Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Video Object Segmentation via Mask Transfer Network

Accuracy and processing speed are two important factors that affect the use of video object segmentation (VOS) in real applications. With the advanced techniques of deep neural networks, the accuracy has been significantly improved, however, the speed is still far below the real-time needs because of the complicated network design, such as the requirement of the first frame fine-tuning step. To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy. The basic idea of MTN is to transfer the reference mask to the target frame via an efficient global pixel matching strategy. The global pixel matching between the reference frame and the target frame is to ensure good matching results. To enhance the matching speed, we perform the matching on a downsampled feature map with 1/32 of the original frame size. At the same time, to preserve the detailed mask information in such a small feature map, a mask network is designed to encode the annotated mask information with 512 channels. Finally, an efficient feature warping method is used to transfer the encoded reference mask to the target frame. Based on this design, our method avoids the fine-tuning step on the first frame and does not rely on the temporal cues and particular object categories. Therefore, it runs very fast and can be conveniently trained only with images, as well as being robust to unseen objects. Experiments on the DAVIS datasets demonstrate that MTN can achieve a speed of 37 fps, and also shows a competitive accuracy in comparison to the state-of-the-art methods.
Semiclassical inverse spectral problem for elastic Love waves in isotropic media

We analyze the inverse spectral problem on the half line associated with elastic surface waves. Here, we extend the treatment of Love waves [arXiv: 1908.10529] to Rayleigh waves. Under certain conditions, and assuming that the Poisson ratio is constant, we establish uniqueness and present a reconstruction scheme for the S-wave speed with multiple wells from the semiclassical spectrum of these waves.
Robust $H_\infty$ Output Feedback Control of a Rotary Capacitive Power Transfer System

This paper investigates the design of a robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> output feedback controller for a capacitive power transfer (CPT) system. Firstly, a first harmonic model of the CPT system is derived based on first harmonic approximation (FHA). Secondly, by linearizing the first harmonic model at its operation point, a linearized state space model is obtained. Thirdly, variations of coupling capacitances and load in the CPT system are described as polytopic uncertainties in the linearized state space model. Then, we propose a novel robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> output feedback controller design technique for the linearized state space model with polytopic uncertainties. Finally, both simulation and experiment are carried out to verify the effectiveness of the proposed controller design.
Native Store Extension for SAP HANA

We present an overview of SAP HANA ’s Native Store Extension ( NSE ). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA ’s in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buﬀer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buﬀer cache integrates with HANA ’s new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit conﬁguration, along with a uniﬁed persistence format, allows the hybrid column store to dynamically switch between in-memory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership ( TCO ). A new partitioning scheme supports load unit speciﬁcation at table, partition, and column level. Finally, a new advisor recommends optimal load unit conﬁgurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.
Control of Movement Impaired visuomotor generalization by inconsistent attentional contexts

Wang TS, Song JH. Impaired visuomotor generalization by inconsistent attentional contexts. J Neurophysiol 118: 1709–1719, 2017. First published June 28, 2017; doi:10.1152/jn.00089. 2017. —In daily life, people are constantly presented with situations in which they have to learn and acquire new motor skills in complex environments, where attention is often distracted by other events. Being able to generalize and perform the acquired motor action in different environments is a crucial part of visuomotor learning. The current study examined whether attentional distraction impairs generalization of visuomotor adaptation or whether consistent distraction can operate as an internal cue to facilitate generalization. Using a dual-task paradigm combining visuomotor rotational adaptation and an attention-demanding secondary task, we showed that switching the attentional context from training (dual-task) to generalization (single-task) reduced the range of transfer of visuomotor adaptation to untrained directions. However, when consistent distraction was present throughout training and generalization, visuomotor generalization was equivalent to without distractions at all. Furthermore, this attentional context-dependent generalization was evident even when sensory modality of distractions differed between training and generalization. Therefore, the general nature of the dual tasks, rather than the specific stimuli, is associated with visuomotor memory and serves as a critical cue for generalization. Taken together, we demonstrated that attention plays a critical role during sensorimotor adaptation in selecting and associating multisensory signals with motor memory. This finding provides insight into developing learning programs that are generalizable in complex daily environments.
Visual Thinking in Life Design: A Conceptual Framework of Visual Tools and Templates

This paper sheds light on the role of visual thinking tools within the process of designing your life, the application of design thinking to life design. It provides a conceptual framework showing illustrative examples of visual templates for each phase indicating its functions and benefits. Based on a thorough understanding of the functions and benefits of knowledge visualization in general, this paper sets out to provide a first overview of the use of visual thinking tools in designing your life informing and supporting practitioners and researcher for more conscious selections of visualization methods in their life design efforts.
ECIS 2018 Proceedings 11-28-2018 MEASURING SERVICE QUALITY : A SYSTEMATIC LITERATURE REVIEW

