Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast depth densification for occlusion-aware augmented reality

Current AR systems only track sparse geometric features but do not compute depth for all pixels. For this reason, most AR effects are pure overlays that can never be occluded by real objects. We present a novel algorithm that propagates sparse depth to every pixel in near realtime. The produced depth maps are spatio-temporally smooth but exhibit sharp discontinuities at depth edges. This enables AR effects that can fully interact with and be occluded by the real scene. Our algorithm uses a video and a sparse SLAM reconstruction as input. It starts by estimating soft depth edges from the gradient of optical flow fields. Because optical flow is unreliable near occlusions we compute forward and backward flow fields and fuse the resulting depth edges using a novel reliability measure. We then localize the depth edges by thinning and aligning them with image edges. Finally, we optimize the propagated depth smoothly but encourage discontinuities at the recovered depth edges. We present results for numerous real-world examples and demonstrate the effectiveness for several occlusion-aware AR video effects. To quantitatively evaluate our algorithm we characterize the properties that make depth maps desirable for AR applications, and present novel evaluation metrics that capture how well these are satisfied. Our results compare favorably to a set of competitive baseline algorithms in this context.
Simulation to Real Transfer Learning with Robustified Policies for Robot Tasks

Learning tasks from simulated data using reinforcement learning has been proven effective. A major advantage of using simulation data for training is that it reduces the burden of acquiring real data. Specifically when robots are involved, it is important to limit the amount of time a robot is occupied with learning, and can instead be used for its intended (manufacturing) task. A policy learned on simulation data can be transferred and refined for real data. In this paper we propose to learn a robustified policy during reinforcement learning using simulation data. A robustified policy is learned by exploiting the ability to change the simulation parameters (appearance and dynamics) for successive training episodes. We demonstrate that the amount of transfer learning for a robustified policy is reduced for transfer from a simulated to real task. We focus on tasks which involve reasl-time non-linear dynamics, since non-linear dynamics can only be approximately modeled in physics engines, and the need for robustness in learned policies becomes more evident.
Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.
Integration of spatial information in convolutional neural networks for automatic segmentation of intraoperative transrectal ultrasound images

Abstract. Image guidance systems that register scans of the prostate obtained using transrectal ultrasound (TRUS) and magnetic resonance imaging are becoming increasingly popular as a means of enabling tumor-targeted prostate cancer biopsy and treatment. However, intraoperative segmentation of TRUS images to define the three-dimensional (3-D) geometry of the prostate remains a necessary task in existing guidance systems, which often require significant manual interaction and are subject to interoperator variability. Therefore, automating this step would lead to more acceptable clinical workflows and greater standardization between different operators and hospitals. In this work, a convolutional neural network (CNN) for automatically segmenting the prostate in two-dimensional (2-D) TRUS slices of a 3-D TRUS volume was developed and tested. The network was designed to be able to incorporate 3-D spatial information by taking one or more TRUS slices neighboring each slice to be segmented as input, in addition to these slices. The accuracy of the CNN was evaluated on data from a cohort of 109 patients who had undergone TRUS-guided targeted biopsy, (a total of 4034 2-D slices). The segmentation accuracy was measured by calculating 2-D and 3-D Dice similarity coefficients, on the 2-D images and corresponding 3-D volumes, respectively, as well as the 2-D boundary distances, using a 10-fold patient-level cross-validation experiment. However, incorporating neighboring slices did not improve the segmentation performance in five out of six experiment results, which include varying the number of neighboring slices from 1 to 3 at either side. The up-sampling shortcuts reduced the overall training time of the network, 161 min compared with 253 min without the architectural addition.
Assessment of diversity in the genus Ficus L.(Moraceae) of Katerniaghat Wildlife Sanctuary, Uttar Pradesh, India

