Flow behaviour of dielectric liquids in an electric field

A family of 10 silicone oils with electrical conductivity similar to 10(-13) S m(-1) (a regime hitherto systematically unexplored) and viscosities ranging from 1 to 2000mPas have been Subjected to an electrical field of up to 1.5kV mm(-1) during flow from a needle. The flow behaviour of these liquids is investigated experimentally in the flow rate regime 10(-8)-10(-12) m(3) s(-1) and we analyse the results using the Ohnesorge number. Due to the low electrical conductivity and high electrical relaxation time of the silicone oils, only unsteady transient jets were found. The onset of this type of jetting has been defined using current measurements and, in contrast to conducting liquids, the non-dimensional jet diameter increases with increase in Ohnesorge number. The time elapsed between the start and finish of jetting increases with increasing Ohnesorge number

Millisecond Time Variations of X-Ray Binaries

The Rossi X-Ray Timing Explorer (RXTE) has found that the neutron stars in low-mass X-ray binaries exhibit oscillations in the range 300-1200 Hz. Persistent emission may exhibit one or both of two features. In bursts a nearly coherent pulsation is seen, which may be the rotation period of the neutron star. For some the frequency equals the difference between the two higher frequencies, suggesting a beat frequency model, but in others it is twice the difference. Similar maximum frequencies suggests that it corresponds to the Kepler orbit frequency at the minimum stable orbit or the neutron star surface, either of which would determine the neutron star masses, radii and equation of state. Theories of accretion onto black holes predict a quasi-periodic oscillation (QPO) related to the inner accretion disk. The two microquasar black hole candidates (BHCs) have exhibited candidates for this or related frequencies.Comment: 4 pages, to be published in the proceedings of IAU Symposium 188: The Hot Univers

Proteins across scales through graph partitioning: application to the major peanut allergen Ara h 1

The analysis of community structure in complex networks has been given much attention recently, as it is hoped that the communities at various scales can affect or explain the global behaviour of the system. A plethora of community detection algorithms have been proposed, insightful yet often restricted by certain inherent resolutions. Proteins are multi-scale biomolecular machines with coupled structural organization across scales, which is linked to their function. To reveal this organization, we applied a recently developed multi-resolution method, Markov Stability, which is based on atomistic graph partitioning, along with theoretical mutagenesis that further allows for hot spot identification using Gaussian process regression. The methodology finds partitions of a graph without imposing a particular scale a priori and analyses the network in a computationally efficient way. Here, we show an application on peanut allergenicity, which despite extensive experimental studies that focus on epitopes, groups of atoms associated with allergenic reactions, remains poorly understood. We compare our results against available experiment data, and we further predict distal regulatory sites that may significantly alter protein dynamics

IFN-gamma is associated with risk of Schistosoma japonicum infection in China.

Before the start of the schistosomiasis transmission season, 129 villagers resident on a Schistosoma japonicum-endemic island in Poyang Lake, Jiangxi Province, 64 of whom were stool-positive for S. japonicum eggs by the Kato method and 65 negative, were treated with praziquantel. Forty-five days later the 93 subjects who presented for follow-up were all stool-negative. Blood samples were collected from all 93 individuals. S. japonicum soluble worm antigen (SWAP) and soluble egg antigen (SEA) stimulated IL-4, IL-5 and IFN-gamma production in whole-blood cultures were measured by ELISA. All the subjects were interviewed nine times during the subsequent transmission season to estimate the intensity of their contact with potentially infective snail habitats, and the subjects were all re-screened for S. japonicum by the Kato method at the end of the transmission season. Fourteen subjects were found to be infected at that time. There was some indication that the risk of infection might be associated with gender (with females being at higher risk) and with the intensity of water contact, and there was evidence that levels of SEA-induced IFN-gamma production were associated with reduced risk of infection

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Scaling Laws in Human Language

Zipf's law on word frequency is observed in English, French, Spanish, Italian, and so on, yet it does not hold for Chinese, Japanese or Korean characters. A model for writing process is proposed to explain the above difference, which takes into account the effects of finite vocabulary size. Experiments, simulations and analytical solution agree well with each other. The results show that the frequency distribution follows a power law with exponent being equal to 1, at which the corresponding Zipf's exponent diverges. Actually, the distribution obeys exponential form in the Zipf's plot. Deviating from the Heaps' law, the number of distinct words grows with the text length in three stages: It grows linearly in the beginning, then turns to a logarithmical form, and eventually saturates. This work refines previous understanding about Zipf's law and Heaps' law in language systems.Comment: 6 pages, 4 figure

Deeply Embedded Protostellar Population in the Central Molecular Zone Suggested by H2_2O Masers and Dense Cores

The Central Molecular Zone (CMZ), usually referring to the inner 500 pc of the Galaxy, contains a dozen of massive ($\sim10^5$ $M_\odot$) molecular clouds. Are these clouds going to actively form stars like Sgr B2? How are they affected by the extreme physical conditions in the CMZ, such as strong turbulence? Here we present a first step towards answering these questions. Using high-sensitivity, high angular resolution radio and (sub)millimeter observations, we studied deeply embedded star formation in six massive clouds in the CMZ, including the 20 and 50 km s$^{-1}$ clouds, Sgr B1 off (as known as dust ridge clouds e/f), Sgr C, Sgr D, and G0.253-0.016. The VLA water maser observations suggest a population of deeply embedded protostellar candidates, many of which are new detections. The SMA 1.3 mm continuum observations reveal peaks in dust emission associated with the masers, suggesting the existence of dense cores. While our findings confirm that clouds such as G0.253-0.016 lack internal compact substructures and are quiescent in terms of star formation, two clouds (the 20 km s$^{-1}$ cloud and Sgr C) stand out with clusters of water masers with associated dense cores which may suggest a population of deeply embedded protostars at early evolutionary phases. Follow-up observations with VLA and ALMA are necessary to confirm their protostellar nature

Label delay in online continual learning

Online continual learning, the process of training models on streaming data, has gained increasing attention in recent years. However, a critical aspect often overlooked is the label delay, where new data may not be labeled due to slow and costly annotation processes. We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps. In each step, the framework reveals both unlabeled data from the current time step t and labels delayed with d steps, from the time step t−d. In our extensive experiments amounting to 1060 GPU days, we show that merely augmenting the computational resources is insufficient to tackle this challenge. Our findings underline a notable performance decline when solely relying on labeled data when the label delay becomes significant. More surprisingly, when using state-of-the-art SSL and TTA techniques to utilize the newer, unlabeled data, they fail to surpass the performance of a naïve method that simply trains on the delayed supervised stream. To this end, we introduce a simple, efficient baseline that rehearses from the labeled memory samples that are most similar to the new unlabeled samples. This method bridges the accuracy gap caused by label delay without significantly increasing computational complexity. We show experimentally that our method is the least affected by the label delay factor and in some cases successfully recovers the accuracy of the non-delayed counterpart. We conduct various ablations and sensitivity experiments, demonstrating the effectiveness of our approach

System-Level Performance Analysis in 3D Drone Mobile Networks

We present a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. A performance boundary derived by deterministic random (Brownian) motion model over Nakagami-m fading interfering channels is developed. This method allows us to circumvent the extremely complex reality model and obtain the upper and lower performance bounds of actual drone mobile networks. The validity and advantages of the proposed framework are confirmed via extensive Monte-Carlo (MC) simulations. The results reveal several important trends and design guidelines for the practical deployment of drone mobile networks

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure