Miracle-dz/UniFAR-FTUs huggingface dataset:
Search and analysis of giant radio galaxies with associated nuclei (SAGAN)

Radio galaxies with jets of relativistic particles are usually hosted by massive elliptical galaxies with active nuclei powered by accretion of interstellar matter onto a supermassive black hole. In some rare cases (< 5%), their jets drive the overall structure to sizes larger than 700 kpc, and they are called giant radio galaxies (GRGs). A very small fraction of the population of such radio galaxies contains molecular and atomic gas in the form of rings or discs that can fuel star formation. The origin of this gas is not well known; it has sometimes been associated with a minor merger with a gas-rich disc galaxy (e.g. Centaurus A) or cooling of material from a hot X-ray atmosphere (e.g. cooling flows). The giant radio jets might be the extreme evolution of these objects, and they can teach us about the radio galaxy evolution. We selected 12 targets from a catalogue of 820 GRGs that are likely to be in a gas-accretion and star formation phase. The targets were selected from the mid-infrared to contain heated dust. We report here the results of IRAM-30m observations, the molecular gas content, and the star formation efficiency, and we discuss the origin of the gas and disc morphology. Three out of our 12 targets are detected, and for the others, we report significant upper limits. We combine our three detections and upper limits with four additional detected GRGs from the literature to discuss the results. Most of the GRG targets belong to the main sequence, and a large fraction are in the passive domain. Their star formation efficiency is comparable to normal galaxies, except for two galaxies that are deficient in molecular gas with a short (∼200 Myr) depletion time, and a quiescent gas-rich giant spiral galaxy. In general, the depletion time is much longer than the lifetime of the giant radio jet.
Designing energy-efficient sub-threshold logic circuits using equalization and non-volatile memory circuits using memristors

The very large scale integration (VLSI) community has utilized aggressive complementary metal-oxide semiconductor (CMOS) technology scaling to meet the everincreasing performance requirements of computing systems. However, as we enter the nanoscale regime, the prevalent process variation effects degrade the CMOS device reliability. Hence, it is increasingly essential to explore emerging technologies which are compatible with the conventional CMOS process for designing highly-dense memory/logic circuits. Memristor technology is being explored as a potential candidate in designing non-volatile memory arrays and logic circuits with high density, low latency and small energy consumption. In this thesis, we present the detailed functionality of multi-bit 1-Transistor 1-memRistor (1T1R) cell-based memory arrays. We present the performance and energy models for an individual 1T1R memory cell and the memory array as a whole. We have considered TiO2and HfOx-based memristors, and for these technologies there is a sub-10% difference between energy and performance
Visual cognition in deep-sea cephalopods : what we don ’ t know and why we don ’ t know it

A quick glance at the recent cephalopod literature, or even at the chapters of this book, tells us that when we talk about cephalopod cognition we are really considering cognition in a handful of genera. There can be no argument that studies of these animals have led to remarkable results that have challenged the traditional view of invertebrate intelligence. Yet when we consider that less than 10 species of cephalopod are commonly seen as the focus of behavioral studies, let alone in studies specifically about cognition, it becomes apparent that claims regarding the cognitive capabilities of cephalopods are generalizations drawn from work on a handful of genera. The majority of the 800 or so described species of cephalopod do not share the neritic and near-shore benthic habitats of the taxa with which we are most familiar; virtually unknown in terms of their behaviour and ecology, these species inhabit a different world in the deep, dark waters of the open ocean (Figure 10.1). In this chapter, we introduce and discuss the neglected cephalopods of the deep sea, many of which are not so distantly related to the species with which we are familiar, but whose existence in the deep sea has little in common with the complex reefs and nearshore habitats associated with taxa such as Octopus and Sepia. What effect might these differences in ecology have on the cognitive abilities of deep-sea cephalopods compared with their shallow water relatives? Chapter 9 of this volume (by Zylinski & Osorio) explored what we can deduce about cuttlefish visual cognition from the body patterns they use for camouflage. In this chapter we revisit the theme of making inferences about visual perceptive and cognitive abilities via body patterning in the handful of epipelagic and mesopelagic species that have been studied to date. Prior to that, we consider how the cognitive needs of these animals might differ from those of near-shore or shallow benthic environments. We explore what is known about the potential for visual cognition in deep-sea cephalopods and discuss why we know so little. Readers might find this chapter heavier on natural history and observation than the others in this book, but we hope that it will serve as a reminder that most cephalopods are essentially unknown beyond their descriptions.
A Top-Down Approach for GIS and Remote Sensing Integration