The paper deals with the taxonomy and diversity within the genus Ficus occurring in Katerniaghat Wildlife Sanctuary based on extensive field and herbarium studies. The genus comprises 11 species in the study area contrary to the earlier report of 8 species. Out of which, one species each belongs to herbs (F. assamica) and straggling shrubs (F. hetero- phylla) and the remaining species to shrub to big free standing trees (F. benghalensis, F. hispida, F. palmata subsp. virgata, F. racemosa, F. religiosa, F. retusa var. nitida, F. rumphii, F. semicordata and F. squamosa). F. microcarpa and F. carica have been excluded as they do not occur in the sanctuary but reported in the previous work due to mis- identification. F. repens has been segregated from F. heterophylla and reinstated as a distinct species with a correct name F. assamica. F. retusa var. nitida forms a new record for the sanctuary. In overall gross morphology, F. hispida, F. palmata subsp. virgata, F. assamica and F. heterophylla exhibit maximum diversity especially in their leaves, pe- duncles and figs. Among all species F. hispida has been found more common especially in low land and moist areas in mixed forest whereas F. retusa var. nitida has been seen with only two plants. All species have been critically examined and provided with correct nomenclature, vernacular name, description, phenology, ecology, uses and reference to voucher specimens. A dichotomous bracketed key to the species along with line drawings has also been included for identification.
Banking Systems, Central Banks and International Reserve Accumulation in East Asian Economies

Abstract This paper examines changes in the balance sheets of the banking system in five East Asian economies which were affected by the 1997 Asian Crisis. These countries have persistently accumulated international reserves since the crisis. This paper estimates the impact of reserve accumulation on some important balance sheet variables such as liquid assets, credit and deposits of the banking system by applying panel data techniques. Estimates using data from Thailand, South Korea, Malaysia, Philippines and Indonesia show that there has been robust positive impact of reserve accumulation on the liquid assets and deposits of the banking system after controlling for the effect of other potential variables.
Perceived Security And Consumer Trust In Adoption Of Fintech Service

Financial Technology (FinTech) is the new technology and innovation in the delivery of financial services. The emergence of FinTech provides different benefits financial consumers and financial companies. For a consumer, FinTech carry weight in a way of global and online transaction, speedier transaction processing, among others. However, studies has shown that consumer trust is still low. Hence, this paper aims to investigate the impact of five variables, namely confidentiality, authentication, authorization, non-repudiation and privacy on consumer trust in the adoption of FinTech in online banking service. The research was an empirical investigation based on surveys completed by 200 students from higher educations in Muadzam Shah, Pahang. Using regression analysis, the paper found that confidentiality, authentication and privacy were found to be positively and significantly related to consumer trust. On the other hand, authorization was found to be negatively and significantly related to the consumer trust. Non-repudiation was found to have positive and insignificant relation to consumer trust. Hence, from this study its shows the importance of security mechanisms in building users perception of security. The study provides a deeper understanding into the impact of perceived security on consumer trust in the adoption of Fintech in online banking service. © 2018 Published by Future Academy www.FutureAcademy. org.UK
MEAN-FIELD ANALYSIS FOR MODEL-BASED SPIKING NETWORKS

The human brain is composed of millions of neurons, firing spikes according to their membrane potentials. The difficulty in studying the brain exists partly because of the randomness property of neurons firing in a network. To understand more about the dynamics of a neuron’s firing rate, we choose to study a specific set of nonlinear dynamical equations that represent a neural network based on a spiking point of view with adaptation qualities. The dynamic membrane potential of a single neuron is a challenge to study since we can hardly know the number of spikes fired at a certain time. In this thesis, we use phase-plane analysis and more precisely mean-field analysis to address the random nature of the dynamic of model-based spiking networks. We find that the dynamics of neurons in a network offer exploitable and relevant information such as patterns of stable or unstable oscillations in certain circumstances.
Does Inflation Harm Corporate Investment? Empirical Evidence from OECD Countries

