Miracle-dz/UniFAR-FTUs huggingface dataset:
GASP XVIII: star formation quenching due to AGN feedback in the central region of a jellyfish galaxy

We report evidence for star formation quenching in the central 8.6 kpc region of the jellyfish galaxy JO201 which hosts an active galactic nucleus, while undergoing strong ram pressure stripping. The ultraviolet imaging data of the galaxy disk reveal a region with reduced flux around the center of the galaxy and a horse shoe shaped region with enhanced flux in the outer disk. The characterization of the ionization regions based on emission line diagnostic diagrams shows that the region of reduced flux seen in the ultraviolet is within the AGN-dominated area. The CO J$_{2-1}$ map of the galaxy disk reveals a cavity in the central region. The image of the galaxy disk at redder wavelengths (9050-9250 {\AA}) reveals the presence of a stellar bar. The star formation rate map of the galaxy disk shows that the star formation suppression in the cavity occurred in the last few 10$^8$ yr. We present several lines of evidence supporting the scenario that suppression of star formation in the central region of the disk is most likely due to the feedback from the AGN. The observations reported here make JO201 a unique case of AGN feedback and environmental effects suppressing star formation in a spiral galaxy.
A framework for the extraction of Deep Neural Networks by leveraging public data

Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in which an adversary extracts an approximation of MLaaS models by making black-box queries to it. However, none of these works is able to satisfy all the three essential criteria for practical model extraction: (1) the ability to work on deep learning models, (2) the non-requirement of domain knowledge and (3) the ability to work with a limited query budget. We design a model extraction framework that makes use of active learning and large public datasets to satisfy them. We demonstrate that it is possible to use this framework to steal deep classifiers trained on a variety of datasets from image and text domains. By querying a model via black-box access for its top prediction, our framework improves performance on an average over a uniform noise baseline by 4.70x for image tasks and 2.11x for text tasks respectively, while using only 30% (30,000 samples) of the public dataset at its disposal.
EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation

One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation - EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.
A Study of the 20 day Superorbital Modulation in the High-mass X-Ray Binary IGR J16493-4348

We report on Nuclear Spectroscopic Telescope Array (NuSTAR), Neil Gehrels Swift Observatory (Swift) X-ray Telescope (XRT), and Swift Burst Alert Telescope (BAT) observations of IGR J16493-4348, a wind-fed supergiant X-ray binary showing significant superorbital variability. From a discrete Fourier transform of the BAT light curve, we refine its superorbital period to be 20.058 ± 0.007 days. The BAT dynamic power spectrum and a fractional root mean square analysis both show strong variations in the amplitude of the superorbital modulation, but no observed changes in the period are found. The superorbital modulation is significantly weaker between MJD 55,700 and MJD 56,300. The joint NuSTAR and XRT observations, which were performed near the minimum and maximum of one cycle of the 20 day superorbital modulation, show that the flux increases by more than a factor of two between superorbital minimum and maximum. We find no significant changes in the 3–50 keV pulse profiles between superorbital minimum and maximum, which suggests a similar accretion regime. Modeling the pulse-phase-averaged spectra we find a possible Fe Kα emission line at 6.4 keV at superorbital maximum. This feature is not significant at superorbital minimum. While we do not observe any significant differences between the pulse-phase-averaged spectral continua apart from the overall flux change, we find that the hardness ratio near the broad main peak of the pulse profile increases from superorbital minimum to maximum. This suggests the spectral shape hardens with increasing luminosity. We discuss different mechanisms that might drive the observed superorbital modulation.
A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles

This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving policy based on Reinforcement Learning. The proposed policy makes minimal or no assumptions about the environment, since no a priori knowledge about the system dynamics is required. We compare the performance of the proposed policy against an optimal policy derived via Dynamic Programming and against manual driving simulated by SUMO traffic simulator.
The Problem of Adhesion Methods and Locomotion Mechanism Development for Wall-Climbing Robots

This review considers a problem in the development of mobile robot adhesion methods with vertical surfaces and the appropriate locomotion mechanism design. The evolution of adhesion methods for wall-climbing robots (based on friction, magnetic forces, air pressure, electrostatic adhesion, molecular forces, rheological properties of fluids and their combinations) and their locomotion principles (wheeled, tracked, walking, sliding framed and hybrid) is studied. Wall-climbing robots are classified according to the applications, adhesion methods and locomotion mechanisms. The advantages and disadvantages of various adhesion methods and locomotion mechanisms are analyzed in terms of mobility, noiselessness, autonomy and energy efficiency. Focus is placed on the physical and technical aspects of the adhesion methods and the possibility of combining adhesion and locomotion methods.
Ecosystem effects of fishing & El Niño at the Galápagos Marine Reserve

