cosmological models under suitable choices of parameters. We test this parameterization against the latest Planck CMB data combined with recent measurements of baryon acoustic oscillations (BAO) and supernovae, and direct measurements of H0. Our analysis reveals that a strong positive correlation between H0 and σ8 is more or less generic for most of the cosmological models. The present data slightly prefer a phantom equation of state for dark energy and a slightly negative effective equation of state for dark matter (a direct signature of interacting models), with a relatively high H0 consistent with Planck+R16 data and simultaneously a consistent . Thus, even though the tensions cannot be fully resolved, a class of interacting models with phantom wDE get a slight edge over wzCDM for the present data. However, although they may resolve the tension between high-redshift CMB data and individual low-redshift data sets, these data sets have inconsistencies between them (e.g., between BAO and H0, supernovae and BAO, and cluster counts and H0).
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three main factors: powerful computers, new techniques to train deeper networks and larger datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields nowadays, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. When it comes to the Oil&Gas industry, confidentiality issues hamper even more the sharing of datasets. In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset acquisition was carried out in the North Sea, Netherlands offshore. The data is publicly available and contains pos-stack data, 8 horizons and well logs of 4 wells. For the purposes of our machine learning tasks, the original dataset was reinterpreted, generating 9 horizons separating different seismic facies intervals. The interpreted horizons were used to generate approximatelly 190,000 labeled images for inlines and crosslines. Finally, we present two deep learning applications in which the proposed dataset was employed and produced compelling results.
Expert System for Social Assistance and Grant Selection Using Analytical Hierarchy Process

The city government in Indonesia allocates the budget for grant and social assistance that sourced from their local government budget annually. According to observation phase, there are several problems found and the one biggest are subjectivity issue. In addition, process of decision making is still using the way that take a long time because of all process doing by observe the completeness of each applicant’s file grants and social assistance, then compare the assessed and estimated who deserves grants and social assistance. This work proposed a design of expert system that can decrease the number of subjectivity issue including implement analytical hierarchy process (AHP) method in experiment phase. In evaluation phase this design shows exactly 70% accuration value.
A Classifier Ensemble Machine Learning Approach to Improve Efficiency for Missing Value Imputation

In data mining process the biggest task of data preprocessing is missing value imputation. Imputation is a statistical process of replacing missing data with substituted values. Many clinical diagnostic dataset are usually incomplete. Excluding incomplete dataset from the original dataset can bring more problem than simplification. In this paper the machine learning techniques for missing value imputation have been explored using Ionosphere data from UCI repository. The data imputation problem has been approached using well-know machine learning techniques. Several different statistical & data mining methods have been compared in this paper. The experiments have shown that the final classifier performance increases when Support Vector Machine (SVM) is used. Experiments show that popular machine learning classifier techniques were found to outperform than standard mean/mode imputation techniques. The advantage of both knowledge representation models are combined together in hybrid machine learning algorithms. The result shows that hybrid Logistic Regression (via Generalized Linear Model or GLM) or Random Forest outperforms the standalone Support Vector Machine (SVM). Machine learning algorithms from R/Revolution environments have been used.
Application of DInSAR Technique to High Coherence Sentinel-1 Images for Dam Monitoring and Result Validation Through In Situ Measurements

Dam monitoring represents a crucial issue in order to avoid catastrophic failures due to infrastructure aging or earthquake damages. Differential SAR Interferometry (DInSAR) is a technique suitable for critical infrastructure monitoring, also for the availability of free data and tools, that can be used by experts in SAR remote sensing and also by geologists and civil engineers, after having acquired the right confidence and experience in these data processing and tool use. In order to apply the DInSAR technique, in its basic and simple version, to critical infrastructure monitoring, it is very important to assess its performance. Nevertheless, validation results are not largely available in literature, because heterogeneous technical competencies are required to this aim and in situ measurements must be collected and made available. In this paper, we propose a highly reproducible DInSAR workflow that can be effectively used for dam monitoring, by validating its results with in situ measurements on some significant case studies in Italy.
A Numerical Study of Quench in the NHMFL 32 T Magnet

The National High Magnetic Field Laboratory (NHMFL), Tallahassee, FL, USA, has developed, built, tested, and commissioned a 32 T all-superconducting user magnet system combining two series-connected high-field high-temperature superconductor (HTS) nested inner coils (insert) wound with SuperPower, Inc. REBCO tapes and a low temperature superconducting (LTS) outer magnet (outsert) composed of five coils (subdivided into 17 electrical sections). Protected-quench tests were performed at the NHMFL to analyze the reliability of the 32 T magnet during the superconducting-to-normal transition. The quench tests were performed at different values of transport current both in the HTS insert and in the LTS outsert. The University of Bologna, in collaboration with the NHMFL, has developed a quasi-3-D FE model suited for the analysis of quench in HTS magnets. The model was previously applied to the analysis of the experimental results of the quench tests carried out on the prototype coils, manufactured in the framework of the 32 T magnet R&D activities. In this paper, the numerical model is applied to analyze the quench initiation and propagation in the 32 T HTS insert. The dump of the transport current during quench is computed and compared with the experimental result. The most stressed regions within the insert windings are identified.
Incorporating Full Attenuation Mechanisms of Poroelastic Media for Realistic Subsurface Sensing

