Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Shape Registration and Active Stereo Shape Reconstruction using Modified Bundle Adjustment

Simultaneous registration and shape fusion using 3D scanners have been proposed for conducting wide-area and dense 3D shape reconstruction. However, because the 3D scanners for such a system must be robust and should provide feedback in real time, only a few devices are available, thereby limiting the application of the technique. In this study, we propose a new wide-area scanning algorithm that only requires an off-the-shelf projector and a camera. In our technique, the devices are not necessarily fixed to each other and the relative positions of the devices as well as the scene shapes can be precisely estimated by bundle adjustment (BA) in case of structured light. To efficiently perform shape registration, a robust and dense shape reconstruction is required, which is currently considered to be an open problem for structured light systems. In this study, we suggest a novel network-based feature detection algorithm as well as shape fusion algorithm for the solution.
Implementation of Selective Pressure Mechanism to Optimize Memory Consumption in the Synthesis of Neuromodels for Medical Diagnostics

The introduction of artificial neural networks in the process of medical diagnosis meets a number of difficulties. The using of general methods for synthesis and training networks is difficult because of the complexity of the simulated system, that is the human. Neuroevolution approach for the synthesis of neural network models has proven itself well, but it is also not without difficulties. The paper proposes a new mechanism for modifying the genetic algo-rithm in the synthesis of neuromodels that can be used in medical diagnostics. Innovations allow to reduce time of synthesis, and also to solve a number of problems at a choice of the best individuals for formation of new population.
Capabilities for Innovation in a Globalizing World: from Nearby or at a Distance?

This paper addresses the question of how clustering affects firms’ capabilities to innovate. This paper uses a specifically tailored survey carried out in 2013 of the geographical distance and scope of collaboration for innovation of 2002 firms located in the four largest Norwegian city-regions with the rest of the country as a separate category. We find that a majority of firms are located in regional clusters. However, the number of international relations increases the capacity to innovate. An international business environment stimulates the capability to innovate more than being part of a regional cluster. Firms that successfully develop global pipelines to knowledge sources in distant locations often sidestep the risk of lock-in, associated with relying solely on local sources of information (Malecki, 2010; Fitjar & RodriguezPose, 2011). Our findings suggest that caution should be exercised when invoking the concept of regional clusters to explain or define regional competitiveness. The cluster model obviously assigns too much importance to the regional business context, and to local interaction and externalities. These findings suggest that policies to advance the capabilities to innovate in Norwegian firms and regions may need to be revised.
Why cloud applications are not ready for the edge (yet)

Mobile Edge Clouds (MECs) are distributed platforms in which distant data-centers are complemented with computing and storage capacity located at the edge of the network. Their wide resource distribution enables MECs to fulfill the need of low latency and high bandwidth to offer an improved user experience. As modern cloud applications are increasingly architected as collections of small, independently deployable services, they can be flexibly deployed in various configurations that combines resources from both centralized datacenters and edge locations. In principle, such applications should therefore be well-placed to exploit the advantages of MECs so as to reduce service response times. In this paper, we quantify the benefits of deploying such cloud micro-service applications on MECs. Using two popular benchmarks, we show that, against conventional wisdom, end-to-end latency does not improve significantly even when most application services are deployed in the edge location. We developed a profiler to better understand this phenomenon, allowing us to develop recommendations for adapting applications to MECs. Further, by quantifying the gains of those recommendations, we show that the performance of an application can be made to reach the ideal scenario, in which the latency between an edge datacenter and a remote datacenter has no impact on the application performance. This work thus presents ways of adapting cloud-native applications to take advantage of MECs and provides guidance for developing MEC-native applications. We believe that both these elements are necessary to drive MEC adoption.
CHEMICAL ANALYSIS OF THE NINTH MAGNITUDE CARBON-ENHANCED METAL-POOR STAR BD+44°493

We present detailed chemical abundances for the bright carbon-enhanced metal-poor (CEMP) star BD+44°493, previously reported on by Ito et al. Our measurements confirm that BD+44°493 is an extremely metal-poor ([Fe/H] =−3.8) subgiant star with excesses of carbon and oxygen. No significant excesses are found for nitrogen and neutron-capture elements (the latter of which place it in the CEMP-no class of stars). Other elements that we measure exhibit abundance patterns that are typical for non-CEMP extremely metal-poor stars. No evidence for variations of radial velocity has been found for this star. These results strongly suggest that the carbon enhancement in BD+44°493 is unlikely to have been produced by a companion asymptotic giant-branch star and transferred to the presently observed star, nor by pollution of its natal molecular cloud by rapidly-rotating, massive, mega metal-poor ([Fe/H] < − 6.0) stars. A more likely possibility is that this star formed from gas polluted by the elements produced in a “faint” supernova, which underwent mixing and fallback, and only ejected small amounts of elements of metals beyond the lighter elements. The Li abundance of BD+44°493 (A(Li) = log (Li/H)+12 =1.0) is lower than the Spite plateau value, as found in other metal-poor subgiants. The upper limit on Be abundance (A(Be) = log (Be/H)+12 < − 1.8) is as low as those found for stars with similarly extremely-low metallicity, indicating that the progenitors of carbon- (and oxygen-) enhanced stars are not significant sources of Be, or that Be is depleted in metal-poor subgiants with effective temperatures of ∼5400 K.
Sieve: Lightweight Robust Regression on Private Sensory Data