The Galápagos Archipelago is home to a diverse range of marine bioregions due to the confluence of several cold and warm water currents, resulting in some of the most productive tropical marine ecosystems in the world. These ecosystems are strongly influenced by El Niño events which can reduce primary production by an order of magnitude, dramatically reducing energy available throughout the food web. Fisheries are an important component of the local economy, although artisanal and illegal overfishing have dramatically reduced the productivity of invertebrate and finfish resources in recent decades, resulting in reductions in catches for local fishers. The regionally-endemic sailfin grouper (Myctereoperca olfax), locally known as bacalao, was once the most important fished species in the Galápagos, but is now listed as vulnerable by the IUCN due to its limited range and dramatic declines in catch over time. It is unknown how reduction of this predatory species has affected ecosystem structure and function. In the absence of stock assessments, we used an estimate of unfished bacalao biomass from fishers’ ecological knowledge along with unfished biomass estimates of other heavily exploited stocks—lobster (Panulirus penicillatus and P. gracilis) and sea cucumber (Isostichopus fuscus)—to create historical, unfished versions of existing modern day ecosystem models. We used the unfished and modern versions of the ecosystem models to test the ecosystem effects of bacalao exploitation at the Bolivar Channel, located in the cold, west upwelling bioregion of the archipelago during both El Niño and non El Niño years, and at Floreana Island, in the warmer, central bioregion. Fishers’ ecological knowledge indicates that at present, the biomass of bacalao is at least seven times lower than when unfished. This reduced bacalao biomass is linked with a greatly reduced ecosystem role compared to when unfished, and ecosystem role is further reduced in El Niño years. Allowing bacalao populations to rebuild to at least half of unfished biomass would partially restore their role within these ecosystems, while also resulting in greater fisheries catches. Comparing ecosystem impacts caused by fishing and El Niño, fishing has had a greater negative impact on bacalao ecosystem role than regular El Niño events.
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

Click-through rate (CTR) prediction is critical for industrial applications such as recommender system and online advertising. Practically, it plays an important role for CTR modeling in these applications by mining user interest from rich historical behavior data. Driven by the development of deep learning, deep CTR models with ingeniously designed architecture for user interest modeling have been proposed, bringing remarkable improvement of model performance over offline metric. However, great efforts are needed to deploy these complex models to online serving system for realtime inference, facing massive traffic request. Things turn to be more difficult when it comes to long sequential user behavior data, as the system latency and storage cost increase approximately linearly with the length of user behavior sequence. In this paper, we face directly the challenge of long sequential user behavior modeling and introduce our hands-on practice with the co-design of machine learning algorithm and online serving system for CTR prediction task. (i) From serving system view, we decouple the most resource-consuming part of user interest modeling from the entire model by designing a separate module named UIC (User Interest Center). UIC maintains the latest interest state for each user, whose update depends on realtime user behavior trigger event, rather than on traffic request. Hence UIC is latency free for realtime CTR prediction. (ii) From machine learning algorithm view, we propose a novel memory-based architecture named MIMN (Multi-channel user Interest Memory Network) to capture user interests from long sequential behavior data, achieving superior performance over state-of-the-art models. MIMN is implemented in an incremental manner with UIC module. Theoretically, the co-design solution of UIC and MIMN enables us to handle the user interest modeling with unlimited length of sequential behavior data. Comparison between model performance and system efficiency proves the effectiveness of proposed solution. To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands. It now has been deployed in the display advertising system in Alibaba.
Exploring Query Results

Users typically interact with a database by asking queries and examining results provided by the database system. We refer to the user examining the results of a query and asking follow-up questions as query result exploration. Our work builds on two decades of database community research on provenance. One of the contributions of this paper is that we identify constraints in the context of query result exploration that have previously been unexplored in provenance research. These constraints are useful in optimizing performance and thus improving the user experience during query result exploration. Further, three approaches for computing provenance have been described in the literature: (a) in the lazy approach, no additional data is materialized and provenance is computed from the base tables as needed; (b) in the eager approach, additional data is materialized and provenance is computed from this additional materialized data; (c) in the hybrid approach, some additional data is materialized, and provenance is computed using this additional materialized data as well as the base tables. In this paper, we investigate lazy and eager approaches that utilize constraints in the context of query result exploration, as well as novel hybrid approaches, where the keys for some of the base tables are materialized as selected by a cost model. For the TPC-H benchmark, we find that the constraints that we identify are applicable to 19 out of the 22 queries, and result in a better performance of the lazy approach for 7 out of these 19 queries. With materialization, our hybrid approach resulted in a better performance for all but one single table TPC-H query with no joins (where existing approach performed as good as our approach). Further, the performance benefit from our approaches are significant, sometimes several orders of magnitude, compared to previous research.
For the last time!