In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or top- down, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood (MLH) classification and a standard Object-Based Classification (OBC) using the boundaries. Using this approach we could reach to the 94% overall accuracy.
SYSTEMIC FUNDING LIQUIDITY SHOCK PROBABILITIES

Following the bankruptcy of Lehman Brothers in mid-September 2008, there were severe disruptions in international money markets and banks reportedly faced severe liquidity shocks, in particular US-dollar funding shortages, prompting central banks around the world to adopt unprecedented policy measures to supply funds to the banks. The turbulence also spilled over to the money market in Hong Kong. A better understanding of the forward-looking information content about funding liquidity risk in the prices of interest-rate derivative instruments is therefore necessary to gauge pressures on systemic liquidity. Using the market prices of the US-dollar LIBOR-overnight index swap (OIS) spread, we estimate the probability of the systemic funding liquidity shock during the crisis period, which deviated from zero on 18 September 2008 to 12%. This provided an early warning signal of the systemic liquidity shock on 29 September 2008 when the interbank market was paralysed and the Federal Reserve authorised a US$330 billion expansion of swap lines with other central banks. JEL classification: F31;G13
Inversion of induced polarization data

We present an algorithm for inverting induced polarization (IP) data acquired in a 3-D environment. The algorithm is based upon the linearized equation for the IP response, and the inverse problem is solved by minimizing an objective function of the chargeability model subject to data and bound constraints. The minimization is carried out using an interior-point method in which the bounds are incorporated by using a logarithmic barrier and the solution of the linear equations is accelerated using wavelet transforms. Inversion of IP data requires knowledge of the background conductivity. We study the effect of different approximations to the background conductivity by comparing IP inversions performed using different conductivity models, including a uniform half-space and conductivities recovered from one-pass 3-D inversions, composite 2-D inversions, limited AIM updates, and full 3-D nonlinear inversions of the dc resistivity data. We demonstrate that, when the background conductivity is simple, reasonable IP results are obtainable without using the best conductivity estimate derived from full 3-D inversion of the dc resistivity data. As a final area of investigation, we study the joint use of surface and borehole data to improve the resolution of the recovered chargeability models. We demonstrate that the joint inversion of surface and crosshole data produces chargeability models superior to those obtained from inversions of individual data sets.
Synthesis and Crystal Structure of 2-(3-Pyridyl)-3-(4-methylphenyl)-1,3-thiazolidin-4-one

The compound 2-(3-pyridyl)-3-(4-methyl phenyl)-1,3-thiazolidin-4-one has been synthesized. The compound crystallizes in the monoclinic system with space group P21/c; the cell parameters are a = 14.505(1)A, b = 8.024(1)A, c = 12.032(2)A, Z = 4, V = 1392.2(3)A.3 The final residual factor is R1 = 0.0695. The compound exhibits intermolecular hydrogen bonds of type C-H…N and C-H…O.
A statistical approach to ozone forecasting during fire season in the National Parks, Sierra Nevada, California

Observational data from four continuous ozone and weather monitoring sites operated by the National Park Service are used to develop a statistical forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The impact of fire smoke on these sites is incorporated in the model by using hourly PM2.5 concentrations predicted by a dynamic smoke transport model (BlueSky). Results from the study indicated the occurrence of significant increases in average ozone levels with increasing fire activity. However, the overall effect on diurnal ozone values seemed to be small (~2% of the variability) when compared with the amount of variability attributed to sources other than fire. For the 3 years in the study the increase in ozone that we were able to attribute to fire was less than 0.5 ppb in 95% of the cases and less than 1.6 ppb in 99% of cases.
Comparison of local image descriptors for plant identification Comparison of local image descriptors for plant identification from leaf images from leaf images

In this paper we present several descriptors used for the task of Plant recognition based on the images of leaves. The set of descriptors include texture based descriptors, fractal descriptors as well as some of the state of the art descriptors for image retrieval and object recognition in images. The descriptors are generated from the leaf images taken from single leaves on homogenous background. The descriptors are then used for training classifiers from a dataset of leaf images. The comparison of the obtained results will be presented in this paper.
Evolutionary Neural AutoML for Deep Learning