Porous materials are ubiquitous in the subsurface formations of the earth where acoustic and seismic waves are used for remote sensing. However, it is not well understood how the dissipation and the dispersion of poroelastic waves are caused by the viscoelastic and viscous properties of the constituents such as solid grains and pore fluid and by the viscoelastic dissipation of the solid frame, as well as the viscodynamic coupling of the pore fluid to the solid frame due to its global and local flows relative to the solid grains. Such attenuation mechanisms have seldom been incorporated in subsurface sensing simulations, although they can be very important to applications. In this paper, we propose a complete attenuation model, including both full stiffness and viscodynamic dissipation, for poroelastic media in seismic wave simulations. Completely based on a generalized Zener model, the effects associated with physical dissipation and frequency-dependent dispersion are accurately simulated by a finite-difference time-domain algorithm. Verifications with analytical solutions show the accuracy, efficiency, and flexibility of our method. Numerical results demonstrate that the attenuation of Biot’s model in the sediment of the seafloor has significant effects on acoustic wave scattering from complex geologic structures.
Co-Design of Controllers and a Switching Policy for Nonstrict Feedback Switched Nonlinear Systems Including First-Order Feedforward Paths

This paper studies the stabilization problem via the co-design of controllers and a switching policy for a new class of nonstrict feedback switched nonlinear systems whose subsystems consist of a chain of integrators, feedback paths, and first-order feedforward paths. Designing only smooth feedback controllers cannot deal with the unstabilizable factors caused by feedforward paths. By exploiting the single control Lyapunov function method, an effective switching policy is co-designed to compensate the controllers. In addition, we present a generalized backstepping process, based on which the solvability of virtual controllers is guaranteed, the algebraic condition for stabilizability is identified, and the transient response of the closed-loop systems is improved. Two examples demonstrate the effectiveness.
Distributed Kalman Filtering Over Wireless Sensor Networks in the Presence of Data Packet Drops

We study distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a linear time-invariant discrete-time system, using its own observations and those transmitted from its neighbors in the presence of data packet drops. This is an optimal one-step prediction problem under the framework of distributed estimation, assuming the TCP-like protocol. We first present the stationary distributed Kalman filter (DKF) that minimizes the local average error variance in the steady state at each sensor node, based on the stabilizing solution to the corresponding modified algebraic Riccati equation (MARE). The existence of the stabilizing solution to the MARE is addressed by adopting the stability margin, which can be computed by solving a set of linear matrix inequalities. Then, the Kalman consensus filter (KCF), consisting of the stationary DKF and a consensus term of prior estimates, is studied. Finally, the performance of the stationary DKF and KCF is illustrated by a numerical example.
Performance Analysis of Content Based Image Retrieval Systems

This paper presents an analysis of using Faiss[8], an open source similarity search library developed at Facebook research, and applying it for the purpose of content based image retrieval(CBIR) using high dimensional feature vectors. Faiss provides novel indexing methods which we have applied for image retrieval applications. Two image descriptors, namely 3-D histograms in HSV color space and convolutional neural network(VGG16) have been used. A third descriptor, which is the concatenation of the two has also been used. This paper shows how the use of Faiss for indexing has significantly reduced the query time with only marginal loss in accuracy. We investigate the use of two different types of image descriptors as mentioned above and how the performance on retrieval differs before and after using Faiss for indexing on some popular datasets.
An Arithmetic and Logical Unit using Reversible Gates

This paper presents novel one bit arithmetic and logical unit (ALU) design using basic reversible gates. Two designs for both the arithmetic and the logical unit are proposed and one design for the control unit is also put forward. These designs are integrated together to give four complete ALU designs. Each of the proposed design has been implemented in Verilog HDL using Xilinx ISE Suite 14.1 software to verify its functionality. The proposed designs are compared based on quantum cost, garbage outputs and ancilla inputs with their existing counterparts. The simplicity and the reduced quantum cost of the proposed ALU designs, when compared to the existing ones, makes them ideal candidates to be used as modules in quantum computers. The proposed ALUs extend their applications to cryptography, machine learning and nanotechnology.
Event-Based Distributed Filtering Over Markovian Switching Topologies

In this paper, we consider the distributed filtering problem for continuous-time stochastic systems over sensor networks subject to Markovian switching topologies. Due to limited communication energy and bandwidth, an event-based communication scheme is proposed with the aim to decrease the transmission frequency. An individual triggering condition is put forward to regulate the communication rates for each component of the system state in order to better reflect the engineering requirements. The aim of this paper is to design a distributed filter over sensor networks with Markovian switching topologies such that the dynamics of the estimation error is exponentially mean-square bounded. It is shown that, with the proposed event-based distributed filtering algorithm, the exponential mean-square boundedness of the estimation errors is guaranteed if the sensor network is distributively detectable and the combined communication topology is strongly connected. A numerical example is presented to illustrate the usefulness of the developed algorithm.