snu-aidas/HVMap-dataset huggingface dataset:
FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization

Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Pointset registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter [1] for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs), which makes it highly efficient even for high DOF systems. The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks. The video demo and source code are available on our project page.
Daily Mindful Responding Mediates the Effect of Meditation Practice on Stress and Mood: The Role of Practice Duration and Adherence.

OBJECTIVE
Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being.


METHOD
Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants.


RESULTS
We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes.


CONCLUSION
These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.
Health Effects Related to Wind Turbine Noise Exposure: A Systematic Review

BACKGROUND
Wind turbine noise exposure and suspected health-related effects thereof have attracted substantial attention. Various symptoms such as sleep-related problems, headache, tinnitus and vertigo have been described by subjects suspected of having been exposed to wind turbine noise.


OBJECTIVE
This review was conducted systematically with the purpose of identifying any reported associations between wind turbine noise exposure and suspected health-related effects.


DATA SOURCES
A search of the scientific literature concerning the health-related effects of wind turbine noise was conducted on PubMed, Web of Science, Google Scholar and various other Internet sources.


STUDY ELIGIBILITY CRITERIA
All studies investigating suspected health-related outcomes associated with wind turbine noise exposure were included.


RESULTS
Wind turbines emit noise, including low-frequency noise, which decreases incrementally with increases in distance from the wind turbines. Likewise, evidence of a dose-response relationship between wind turbine noise linked to noise annoyance, sleep disturbance and possibly even psychological distress was present in the literature. Currently, there is no further existing statistically-significant evidence indicating any association between wind turbine noise exposure and tinnitus, hearing loss, vertigo or headache.


LIMITATIONS
Selection bias and information bias of differing magnitudes were found to be present in all current studies investigating wind turbine noise exposure and adverse health effects. Only articles published in English, German or Scandinavian languages were reviewed.


CONCLUSIONS
Exposure to wind turbines does seem to increase the risk of annoyance and self-reported sleep disturbance in a dose-response relationship. There appears, though, to be a tolerable level of around LAeq of 35 dB. Of the many other claimed health effects of wind turbine noise exposure reported in the literature, however, no conclusive evidence could be found. Future studies should focus on investigations aimed at objectively demonstrating whether or not measureable health-related outcomes can be proven to fluctuate depending on exposure to wind turbines.
A Survey of the Functional Splits Proposed for 5 G Mobile Crosshaul Networks Line

Pacing the way towards 5G has lead researchers and industry in the direction of centralized processing known from Cloud-Radio Access Networks (C-RAN). In C-RAN research, a variety of different functional splits is presented by different names and focusing on different directions. The functional split determines how many Base Station (BS) functions to leave locally, close to the user, with the benefit of relaxing fronthaul network bitrate and delay requirements, and how many functions to centralize with the possibility of achieving greater processing benefits. This work presents for the first time a comprehensive overview systematizing the different work directions for both research and industry, while providing a detailed description of each functional split option and an assessment of the advantages and disadvantages. This work gives an overview of where the most effort has been directed in terms of functional splits, and where there is room for further studies. The standardization currently taking place is also considered and mapped into the research directions. It is investigated how the fronthaul network will be affected by the choice of functional split, both in terms of bitrates and latency, and as the different functional splits provide different advantages and disadvantages, the option of flexible functional splits is also looked into. Keywords—Functional Split; Crosshaul; X-haul; C-RAN; Fronthaul; standardization; industry; network architecture; Please refer to the list of acronyms provided in the end of the paper, right before the references.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1
A framework for the design and integration of collaborative classroom games

The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components. 2011 Elsevier Ltd. All rights reserved.
Model-based control of a 6-dof electrohydraulic Stewart – Gough platform

In this paper, a novel model-based controller for a six Degree-of-Freedom (dof) electrohydraulic Stewart–Gough platform is developed. Dynamic models of low complexity are employed that describe the salient dynamics of the main electrohydraulic components. Rigid body equations of motion and hydraulics dynamics, including friction and servovalve models are used. The developed feedback controller uses the system dynamic and hydraulic model to yield servovalve currents, so that the error dynamics converge asymptotically to zero, independent of load variations. In this approach, force, pressure or acceleration feedback is not required. Simulations with typical desired trajectory inputs are presented and a good performance of the controller is obtained. The proposed methodology can be extended to electrohydraulic serial or closed-chain manipulators and simulators. 2007 Elsevier Ltd. All rights reserved.
Assessment of chronic pain coping strategies