Abstract Theoretical models point at various channels of the impact of inflation on corporate investment. This article attempts to answer the question what the direction and strength of this possible impact is, examining the relationship between corporate investment and inflation on the sample of 21 OECD countries in the years 1960–2005. The obtained negative relationship, statistically and economically significant, proves robust to changes in the specification of the estimated equation, estimators, frequency of variables used in the study and analysed period. Moreover, the obtained results suggest a nonlinear character of this relationship: the marginal effect on corporate investment is higher at inflation rates between 3 and 5.5 per cent. These results suggest that the impact of inflation on corporate investment dynamics may be the source of the nonlinear nature of the relationship between GDP growth and inflation identified in previous empirical studies. Finally, taking into account the direct impact of inflation on investment, variables approximating the cost of capital utilisation prove to be statistically insignificant determinants of corporate investment.
Characteristics of Gas Exchange in Three Domesticated Anemone Species

Seeds of three Anemone species were collected from the suburban areas of Kunming and planted in a nursery for three and a half years at Kunming, Yunnan Province, China. Leaf gas exchange measurement indicated that these species had similar one-peak diurnal trends of net photosynthetic rate (PN), although A. rivularis had lower transpiration rate (TR), stomatal conductance (gs) and intercellular CO2 concentration (Ci), and higher stomatal limit in the afternoon. Species differences in response of PN to photosynthetically active radiation (PAR) were observed, especially under strong light. A. rivularis had the highest PN and Ci under strong light which corresponded with its highest gs and TR. A. rivularis had the highest light saturation point (LSP) (1000 mol m-2 s-1) and light compensation point (LCP) (69 mol m-2 s-1), while A. hupehensis var. japonica had the lowest LSP (800 ? mol m-2 s-1) and a lower LCP (53 mol m-2 s-1). But the three species responded similarly to the change of CO2 concentration in the air from 0 to 350 ? mol (CO2) mol-1, and their observed CO2 compensation point showed little difference (47, 53 and 56 ? mol (CO2) mol-1). Moreover, A. rivularis had the highest apparent quantum yield (0.032), carboxylation efficiency (0.049), PN (11.68 ? mol (CO2) m-2 s-1) and TR (5.36 mmol (H2O) m-2 s-1) based on the PN -PAR response. The results implied that A. rivularis is able to grow well under higher radiation, while A. hupehensis var. japonica is the best one to grow under partial shade.
How Useful are Your Comments ? Analyz i ng and P redi ct i ng YouTube Comment s and Comment Ratings

An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.
The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.
ADARES: Adaptive Resource Management for Virtual Machines

Virtual execution environments allow for consolidation of multiple applications onto the same physical server, thereby enabling more efficient use of server resources. However, users often statically configure the resources of virtual machines through guesswork, resulting in either insufficient resource allocations that hinder VM performance, or excessive allocations that waste precious data center resources. In this paper, we first characterize real-world resource allocation and utilization of VMs through the analysis of an extensive dataset, consisting of more than 250k VMs from over 3.6k private enterprise clusters. Our large-scale analysis confirms that VMs are often misconfigured, either overprovisioned or underprovisioned, and that this problem is pervasive across a wide range of private clusters. We then propose ADARES, an adaptive system that dynamically adjusts VM resources using machine learning techniques. In particular, ADARES leverages the contextual bandits framework to effectively manage the adaptations. Our system exploits easily collectible data, at the cluster, node, and VM levels, to make more sensible allocation decisions, and uses transfer learning to safely explore the configurations space and speed up training. Our empirical evaluation shows that ADARES can significantly improve system utilization without sacrificing performance. For instance, when compared to threshold and prediction-based baselines, it achieves more predictable VM-level performance and also reduces the amount of virtual CPUs and memory provisioned by up to 35% and 60% respectively for synthetic workloads on real clusters.
High-fidelity organic preservation of bone marrow in ca. 10 Ma amphibians