Digital transformation of industries, technologies and society changed the way of service provision and led to changing requirements on service quality. Several models for measuring service quality exist within the literature. Literature reviews on these models mostly focus on the model structure to emphasize differences or commons in e.g. number of dimensions or context of measurement. The following paper will raise this detailed level of investigation to a higher level. The main purpose is to provide an overview about different service quality measurement models within IS literature and emphasize differences between these models compared to traditional measurement scales. A systematic literature review was conducted to structure the literature and reveal further research gaps. Findings were assigned to the service typology matrix of Jaakkola et al. (2017) to gain further research gaps for service quality measurement models directly related to socio-technical change as the two dimensions of the matrix reflect two important attributes of digitization. Beneath these gaps, findings indicate that service quality measurement models are e.g. context-dependent and offer several areas for further research. The results contribute to IS literature as they structure service quality measurement models from literature on the basis of different service forms and emphasize research gaps.
Quantum creation of a toy Universe without inflation

We propose a toy model for the origin of the Universe, where the scale-invariant fluctuations are generated together with the quantum creation process of the Universe. The fluctuations arise inside an instanton in the Euclidean domain of time. In the Lorentzian point of view, the Universe emerges with passive, coherent and scale-invariant fluctuations present from the beginning, without the need of inflation or a bounce. For this mechanism to work, we need anisotropic scaling in space and time, which is realized in a toy model of Horava–Lifshitz (HL) gravity with a Lifshitz scalar field.
Effective and Efficient Network Embedding Initialization via Graph Partitioning

Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that divides the graph into several disjoint subsets, and then construct an abstract graph based on the partitions. We obtain the initialization of the embedding for each node in the graph by computing the network embedding on the abstract graph, which is much smaller than the input graph, and then propagating the embedding among the nodes in the input graph. With extensive experiments on various datasets, we demonstrate that our initialization technique significantly improves the performance of the state-of-the-art algorithms on the evaluations of link prediction and node classification by up to 7.76% and 8.74% respectively. Besides, we show that the technique of initialization reduces the running time of the state-of-the-arts by at least 20%. ACM Reference Format: Wenqing Lin, Feng He, Faqiang Zhang, Xu Cheng, Hongyun Cai. 2019. Effective and Efficient Network Embedding Initialization via Graph Partitioning. In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
A full Stokes subgrid model for simulation of grounding line migration in ice sheets using Elmer/ICE(v8.3)

Abstract. The full Stokes equations are solved by a finite element method for simulation of large ice sheets and glaciers. The simulation is particularly sensitive to the discretization of the grounding line which separates the ice resting on the bedrock and the ice floating on water and is moving in time. The boundary conditions at the ice base are enforced by Nitsche's method and a subgrid treatment of the elements in the discretization close to the grounding line. Simulations with the method in two dimensions for an advancing and a retreating grounding line illustrate the performance of the method. It is implemented in the two dimensional version of the open source code Elmer/ICE.
ALMA Observations of Fragmentation, Substructure, and Protostars in High-mass Starless Clump Candidates

The initial physical conditions of high-mass stars and protoclusters remain poorly characterized. To this end, we present the first targeted ALMA Band 6 1.3 mm continuum and spectral line survey toward high-mass starless clump candidates, selecting a sample of 12 of the most massive candidates ( ) within . The joint array maps have a high spatial resolution of ( , θsyn ≈ 0.″8) and have high point-source mass-completeness down to at (or column density sensitivity of ). We discover previously undetected signposts of low-luminosity star formation from and bipolar outflows and other signatures toward 11 out of 12 clumps, showing that current MIR/FIR Galactic plane surveys are incomplete to low- and intermediate-mass protostars ( ), and emphasizing the necessity of high-resolution follow-up. We compare a subset of the observed cores with a suite of radiative transfer models of starless cores. We find a high-mass starless core candidate with a model-derived mass consistent with when integrated over size scales of . Unresolved cores are poorly fit by radiative transfer models of externally heated Plummer density profiles, supporting the interpretation that they are protostellar even without detection of outflows. A high degree of fragmentation with rich substructure is observed toward 10 out of 12 clumps. We extract sources from the maps using a dendrogram to study the characteristic fragmentation length scale. Nearest neighbor separations, when corrected for projection with Monte Carlo random sampling, are consistent with being equal to the clump average thermal Jeans length ( ; i.e., separations equal to ). In the context of previous observations that, on larger scales, see separations consistent with the turbulent Jeans length or the cylindrical thermal Jeans scale ( ), our findings support a hierarchical fragmentation process, where the highest-density regions are not strongly supported against thermal gravitational fragmentation by turbulence or magnetic fields.
KFPA Examinations of Young STellar Object Natal Environments (KEYSTONE): Hierarchical Ammonia Structures in Galactic Giant Molecular Clouds

We present initial results from the K-band Focal Plane Array Examinations of Young STellar Object Natal Environments survey, a large project on the 100 m Green Bank Telescope mapping ammonia emission across 11 giant molecular clouds at distances of 0.9–3.0 kpc (Cygnus X North, Cygnus X South, M16, M17, Mon R1, Mon R2, NGC 2264, NGC 7538, Rosette, W3, and W48). This data release includes the NH3 (1,1) and (2,2) maps for each cloud, which are modeled to produce maps of kinetic temperature, centroid velocity, velocity dispersion, and ammonia column density. Median cloud kinetic temperatures range from 11.4 ± 2.2 K in the coldest cloud (Mon R1) to 23.0 ± 6.5 K in the warmest cloud (M17). Using dendrograms on the NH3 (1,1) integrated intensity maps, we identify 856 dense gas clumps across the 11 clouds. Depending on the cloud observed, 40%–100% of the clumps are aligned spatially with filaments identified in H2 column density maps derived from spectral energy distribution fitting of dust continuum emission. A virial analysis reveals that 523 of the 835 clumps (∼63%) with mass estimates are bound by gravity alone. We find no significant difference between the virial parameter distributions for clumps aligned with the dust-continuum filaments and those unaligned with filaments. In some clouds, however, hubs or ridges of dense gas with unusually high mass and low virial parameters are located within a single filament or at the intersection of multiple filaments. These hubs and ridges tend to host water maser emission, multiple 70 μm detected protostars, and have masses and radii above an empirical threshold for forming massive stars.
Vascular Endothelial Growth Factor as an Autocrine Survival Factor for Retinal Pigment Epithelial Cells under Oxidative Stress via the VEGF-R 2 / PI 3 K / Akt

METHODS. ARPE-19 cells were treated with hydrogen peroxide, and cell death was measured by flow cytometry with annexin V-fluorescein isothiocyanate. Survival analysis was performed with pretreatment of VEGF-A–neutralizing antibodies, VEGF receptor tyrosine kinase inhibitor (SU5416), or VEGF-A receptor-neutralizing antibodies (anti-VEGF-R1 and anti-VEGF-R2). The expression of VEGF-A, -R1, -R2, and soluble VEGF-R1 was determined by semiquantitative RT-PCR or Western blot analysis. Phosphorylation of VEGF-R2 was detected with immunoprecipitation and immunoblot analysis.
IMPLEMENTASI FACE IDENTIFICATION DAN FACE RECOGNITION PADA KAMERA PENGAWAS SEBAGAI PENDETEKSI BAHAYA

Rasa aman merupakan salah satu kebutuhan yang sangat penting bagi manusia dalam menghadapi era teknologi saat ini. Keingintahuan pada keadaan atau kondisi hal yang dimiliki juga menjadi faktor penting bagi manusia untuk terus mengembangkan sistem keamanan gedung maupun rumah. Sistem dapat mendeteksi objek berupa wajah dari masukan citra. Sistem akan memerlukan masukan citra dari sebuah kamera keamanan. Setelah objek berupa wajah terdeteksi, sistem akan melakukan pencocokan wajah dengan gambar wajah yang terdapat pada database sistem. Dari pengolahan data, sistem akan menghasilkan logic yang digunakan pada proses selanjutnya dalam sistem keseluruhan. Sistem merupakan penerapan dari Computer Vision dalam sistem kemanan gedung atau rumah. Sistem akan mengambil citra dari masukan berupa video. Setiap frame dari video tersebut akan diproses oleh sistem. Citra akan diproses dengan menggunakan metode Haar Cascade untuk mendeteksi objek berupa wajah yang terdapat pada citra. Lalu sistem akan menggunakan metode Fisherface sebagai metode untuk proses mencocokkan objek wajah yang terdeteksi dengan wajah yang terdapat pada database sistem. Kata kunci : Keamanan, Computer Vision, Haar Cascade, Fisherface
A Survey on Different Classification for Method Incomplete Pattern

The classification of incomplete patterns is an exceptionally difficult assignment in light of the fact that the protest (incomplete example) with various conceivable estimations of missing qualities may yield particular classification comes about. The instability (vagueness) of classification is for the most part brought about by the absence of data of the missing information. Another model based credal classification (PCC) strategy is proposed to manage incomplete patterns because of the conviction work structure utilized traditionally as a part of evidential thinking approach. The class models acquired via preparing tests are individually used to gauge the missing qualities. Regularly, in a c-class issue, one needs to manage c models, which yield c estimations of the missing qualities. The diverse altered patterns, in light of all possible conceivable estimation have been grouped by a standard classifier and we can get at most c unmistakable classification comes about for an incomplete example. Since all these unmistakable classification results are conceivably acceptable, we propose to join all of them together to acquire the last classification of the incomplete example. Another credal blend strategy is presented for taking care of the classification issue, and it can portray the inalienable instability because of the conceivable clashing results conveyed by various estimations of the missing qualities. The incomplete patterns that are exceptionally hard to group in a particular class will be sensibly and naturally dedicated to some legitimate meta-classes by PCC strategy with a specific end goal to decrease mistakes. The adequacy of PCC technique has been tried through four investigations with counterfeit and genuine information sets. In this paper, we talk about different incomplete example classification and evidential thinking procedures utilized as a part of the region of information mining.
Numerical study of convergence of the mass redistribution method for elastody- namic contact problems

Abstract. This note deals with two and three–dimensional elastodynamic contact problems. An approximated solution combining the finite element and mass redistribution methods is exhibited. The mass redistribution method consists in a redistribution of the body mass such that the inertia at the contact node vanishes. Some numerical experiments using two time–integration methods, the Crank–Nicolson as well as backward Euler methods, highlighted the convergence properties of the mass redistribution method.
Leveraging Incremental Localization Information for Adapting Feature-based Map Matching Methods

The current state of the art of SLAM methods are sufficient to enable a specific robot to navigate a specific, static environment. However, the performance strongly depends on an expert to chose the right parameters. This thesis focuses on feature-based map matching, which is often used as a part of a SLAM solution to detect loop closures, thereby reducing the accumulated error of the robot's pose. In practice, the parameter-dependence constrains the robot's autonomy severely, since it can only safely navigate environments of which the rough structure is known beforehand. It also complicates using a shared map in heterogeneous multi-robot teams, because each robot system requires its own set of parameters to reliably navigate its environment. Finally, tuning the many parameters manually is tedious and often quite hard, even with expert knowledge about the system. The high dimensionality of the parameter space and long evaluation durations make tackling this problem with simple approaches, like a grid search, infeasible. To remedy this problem, a framework for automated parameter tuning is proposed that uses Bayesian optimization. It includes a measure for map matcher performance based on both the quality and the quantity of the matches, which only requires ground truth transformations between submap pairs. Labels that tell whether a submap pair should be matchable are not required. The proposed method aims to leverage transformations estimated by a robot's incremental localization system as pseudo ground truth, to calculate the measure during the optimization process. Since most autonomous robots use an incremental localization system, those transformations are available anyway. The proposed solution is evaluated on a simulation dataset and a dataset recorded with a real robot system. The experiments show that the proposed solution is capable of providing map matcher parameters with at least similar performance than a reference parameter set that has been hand tuned for the evaluated datasets.
Wireless Network Localization Algorithm Based on Tikhonov Regularization for Anisotropic Networks

In anisotropic network, hop-counts between nodes may not match physical distances well. Hence, it may introduce huge errors to employ multi-hop range-free localization algorithm to estimate nodes location. In this paper, we present a novel multi-hop range-free localization algorithm for anisotropy network. Firstly, we build the relationship between hop-counts and distances among nodes under Tikhonov regularization metric. Since the relationship retains all the hop-counts characteristics to all anchors in all directions, then we can precisely capture the anisotropic relationship between hop-counts and physical distances. Finally, we use the multilateration technique to estimate the locations of all nodes. We evaluate our method based on multiple anisotropy factors, and analyze its performance. We also compare our method with state-of-art methods, and demonstrate the high efficiency of our proposed method. Experiments results show that proposed algorithm improves localization accuracy by more than 90%.
Synthetic Data for Face Recognition

Datasets for facial recognition are not easy to create for numerous reasons. Distance and outdoor datasets are even harder to create. This paper tries to shed light on these reasons and propose a solution of using synthetic data. We show both the creation, validation and use of this data. Why synthetic data is needed and essentially the only alternative to solving this problem is discussed.