Introduction. We made an adaptation of the Coping Strategies Questionnaire (CSQ) to the Spanish population. This measure, the most used in its scope, was developed by Rosenstiel and Keefe in 1983. Method. 205 participants coming from Primary Health Care and pain clinics made up the sample. More than half suffered migraine and chronic tension-type headache; the rest, fibromyalgia, low back pain, arthrosis or arthritis. Results. Factor analyses explained 59% of the total variance, on an 8-factor structure that converged into a 2-factor structure. In the 8-factor solution the novelty was the diversification of mental-non-mental distraction strategies, and religious-non-religious hope strategies. In the 2-factor solution the novelty was the grouping according to the efficacy of the coping. All the CSQ factors showed inner consistency and construct validity. Thus, unadaptive coping strategies were related to negative, anxious and depressed self-talk, related to lack of control and perceived self-efficacy, and related to many pain behaviors. On the contrary it happened with adaptive coping strategies. In addition, the diagnosis of pain was related to the utilization and effectiveness of coping strategies. Conclussions. CSQ is shown to be a reliable and valid measure of coping strategies in chronic pain in the Spanish population, showing the difference between theoretical and empirical factor structures again.
Challenges during the transition to Agile Methodologies : A Holistic Overview
The Human Brain in Numbers: A Linearly Scaled-up Primate Brain

The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10x more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.
The Great Grevy ’ s Rally : The Need , Methods , Findings , Implications and Next Steps
Reinforcement Learning for Humanoid Robotics

Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. ‘Vanilla’ policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.
Gravitational Search Algorithm and Recent Trends

A R T I C L E I N F O A B S T R A C T Article history: Received 20 March 2017 Received in revised form 22 March 2017 Accepted 28 March 2017 Available online 31March 2017 The inability of conventional optimization algorithms to handle exponential increase in the size of the search space has paved way towards capturing the behavior of natural organisms for solving optimization problems. Nature Inspired Algorithms have mostly been associated with the swarm behavior of organisms. A rather new idea in this respect was proposed by Rashedi et al in 2009 by the name ‘Gravitational Search Algorithm’. The basis of their proposal, as clear from the name, is the Newtonian gravity and interactions between populations of masses. The algorithm has been widely used since then in many applications of science. Since its proposal, researches have been continuously headed in extending GSA for different scenarios and applications. The working of GSA and some such popular and recent proposals in the direction of extending GSA are discussed in this paper.
Diaphragmatic Eventration in Sisters with Asparagine Synthetase Deficiency: A Novel Homozygous ASNS Mutation and Expanded Phenotype.

BACKGROUND
Asparagine Synthetase Deficiency (ASNSD; OMIM #615574) is a newly described rare autosomal recessive neurometabolic disorder, characterised by congenital microcephaly, severe psychomotor delay, encephalopathy and progressive cerebral atrophy. To date, seven families and seven missense mutations in the ASNSD disease causing gene, ASNS, have been published.


METHODS
We report two further affected infant sisters from a consanguineous Indian family, who in addition to the previously described features had diaphragmatic eventration. Both girls died within the first 6 months of life. Whole exome sequencing (WES) was performed for both sisters to identify the pathogenic mutation. The clinical and biochemical parameters of our patient are compared to previous reports.


RESULTS
WES demonstrated a homozygous novel missense ASNS mutation, c.1019G > A, resulting in substitution of the highly conserved arginine residue by histidine (R340H).


CONCLUSION
This report expands the phenotypic and mutation spectrum of ASNSD, which should be considered in neonates with congenital microcephaly, seizures and profound neurodevelopmental delay. The presence of diaphragmatic eventration suggests extracranial involvement of the central nervous system in a disorder that was previously thought to exclusively affect the brain. Like all previously reported patients, these cases were diagnosed with WES, highlighting the clinical utility of next generation sequencing in the diagnosis of rare, difficult to recognise disorders.
Introducing the Occupational Balance Questionnaire (OBQ).

OBJECTIVE
The concept of occupational balance is frequently used in occupational therapy but the fact that it has been defined and measured differently is a limitation. This article introduces the Occupational Balance Questionnaire (OBQ), which focuses on satisfaction with the amount and variation of occupations. It consists of 13 items measured on six-step ordinal scales. It has shown good content validity in a sample of 21 occupational therapists but other psychometric properties have not been investigated. The aim was to investigate the OBQ regarding internal consistency, test-retest reliability, and floor/ceiling effects.


METHODS
The OBQ was administered twice to a sample selected through convenience sampling. Internal consistency was investigated by Cronbach's alpha and test-retest reliability analysed with Spearman's Rho correlation for the total score and weighted kappa on each item. Potential floor/ceiling effects were explored by checking for the percentage of participants who scored lowest and highest.


RESULTS
The results demonstrated that the OBQ has good internal consistency (Cronbach's alpha 0.936) and sufficient test-retest reliability (Spearman's Rho for the total score was 0.926) and, thus, seems stable over time. No floor or ceiling effect was detected.


CONCLUSIONS
The OBQ therefore showed promising reliability, although further instrument development studies to examine its construct validity are required.
Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems

Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach. PVLDB Reference Format: Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanan Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao. Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. PVLDB, 11 (10): xxxx-yyyy, 2018. DOI: https://doi.org/10.14778/3231751.3231765
Does Life Satisfaction influence the intention (We-Intention) to use Facebook?

Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers’ behavior. 2015 Elsevier Ltd. All rights reserved.
Sampling Generative Networks: Notes on a Few Effective Techniques

We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model’s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks. Keywords—Generative; VAE; GAN; Sampling; Manifold