Deep neural networks (DNNs) have produced state-of-the-art re-sults in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a configuration is difficult and as a result, DNNs are often not used to their full potential. In addition, DNNs in commercial applications often need to satisfy real-world design constraints such as size or number of parameters. To make configuration easier, automatic machine learning (AutoML) systems for deep learning have been developed, focusing mostly on optimization of hyperparameters. This paper takes AutoML a step further. It introduces an evolutionary AutoML framework called LEAF that not only optimizes hyperparameters but also network architectures and the size of the network. LEAF makes use of both state-of-the-art evolutionary algorithms (EAs) and distributed computing frameworks. Experimental results on medical image classification and natural language analysis show that the framework can be used to achieve state-of-the-art performance. In particular, LEAF demonstrates that architecture optimization provides a significant boost over hyperparameter optimization, and that networks can be minimized at the same time with little drop in performance. LEAF therefore forms a foundation for democratizing and improving AI, as well as making AI practical in future applications.
Why Women Want to Play Sports: Identity, Culture, and Motivation

This paper is part of a series of research dedicated to specific issues uncovered in sports-for-peace programs. Other research has focused on cross-cultural issues, for example. In this research project, the authors were interested in how to encourage the inclusion and promotion of women in all sports around the world. The authors sought to discover who encouraged the women to play competitive sports, how long they had been playing sports, the barriers they encountered when playing competitive sports, and how they felt about identifying as sportswomen at the higher levels of competition. The authors used an on-line anonymous survey instrument and asked Division I college volleyball coaches to forward the link to their teams for voluntary participation in the study. While the authors review the literature as well as the recent history of many competitive sports for women, volleyball was chosen as the focus for the on-line survey because it is one of the most common sports women can play in college at the very competitive Division 1 level in the U.S. The survey questions were both demographic and open-ended in nature. The authors surveyed 149 women who played college-level sports. Narrative analysis was used to understand the themes presented in the open-ended question data. The authors propose that family dynamics, availability of sports programs, and gendered discourses have a combined effect on women’s orientation to particular sports, the women’s long term dedication to the sport, and at the competitive level at which they play. These findings should be considered before inviting women into sports-for-peace programs. The findings have global implications for inclusion and promotion of women in sporting activities. This research will be of interest to coaches, sportswomen, peace educators, gender educators, as well as practitioners who run international sports-for-peace programs.
Modeling Language Games

A central belief of the learning sciences is that learning is situated within communities of practice. This paper demonstrates a method for modeling learning within an online discourse community as two intersecting phenomena: members’ trajectories of participation and change in the community’s language norms. Using the word2vec algorithm, a language model is trained on 12 million comments over a decade, capturing a snapshot every month. This paper replicates prior findings showing that users’ individual language stabilizes while community norms progress, until users depart the community. We also show how the discourse community’s word meanings shift over time with respect to relational axes such as gender and morality.
Modeling Language Games

The last five years have seen great advances in natural language processing (NLP). Word2Vec (Mikholov et al., 2013) uses an embedding which maps words to high-dimensional vectors such that nearby words have similar meaning. GloVe (Pennington et al., 2014) takes a different approach, considering global word covariance statistics. Both models are effective for a wide range of NLP tasks (Baroni et al., 2014). Training these models relies on the fact that word meanings are based on their use. However, the results frame word meaning as fixed and global, whereas learning theories view word meaning as dynamically shaped through use in local “language games” (Wittgenstein, 1953). Talk is an important practice through which identities are performed; learning is described as increasingly central participation in the practices of a community (Lave & Wenger, 1991) and as “the process of identity formation in figured worlds” (Boaler & Greeno, 2000).
Brussel The AMP-Foot 3 , New Generation Propulsive Prosthetic Feet with Explosive Motion Characteristics-Design & Validation

The last decades, rehabilitation has become a challenging context for mechatronical engineering. From the state-of-the-art it is seen that the field of prosthetics offers very promising perspectives to roboticist. Today’s prosthetic feet tend to improve amputee walking experience by delivering the necessary push-off forces while walking. Therefore, several new types of (compliant) actuators are developed in order to fulfill the torque and power requirements of a sound ankle-foot complex with minimized power consumption. At the Vrije Universiteit Brussel, the Robotics & Multibody Mechanics research group puts a lot of effort in the design and development of new bionic feet. In 2013, the Ankle Mimicking Prosthetic (AMP-) Foot 2, as a proof-of-concept, showed the advantage of using the Explosive Elastic Actuator capable of delivering the full ankle torques (±120 Nm) and power (±250 W ) with only a 60 W motor. In this article, the authors present the AMP-Foot 3, using an improved actuation method and using two locking mechanisms for improved energy storage during walking. The article focusses on the mechanical design of the device and validation of its working principle.
Dip moveout in the frequency-wavenumber domain

In recent years dip moveout (DMO) has come into routine use in the seismic processing industry. The main benefits of including DMO in the processing sequence are that (1) stacking velocities after DMO are dip-independent, and (2) "reflection point smear" associated with dipping events is eliminated by laterally shifting the reflection points to their zero-offset position. These effects of DMO are also beneficial for estimation of stacking velocities by simplifying the interpretation of velocity analysis. Dip moveout also has an inherent dip filtering effect (Bolondi et al., 1984) by lowering the frequency content on the stacked section of steeply dipping aliased events, which leads to reduced migration noise.
Response of Benthic Microalgal Community Composition at East Beach, Galveston Bay, Texas to Changes in Salinity and Nutrients

Response of Benthic Microalgal Community Composition at East Beach, Galveston Bay, Texas to Changes in Salinity and Nutrients. (May 2009) Alyce Rebekah Lee, B.S., Salem State College; M.S., Marshall University Graduate College Co-chairs of Advisory Committee: Dr. Daniel C. O. Thornton Dr. James L. Pinckney Benthic microalgal community composition on an ephemerally submerged sandflat at East Beach, Galveston Island, Texas was studied to determine the spatial and temporal variability of total biomass and community composition and its responses to experimental manipulations of two environmental factors (salinity and nutrients). Four field studies were conducted between August 2004 and February 2005. The community consisted of two major algal groups, diatoms, and cyanobacteria with two less abundant groups, green algae, and phototrophic bacteria. Spatial variability showed that patch sizes of 12 25 m were detected over larger scales with smaller scale (cm) patches of approximately 28 201 cm -2 contained within the larger patches. The second study examined the spatio-temporal variability of BMA over a 21-month period in a 1,000 m 2 area. Sampling location and date explained a significant amount of the variability in the abundances of algal groups, which were positively correlated with the water content of the sediments and negatively correlated with temperature (sediment and water). All of the algal groups showed a seasonal pattern with higher abundances measured in the
Meltwater delivery from the tidewater glacier Kronebreen to Kongsfjorden, Svalbard : insights from in-situ and remote-sensing analyses of sediment plumes

Tidewater glaciers form a significant drainage catchment of glacierised areas, directly transporting meltwater from the terrestrial to the marine environment. Surface melt of glaciers in the Arctic is increasing in response to warmer atmospheric temperatures, whilst tidewater glaciers are also exposed to warmer ocean temperatures, stimulating submarine melt. Increased freshwater discharge not only freshens fjord waters, but also plays a key role in glacimarine sedimentary processes, transporting sediment to glacial fjords. Despite this, the temporal evolution of meltwater production, storage and release from tidewater glacier systems at seasonal and interannual time scales is poorly understood. This leaves large uncertainties in the predictions for future sea level rise, ocean circulation and the impacts on the marine ecosystem. This study focuses on Kronebreen, a tidewater glacier which flows into the head of Kongsfjorden, north west Svalbard. Surface melt produces freshwater runoff, which is discharged from the grounding line as a buoyant, sediment laden plume, which spreads laterally across the surface water. This supraglacial melt is the dominant freshwater source, contributing an order of magnitude more freshwater to Kongsfjorden, than direct submarine melting of the ice face. Calibration of MODIS band 1 satellite imagery with in situ measurements of Total Suspended Solids and spectral reflectance, provides a method to quantify meltwater and sediment discharge. Plume extent has been determined for each cloud free day, from June to September, 2002 - 2013. Analysis of plume extent with atmospheric temperature and modeled surface runoff, gives a source to sea insight to meltwater production, storage and discharge. The extent of the plume changes in response to meltwater; larger plumes form when discharge increases. These results reveal that meltwater discharge into Kongsfjorden lags atmospheric temperature, the primary driver of meltwater production, by over a week during June and July. This is reduced to only 1 - 2 days in August and September, indicating a decline in meltwater storage as the ablation season progresses, and the development of more efficient glacial drainage. Sediment plumes respond to meltwater production, making them a valuable tool for quantifying meltwater discharge from a tidewater glacier. Insights to glacier hydrology can also be obtained when surface processes are also considered. This furthers the understanding of tidewater glacier hydrology, which is valuable for improving the accuracy of sea level rise predictions.