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Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
(2018)
Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.
The extraction kinetics of polyphenols, which are leached from red vine leaves, are studied and evaluated using a laboratory robot and nonconventional processing techniques such as ultrasonic (US)-, microwave (MW)-, and pulsed electric field (PEF)-assisted extraction processes. The robotic high-throughput screening reveals optimal extraction conditions at a pH value of 2.5, a temperature of 56 °C, and a solvent mixture of methanol:water:HCl of 50:49:1 v/v/v. Nonconventional processing techniques, such as MW- and US-assisted extraction, have the fastest kinetics and produce the highest polyphenol yield. The non-conventional techniques yield is 2.29 g/L (MW) resp. 2.47 g/L (US) for particles that range in size from 450 to 2000 µm and 2.20 g/L (MW) resp. 2.05 g/L (US) for particles that range from 2000 to 4000 µm. PEF has the lowest yield of polyphenols with 0.94 g/L (450–2000 µm), resp. 0.64 g/L (2000–4000 µm) in comparison to 1.82 g/L (2000 to 4000 µm) in a standard stirred vessel (50 °C). When undried red vine leaves (2000 to 4000 µm) are used the total phenol content is 1.44 g/L with PEF.
We report on generation of pulsed broadband terahertz radiation utilizing the inverse spin hall effect in Fe/Pt bilayers on MgO and sapphire substrates. The emitter was optimized with respect to layer thickness, growth parameters, substrates and geometrical arrangement. The experimentally determined optimum layer thicknesses were in qualitative agreement with simulations of the spin current induced in the ferromagnetic layer. Our model takes into account generation of spin polarization, spin diffusion and accumulation in Fe and Pt and electrical as well as optical properties of the bilayer samples. Using the device in a counterintuitive orientation a Si lens was attached to increase the collection efficiency of the emitter. The optimized emitter provided a bandwidth of up to 8 THz which was mainly limited by the low-temperature-grown GaAs (LT-GaAS) photoconductive antenna used as detector and the pulse length of the pump laser. The THz pulse length was as short as 220 fs for a sub 100 fs pulse length of the 800 nm pump laser. Average pump powers as low as 25 mW (at a repetition rate of 75 MHz) have been used for terahertz generation. This and the general performance make the spintronic terahertz emitter compatible with established emitters based on optical rectification in nonlinear crystals.
In this paper, we demonstrate the power of functional data models for a statistical analysis of stimulus-response experiments which is a quite natural way to look at this kind of data and which makes use of the full information available. In particular, we focus on the detection of a change in the mean of the response in a series of stimulus-response curves where we also take into account dependence in time.
Initiated by a task in tunable microoptics, but not limited to this application, a microfluidic droplet array in an upright standing module with 3 × 3 subcells and droplet actuation via electrowetting is presented. Each subcell is filled with a single (of course transparent) water droplet, serving as a movable iris, surrounded by opaque blackened decane. Each subcell measures 1 × 1 mm ² and incorporates 2 × 2 quadratically arranged positions for the droplet. All 3 × 3 droplets are actuated synchronously by electrowetting on dielectric (EWOD). The droplet speed is up to 12 mm/s at 130 V (Vrms) with response times of about 40 ms. Minimum operating voltage is 30 V. Horizontal and vertical movement of the droplets is demonstrated. Furthermore, a minor modification of the subcells allows us to exploit the flattening of each droplet. Hence, the opaque decane fluid sample can cover each water droplet and render each subcell opaque, resulting in switchable irises of constant opening diameter. The concept does not require any mechanically moving parts or external pumps.
The scales of white beetles strongly scatter light within a thin disordered network of
chitin filaments. There is no comparable artificial material achieving such a high scat-
tering strength within a thin layer of low refractive index material. Several analyses
investigated the scattering but could not explain the underlying concept. Here a model
system is described, which has the same optical properties as the white beetles’ scales
in the visible wavelength range. With some modification, it also explains the behavior
of the structures in the near infrared range. The comparison of the original structure and
the model system is done by finite-difference time-domain calculations. The calcula-
tions show excellent agreement with the beetles’ scales with respect to the reflectance,
the time-of-flight, and the intensity distribution in the far-field.
Based on the Lindblad master equation approach we obtain a detailed microscopic model of photons in a dye-filled cavity, which features condensation of light. To this end we generalise a recent non-equilibrium approach of Kirton and Keeling such that the dye-mediated contribution to the photon-photon interaction in the light condensate is accessible due to an interplay of coherent and dissipative dynamics. We describe the steady-state properties of the system by analysing the resulting equations of motion of both photonic and matter degrees of freedom. In particular, we discuss the existence of two limiting cases for steady states: photon Bose-Einstein condensate and laser-like. In the former case, we determine the corresponding dimensionless photon-photon interaction strength by relying on realistic experimental data and find a good agreement with previous theoretical estimates. Furthermore, we investigate how the dimensionless interaction strength depends on the respective system parameters.
Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N = 41 physics majors. We found that students’ performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding.Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixed method research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.
Influence of the Crystal Surface on the Austenitic and Martensitic Phase Transition in Pure Iron
(2018)
Using classical molecular dynamics simulations, we studied the influence that free
surfaces exert on the austenitic and martensitic phase transition in iron. For several single-indexed
surfaces—such as (100)bcc and (110)bcc as well as (100)fcc and (110)fcc surfaces—appropriate
pathways exist that allow for the transformation of the surface structure. These are the Bain,
Mao, Pitsch, and Kurdjumov–Sachs pathways, respectively. Tilted surfaces follow the pathway
of the neighboring single-indexed plane. The austenitic transformation temperature follows the
dependence of the specific surface energy of the native bcc phase; here, the new phase nucleates at
the surface. In contrast, the martensitic transformation temperature steadily decreases when tilting
the surface from the (100)fcc to the (110)fcc orientation. This dependence is caused by the strong
out-of-plane deformation that (110)fcc facets experience under the transformation; here, the new
phase also nucleates in the bulk rather than at the surface.
Motivation: Mathematical models take an important place in science and engineering.
A model can help scientists to explain dynamic behavior of a system and to understand
the functionality of system components. Since length of a time series and number of
replicates is limited by the cost of experiments, Boolean networks as a structurally simple
and parameter-free logical model for gene regulatory networks have attracted interests
of many scientists. In order to fit into the biological contexts and to lower the data
requirements, biological prior knowledge is taken into consideration during the inference
procedure. In the literature, the existing identification approaches can only deal with a
subset of possible types of prior knowledge.
Results: We propose a new approach to identify Boolean networks fromtime series data
incorporating prior knowledge, such as partial network structure, canalizing property,
positive and negative unateness. Using vector form of Boolean variables and applying
a generalized matrix multiplication called the semi-tensor product (STP), each Boolean
function can be equivalently converted into a matrix expression. Based on this, the
identification problem is reformulated as an integer linear programming problem to
reveal the system matrix of Boolean model in a computationally efficient way, whose
dynamics are consistent with the important dynamics captured in the data. By using
prior knowledge the number of candidate functions can be reduced during the inference.
Hence, identification incorporating prior knowledge is especially suitable for the case of
small size time series data and data without sufficient stimuli. The proposed approach is
illustrated with the help of a biological model of the network of oxidative stress response.
Conclusions: The combination of efficient reformulation of the identification problem
with the possibility to incorporate various types of prior knowledge enables the
application of computational model inference to systems with limited amount of time
series data. The general applicability of thismethodological approachmakes it suitable for
a variety of biological systems and of general interest for biological and medical research.