Mobile crowd sensing (MCS) data have unique features, i.e., private, error-prone, non-stationary and opportunistically generated, and collected by resource-constrained mobile devices, which bring the regression task new challenges. First, it is of great difficulty to derive an accurate model without acquiring raw data. Second, adaptive model updating mechanism is urgently needed. Last, regression schemes should be lightweight. In this paper, we propose a blind regression scheme, called Sieve, in MCS settings. The core idea is first to let the server help in coordinating the selection of a small ‘clean’ subset of observations locally stored over all volunteers. Based on the ‘clean’ subset, an initial model can be established among volunteers in a distributed fashion. With observations constantly coming, instead of model re-estimating from the scratch, newly selected ‘clean’ observations are used to update the established model. Sieve preserves data privacy by only exchanging aggregated information. With the incremental model updating strategy, it also minimizes the communication and computation overhead of mobile devices. Extensive trace-driven simulations are conducted and the results demonstrate the efficacy of the Sieve design.
Measurements of the Impression of Three Pieces of Sounds Based on Visual Analog Scale by Small Samples

This paper discusses that measurements of the impression of three pieces of sounds using box-and-whisker plot, univariate scatter plot and cluster analysis based on Visual Analog Scale (VAS) by small samples. Eight healthy people are invited as the experiment participants, two pieces of music (minor music and major music) and natural sound (babbling of a stream) are employed as the test pieces. All participants are asked to listen to the test pieces individually. In order to analyze their impression of music or sound, they are asked to answer the questionnaire on VAS (7 questions; "Bright-Dark", "Happy-Sad", and so on) individually. The results show that subjective evaluation values based on the impression (ex. "happy", "sad", "bright", "dark", and so on) of test pieces have been obtained and visualized by a combination of box-and-whisker plot and univariate scatter plot. In addition, it has been discovered that the difference of the impression by the different sounds can be expressed. Moreover, it has also discovered that the questions based on the similar impression belong to the same cluster. And as the results of minor music, they are agreed with our previous study (more, the participants of this study are not the same as that participants) [8].
The Effect of Mental Health on Job Satisfaction of Principals (Case Study: Islamic Azad University of Tehran-East Branch)

The main goal of current research is the effect of mental health of principals in Islamic Azad University of Tehran-East Branch on their job satisfaction. In order to reach goals of the research, theoretical basics and the research background inside and out of the country, were studied and then a set of theories, concepts, background, definitions and symptoms related to lack of mental health and job satisfaction. Then, the researcher has studied the job satisfaction by means of a mental questionnaire and a general health questionnaire consisting 30 four-choice questions. The statistical population of current research consists of all principals in Islamic Azad University of Tehran-East Branch whom some of them were selected as statistical sample. Data collected from questionnaires was analyzed by means of descriptive statistics and also by using the inferential statistics. The results show that mental health of principals affects their job satisfaction. Also various components of mental health of principals are effective on their job satisfaction. Another side mental health and gender of principals have influence on their job satisfaction. Major of study and mental health of principals are effective on job satisfaction and education level and mental health of principals affect their job satisfaction are other result of this research as well.
Keyword-based information retrieval for the WoT

The Internet of Things (IoT) is expected to contain huge numbers of "things" producing vast amounts of information. To turn these raw data to useful services for the Web of Things (WoT), we have previously proposed KIOT, a keyword-based scheme for gathering and processing IoT information using Information-Centric Networking (ICN) techniques. In KIOT data items, such as sensor readings, can be named with arbitrary sets of keywords, while users can retrieve all data items matching a desired set of keywords and (optionally) process them with arbitrary functions. In this paper we focus on a prototype implementation of the data retrieval part of KIOT. To maximize flexibility in diverse settings, our implementation automatically configures the network and its routing tables, allowing arbitrary sets of keywords to be used for both data items and queries. Our implementation can be used on any IoT device supporting Java, and is also available for large scale testing over emulated networks using Mininet.
Res3ATN - Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos

Hand gesture recognition is a strenuous task to solve in videos. In this paper, we use a 3D residual attention network which is trained end to end for hand gesture recognition. Based on the stacked multiple attention blocks, we build a 3D network which generates different features at each attention block. Our 3D attention based residual network (Res3ATN) can be build and extended to very deep layers. Using this network, an extensive analysis is performed on other 3D networks based on three publicly available datasets. The Res3ATN network performance is compared to C3D, ResNet-10, and ResNext-101 networks. We also study and evaluate our baseline network with different number of attention blocks. The comparison shows that the 3D residual attention network with 3 attention blocks is robust in attention learning and is able to classify the gestures with a better accuracy, thus outperforming existing networks.
ABOUT THE POSSIBLE ROLE OF HYDROCARBON LAKES IN THE ORIGIN OF TITAN'S NOBLE GAS ATMOSPHERIC DEPLETION

An unexpected feature of Titan's atmosphere is the strong depletion in primordial noble gases revealed by the Gas Chromatograph Mass Spectrometer aboard the Huygens probe during its descent on 2005 January 14. Although several plausible explanations have already been formulated, no definitive response to this issue has yet been found. Here, we investigate the possible sequestration of these noble gases in the liquid contained in lakes and wet terrains on Titan and the consequences for their atmospheric abundances. Considering the atmosphere and the liquid existing on the soil as a whole system, we compute the abundance of each noble gas relative to nitrogen. To do so, we make the assumption of thermodynamic equilibrium between the liquid and the atmosphere, the abundances of the different constituents being determined via regular solution theory. We find that xenon's atmospheric depletion can be explained by its dissolution at ambient temperature in the liquid presumably present on Titan's soil. In the cases of argon and krypton, we find that the fractions incorporated in the liquid are negligible, implying that an alternative mechanism must be invoked to explain their atmospheric depletion.
A 10-MHz Current-Mode Fixed-Frequency Hysteretic Controlled DC-DC Converter With Fast Transient Response

This paper proposes a fixed-frequency current-mode hysteretic controlled DC-DC buck converter operating at 10 MHz. The proposed converter senses the inductor current through a current-sensing filter and compares the sense-node voltage with only one threshold of the hysteretic comparator. The reference clock synchronizes the switching frequency of the converter by forcedly altering the present state of the hysteretic comparator. The proposed converter is targeted to switch at a frequency of 10 MHz with the output filter components of L=200 nH and C=4.7 µF. The simulation results show that the maximum overshoot and the settling time are below 12 mV and 1.9 µs respectively while the load changes from 50 mA to 450 mA within 1 ns time. The output voltage load regulation is 2.5 mV/A.
A BCI-based Assessment of a Player's State of Mind for Game Adaptation

Playing videogames is a process driven by both cognitive and emotional factors. Then, developing a mechanism that takes into account players’ emotional state for adapting specific game features is a way to increase their engagement and flow during gameplay. In this paper, we present how a passive Brain Computer Interface (BCI) can be used to assess the state of mind of a player that can be used for enhancing his experience through adaptation. In particular, we collected data from EEG signals, in a horror adventure game, to learn a model of flow by monitoring the level of boredom, flow, and stress of the player. To this aim, we set an experiment and collected both subjective data about the perceived emotions and state of flow and data from the BCI that have been used to learn a classifier to recognize and assess the player’s affective state. Results are encouraging and the learned model achieves a good accuracy in distinguishing the three player’s states.
The impact of the Non-coverage of Residential mobility. A comparison of the German Microcensus panel with the SOEP.

The German Microcensus can be seen as a rotating panel, where the units stay for four observations in the survey. Because of the very high case numbers and the mandatory participation it appears as a valuable data base for short duration analysis. However, the Microcensus is by design a sample of dwellings. Consequently there is no information on participants after they moved from the dwelling. We investigate how the use of the German SocioEconomic Panel (SOEP) can help to measure the non-coverage bias of the Microcensus. The analysis is demonstrated for transitions between the labor force states: employed, unemployed and inactive. This methodological work is a necessary prerequisite for the use of the Microcensus as a longitudinal data base. Besides, our analysis can be seen as a first longitudinal evaluation of the SOEP against the Microcensus.
Link Budget Validation for Backscatter-Radio System in Sub-1GHz

Ambient backscatter communications system (ABCS) has recently been introduced as a cutting edge technology in which devices communicate in wireless mode by exploiting ambient radio frequency (RF) signals instead of actively generating them. ABCS is a promising technology for Internet of Things (IoT) use cases in which the power efficiency is a major challenge yet to be addressed. ABCS is in its early development stages from theoretical and practical perspectives. In this regard, it is highly important to understand the multi-aspects of the link budget of ABCS. Hence, in this paper we conducted a comprehensive study including measurements in different propagation environment and thorough simulation. The measurements are preformed in sub-1 GHz band and particularly in 590 MHz with the tags designed in the house. The results confirm the match between measurements and the simulations with trivial error.
Traffic-aware Advanced Sleep Modes management in 5G networks