Bone marrow in ca. 10 Ma frogs and salamanders from the Miocene of Libros, Spain, represents the first fossilized example of this extremely decay-prone tissue. The bone marrow, preserved in three dimensions as an organic residue, retains the original texture and red and yellow color of hematopoietic and fatty marrow, respectively; moldic osteoclasts and vascular structures are also present. We attribute exceptional preservation of the fossilized bone marrow to cryptic preservation: the bones of the amphibians formed protective microenvironments, and inhibited microbial infiltration. Specimens in which bone marrow is preserved vary in their completeness and articulation and in the extent to which the body outline is preserved as a thin film of organically preserved bacteria. Cryptic preservation of these labile tissues is thus to a large extent independent of, and cannot be predicted by, the taphonomic history of the remainder of the specimen.
Stitched Trajectories for Off-Policy Learning

We study the problem of off-policy evaluation in RL settings. Importance sampling methods provide unbiased estimators for the value of a policy, without the need to learn an explicit model of the environment, but suffer from high variance, in part due to small effective sample sizes when evaluating deterministic policies. We introduce a method of stitching together sequences from different trajectories to increase the effective sample size for importance sampling estimators, thus reducing their variance while retaining their unbiasedness. We demonstrate that our method reduces the policy value estimation error on several synthetic toy examples.
Achievement in Terms of the Process of Integrating Technology into Instruction : A Study on the McA

The aim of this study was to determine the effect of the use of smart boards (SBs) on academic achievement in accordance with a multi-complementary approach (McA). The study was conducted in three stages: the first stage included the examination of meta-analytic and thematic reviews based on document review to obtain precomplementary data and identify gaps with this data; the second one considered the process of obtaining post-complementary data with a view to eliminate the identified gaps; the third one implied the integration of the results in two stages and the presentation of recommendations to obtain complementary information. With these aims, meta-analytic, thematic and experimental analyses were carried out. The three-stage process including both qualitative and quantitative results contributed to the validity and reliability of the current study.
Regularizing Reinforcement Learning with State Abstraction

State abstraction in a discrete reinforcement learning setting clusters states sharing a similar optimal action to yield an easier to solve decision process. In this paper, we generalize the concept of state abstraction to continuous action reinforcement learning by defining an abstract state as a state cluster over which a near-optimal policy of simple shape exists. We propose a hierarchical reinforcement learning algorithm that is able to simultaneously find the state space clustering and the optimal sub-policies in each cluster. The main advantage of the proposed framework is to provide a straightforward way of regularizing reinforcement learning by controlling the behavioral complexity of the learned policy. We apply our algorithm on several benchmark tasks and a robot tactile manipulation task and show that we can match state-of-the-art deep reinforcement learning performance by combining a small number of linear policies.
Idle state stability, limit cycle walking & regenerative walking: Towards long time autonomy in bipeds

This work presents an integral approach to tackle energy issues in bipedal robots. It introduces three combined ideas to increase these robots’ autonomy: First, the exploitation of the inherent equilibrium that should exist in the rest position of a welldesigned mechanism; then, the efficient usage of the energy to walk based on the natural limit cycle of the system; and finally, the harvest of energy based on the new idea of regenerative walking. Simulations and experimental tests show promising results of this approach, built under a delicate equilibrium between appropriate control scheme, suitable mechanical design and proper actuators choice.
Enhancing and Measuring the Performance in Software Defined Networking

Software Defined Networking (SDN) is a challenging chapter in today’s networking era. It is a network design approach that engages the framework to be controlled or 'altered' adroitly and halfway using programming applications. SDN is a serious advancement that assures to provide a better strategy than displaying the Quality of Service (QoS) approach in the present correspondence frameworks. SDN etymologically changes the lead and convenience of system instruments using the single high state program. It separates the system control and sending functions, empowering the network control to end up specifically. It provides more functionality and more flexibility than the traditional networks. A network administrator can easily shape the traffic without touching any individual switches and services which are needed in a network. The main technology for implementing SDN is a separation of data plane and control plane, network virtualization through programmability. The total amount of time in which user can respond is called response time. Throughput is known as how fast a network can send data. In this paper, we have design a network through which we have measured the Response Time and Throughput comparing with the Real-time Online Interactive Applications (ROIA), Multiple Packet Scheduler, and NOX.