Group B streptococcus (GBS) is the leading infectious cause of morbidity and mortality in Australian newborns. Although intrapartum chemoprophylaxis is recommended to reduce the risk of neonatal GBS transmission and disease, controversy exists as to the best method to select women 'at risk' for this treatment. Our study aimed to survey the opinions of obstetricians and neonatologists currently in practice in Australia on GBS screening and treatment. Of the 488 obstetricians and 68 neonatologists currently in practice who responded to the survey, 271 obstetricians (56%) and 40 neonatologists (61%) supported universal antenatal screening. Of those respondents who did not support a universal antenatal screening policy, 196 (93%) and 24 (92%) of the obstetricians and neonatologists respectively, supported antenatal screening based on risk factors. This diversity in practitioner opinion highlights the lack of certainty in the literature as to the best management strategy to prevent neonatal GBS sepsis.
An Overview of Computational Approaches for Interpretation Analysis

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.
The microbiome profiling of fungivorous black tinder fungus beetle Bolitophagus reticulatus reveals the insight into bacterial communities associated with larvae and adults

Saproxylic beetles play a crucial role in key processes occurring in forest ecosystems, and together with fungi contribute to the decomposition and mineralization of wood. Among this group are mycetophilic beetles which associate with wood-decaying fungi and use the fruiting body for nourishment and development. Therefore, their feeding strategy (especially in the case of fungivorous species) requires special digestive capabilities to take advantage of the nutritional value of fungal tissue. Although polypore-beetle associations have been investigated in numerous studies, detailed studies focusing on the microbiome associated with species feeding on fruiting bodies of polypores remain limited. Here we investigated the bacterial communities associated with larvae and adults of Bolitophagus reticulatus collected from Fomes fomentarius growing on two different host tree: beech (Fagus sp.) and birch (Betula sp.), respectively. Among 24 identified bacterial phyla, three were the most relatively abundant (Proteobacteria, Actinobacteria and Bacteroidetes). Moreover, we tried to find unique patterns of bacteria abundances which could be correlated with the long-term field observation showing that the fruiting bodies of F. fomentarius, growing on birch are more inhabited by beetles than fruiting bodies of the same fungus species growing on beech. Biochemical analyses showed that the level of protease inhibitors and secondary metabolites in F. fomentarius is higher in healthy fruiting bodies than in the inhabited ones. However, tested microbiome samples primarily clustered by developmental stage of B. reticulatus and host tree did not appear to impact the taxonomic distribution of the communities. This observation was supported by statistical analyses.
Deep Learning Methods for Predicting Disease Status Using Genomic Data

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature search. All four articles first used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. These deep learning approaches outperformed existing prediction methods, such as prediction based on transcript-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.
Black holes in the quantum universe

A succinct summary is given of the problem of reconciling observation of black hole-like objects with quantum mechanics. If quantum black holes behave like subsystems, and also decay, their information must be transferred to their environments. Interactions that accomplish this with ‘minimal’ departure from a standard description are parametrized. Possible sensitivity of gravitational wave or very long baseline interferometric observations to these interactions is briefly outlined. This article is part of a discussion meeting issue ‘Topological avatars of new physics’.
Revisiting hard thresholding for DNN pruning

The most common method for DNN pruning is hard thresholding of network weights, followed by retraining to recover any lost accuracy. Recently developed smart pruning algorithms use the DNN response over the training set for a variety of cost functions to determine redundant network weights, leading to less accuracy degradation and possibly less retraining time. For experiments on the total pruning time (pruning time + retraining time) we show that hard thresholding followed by retraining remains the most efficient way of reducing the number of network parameters. However smart pruning algorithms still have advantages when retraining is not possible. In this context we propose a novel smart pruning algorithm based on difference of convex functions optimisation and show that it is often orders of magnitude faster than competing approaches while achieving the lowest classification accuracy degradation. Furthermore we investigate theoretically the effect of hard thresholding on DNN accuracy. We show that accuracy degradation increases with remaining network depth from the pruned layer. We also discover a link between the latent dimensionality of the training data manifold and network robustness to hard thresholding.
Enhanced thermal sensitivity of MEMS bolometers integrated with nanometer-scale hole array structures

We have fabricated two-dimensional phononic crystal (PnC) structures on GaAs doubly-clamped microelectromechanical system (MEMS) beam resonators to modulate their thermal properties. Owing to the reduction in the thermal conductance of the MEMS beams by introducing the PnC structures, the MEMS bolometers with the PnC structures show 2-3 times larger thermal sensitivities than the unpatterned reference sample. Furthermore, since the heat capacitance of the MEMS beams is also reduced by introducing the PnCs, the thermal decay time of the patterned MEMS beams is increased only by about 30-40 %, demonstrating the effectiveness of the PnCs for enhancing the thermal sensitivities of bolometers without significantly deteriorating their operation bandwidths.
Two stage cluster for resource optimization with Apache Mesos

As resource estimation for jobs is difficult, users often overestimate their requirements. Both commercial clouds and academic campus clusters suffer from low resource utilization and long wait times as the resource estimates for jobs, provided by users, is inaccurate. We present an approach to statistically estimate the actual resource requirement of a job in a Little cluster before the run in a Big cluster. The initial estimation on the little cluster gives us a view of how much actual resources a job requires. This initial estimate allows us to accurately allocate resources for the pending jobs in the queue and thereby improve throughput and resource utilization. In our experiments, we determined resource utilization estimates with an average accuracy of 90% for memory and 94% for CPU, while we make better utilization of memory by an average of 22% and CPU by 53%, compared to the default job submission methods on Apache Aurora and Apache Mesos.