Advanced Sleep Modes (ASMs) are defined as a progressive shutdown of the Base Station (BS) depending on the activation and the deactivation times of the different components. This transition duration defines different levels of sleep modes that can be implemented in future 5G networks. We propose in this paper a management strategy based on Q-learning approach which will enable to find the best combination and durations of ASM levels depending on the traffic load and the network operator's policy regarding energy reduction versus latency. Our results show that even in delay-sensitive scenarios, high energy gains can be achieved in low and moderate traffic loads, respectively 55% and 10%, without inducing an extra latency. Starting from a certain traffic load (approximately 30%), ASM should not be implemented in case of stringent latency constraint.
Fast Non-Convex Hull Computation

3D surface reconstruction usually begins with a point cloud and aims to build a representation of the object producing that point cloud. There are several algorithms to solve this problem, each with different priors over the point cloud, such as the type of object represented, or the method by which it was obtained. In this work, we focus on an algorithm called Non-Convex Hull (NCH), which reconstructs surfaces through a concept similar to the Medial Axis Transform. A new algorithm called Shrinking Planes is proposed to compute the NCH, based on the Shrinking Ball method with a few improvements. We prove that the new method can approximate surfaces to arbitrarily small error, and evaluate its performance on the surface reconstruction task. The new method maintains the same reconstruction quality as the Naïve Non-Convex Hull method, while achieving a large performance improvement.
Stochastic Dynamics of Baroclinic Waves

Abstract The maintenance of variance and attendant heat flux in linear, forced, dissipative baroclinic shear flows subject to stochastic excitation is examined. The baroclinic problem is intrinsically nonnormal and its stochastic dynamics is found to differ significantly from the more familiar stochastic dynamics of normal systems. When the shear is sufficiently great in comparison to dissipative effects, stochastic excitation supports highly enhanced variance levels in these nonnormal systems compared to variance levels supported by the same forcing and dissipation in related normal systems. The eddy variance and associated heat flux are found to arise in response to transient amplification of a subset of forcing functions that obtain energy from the mean flow and project this energy on a distinct subset of response functions (E0Fs) that are in turn distinct from the set of normal modes of the system. A method for obtaining the dominant forcing and response functions as well as the distribution of heat f.
A Lossy Compression Method on Positional Index for Efficient and Effective Retrieval

In query processing, incorporating proximity between query terms is beneficial for effective retrieval. However, it brings inevitable storage and computing costs by using positional data in inverted indexes. In this paper, we propose a lossy method for compressing term position data in the case of utilizing term proximity. Our method exploits clustering property of term occurrences, adaptively clusters the nearby occurrences, and replaces the clustered positions with a centralized value. Experimental results show that our adaptive method is competitive with respect to index size, ranking efficiency and effectiveness.
Towards a Self-Organizing Deciphering System based on a Wave Digital Emulator

Current cryptographic systems are based on functions that are easy to compute, yet finding the inverse operation is computationally complex. One of these functions is the prime factorization which is utilized in modern communication systems. Memristive circuits are challenging these standards because they enable self-organizing logic gates (SOLGs) which can be operated in reverse mode, where every node can electively be used as an input or output. So far, only investigations with idealized memristor models have been done. That is because manufacturing costs of real memristors are high. Thus, our goal is to create a highly flexible emulator for an universal SOLG to aid future investigations. We therefore exploit the wave digital algorithm to obtain an emulator which preserves stability, allows for exchangeable memristor models, and enables parameter optimization and sensitivity analyses during runtime for future research.
The Optimization Study of the Front Suspension Structure Parameters for the Mine Truck Based on Adams/car

The suspension performance is heavily determined by the suspension structure parameters. This paper researches the geometric relations between the structure parameters of double wishbone front suspension and alignment parameters of steering wheel, then establishes a double wishbone suspension steering system dynamics model using ADAMS/car, finally simulates and optimizes the main hard points of the front suspension with the wheel alignment parameters as optimization objective. The optimized steering wheel alignment parameters basically meet the design requirements, and the calculated results have been applied to the real truck, and achieved good results.
Energy-efficient Resource Allocation and Channel Assignment for NOMA-based Mobile Edge Computing

In this paper, we study resource allocation (including power and computation resources) and channel assignment in an uplink Non-orthogonal Multiple Access (NOMA)-based Mobile Edge Computing (MEC) system. Our objective is to minimize the total energy consumption of all users. The problem, however, is a non-convex combinatorial optimization problem. We first investigate the hidden convexity by reformulating the resource allocation problem when the channel assignment is given, and propose an efficient algorithm to allocate the resources by dual decomposition methods. Furthermore, we design a heuristic algorithm to decide the channel assignment leveraging the structural property in the reformulation. Extensive simulations verify that NOMA has great advantages over Orthogonal Multiple Access (OMA) in multi-user latency-intensive MEC systems.