Miracle-dz/UniFAR-FTUs huggingface dataset:
EQUILIBRIUM LAND USE PATTERNS IN A NONMONOCENTRIC CITY

In the 1970's, a new genre of mathematical urban land use theory, called the new urban economics (Mills and MacKinnon [S]), was developed. The principal characteristics of those models are the monocentricity of the city, the uniform transportation system, the homogeneity of households and production firms, and so forth (Richardson [ 161). Among these, the assumption of monocentricity seems t o be very crucial to the formulation of the models. By monocentricity, we assume that the city has a single, pre-specified center, the CBD, which has a fixed size and employs the city's entire labor force. This assumption greatly simplifies the analysis: for example, commuting trips can be exactly specified if the residential locations are known, and with the assumption of a linear or circular city, the spatial characteristics of each location in the city can be described simply by the distance from the CBD. However, from the viewpoint of theoretical completeness, the centrality or noncentrality of a city should be explained within the framework of the model, which incorporates interdependences among economic activities, without prespecified locations of employment activities. And, if the model succeeds in clarifying the conditions for the existence of the CBD, we can not only judge the adequacy of the monocentric assumption adopted by the current urban land use models but also step forward to a more fruitful theory which is capable of explaining various kinds of nonmonocentric phenomena. Moreover, from the viewpoint of reality, monocentricity is an implausible assumption. In the face of the tendency of increasing decentralization and a decline of the role of the CBD as a single focus of employment activity (Mills [9]), urban land use models based on the monocentric assumption are clearly inadequate for analyzing recent cities. ' Thus, both from the viewpoints of theoretical completeness and practical usefulness, the development of nonmonocentric models of urban land use is needed. There have been a few works which attempt to relax the monocentric assumption in two different ways. One approach is to introduce pre-specified
Distribution of two distinct lineages of sika deer (Cervus nippon) on Shikoku Island revealed by mitochondrial DNA analysis

ABSTRACT Nucleotide sequences of sika deer (Cervus nippon) collected from the eastern part of Shikoku Island were investigated & compared with those from other areas. Nucleotide sequence of the whole D-loop region of the mitochondrial DNA was determined by direct sequencing technique for each sample. The phylogenetic tree constructed by the sequences indicates that sika deer from Shikoku Island are divided into two distinct lineages: the northern Japan group and the southern Japan group. Proportion of the northern Japan lineage was higher in the northeastern part of the sampling area. There was no border between the distribution of the two lineages, rather it seemed that their distribution intermingled. Besides, there were locations where both lineages were found within a small area. These results indicate that two lineages might be hybridized in some areas of eastern Shikoku.
Analytical Probability Distributions and EM-Learning for Deep Generative Networks

Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known analytical form for the posterior and likelihood expectation, VAEs resort to approximations, including (Amortized) Variational Inference (AVI) and Monte-Carlo (MC) sampling. We exploit the Continuous Piecewise Affine (CPA) property of modern DGNs to derive their posterior and marginal distributions as well as the latter's first moments. These findings enable us to derive an analytical Expectation-Maximization (EM) algorithm that enables gradient-free DGN learning. We demonstrate empirically that EM training of DGNs produces greater likelihood than VAE training. Our findings will guide the design of new VAE AVI that better approximate the true posterior and open avenues to apply standard statistical tools for model comparison, anomaly detection, and missing data imputation.
The nuclear region of NGC 613 – II. Kinematics and stellar archaeology

In this work, we continue the study of the central region of NGC 613 by da Silva, Menezes & Steiner (Paper I), by analysing the stellar and gas kinematics and the stellar archaeology in optical and near-infrared data cubes. The high spatial resolution of the Gemini Multi-Object Spectrograph (GMOS) data cube allowed the detection, using spectral synthesis methods, of an inner circumnuclear ring, with a radius of ∼1 arcsec, composed of ∼109-yr stellar populations. Such a ring is located between the nucleus and the circumnuclear ring composed by H ii regions detected in previous works. Besides that, there is a stellar rotation around the nucleus and the rings follow the same direction of rotation with different velocities. The intensity-weighted average stellar velocity dispersion at the centre is 92 ± 3 km s−1. Three distinct gas outflow components were detected. The direction of the outflow observed with the H α emission line is compatible with the direction of the previously observed radio jet. The direction of one of the outflows detected in the [O iii]λ5007 emission coincides with the axis of the ionization cone. There is no difference regarding the stellar populations and the stellar kinematics along the double stellar emission, probably separated by a dust lane as mentioned in Paper I, confirming that they are part of the same structure.
Self-supervised Knowledge Distillation for Few-shot Learning

Real-world contains an overwhelmingly large number of object classes, learning all of which at once is infeasible. Few shot learning is a promising learning paradigm due to its ability to learn out of order distributions quickly with only a few samples. Recent works [7, 41] show that simply learning a good feature embedding can outperform more sophisticated meta-learning and metric learning algorithms for few-shot learning. In this paper, we propose a simple approach to improve the representation capacity of deep neural networks for few-shot learning tasks. We follow a two-stage learning process: First, we train a neural network to maximize the entropy of the feature embedding, thus creating an optimal output manifold using a self-supervised auxiliary loss. In the second stage, we minimize the entropy on feature embedding by bringing self-supervised twins together, while constraining the manifold with student-teacher distillation. Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process. Our codes are available at: this https URL.
Hydrogen Production from Nuclear Energy via High Temperature Electrolysis

This paper presents the technical case for high-temperature nuclear hydrogen production. A general thermodynamic analysis of hydrogen production based on high-temperature thermal water splitting processes is presented. Specific details of hydrogen production based on high-temperature electrolysis are also provided, including results of recent experiments performed at the Idaho National Laboratory. Based on these results, high-temperature electrolysis appears to be a promising technology for efficient large-scale hydrogen production.
Field evolution of the spin-liquid candidate 
YbMgGaO4

We report magnetization, heat capacity, thermal expansion, and magnetostriction measurements down to millikelvin temperatures on the triangular antiferromagnet $\mathrm{Yb}\mathrm{Mg}\mathrm{Ga}{\mathrm{O}}_{4}$. Our data exclude the formation of the distinct $\frac{1}{3}$ plateau phase observed in other triangular antiferromagnets, but reveal plateaulike features in second derivatives of the free energy, magnetic susceptibility, and specific heat, at ${\ensuremath{\mu}}_{0}H=1.0--2.5$ T for $H\phantom{\rule{0.16em}{0ex}}\ensuremath{\parallel}\phantom{\rule{0.16em}{0ex}}c$ and $2--5$ T for $H\ensuremath{\perp}c$. Using Monte Carlo simulations of a realistic spin Hamiltonian, we ascribe these features to nonmonotonic changes in the magnetization and the $\frac{1}{2}$ plateau that is smeared out by the random distribution of exchange couplings in $\mathrm{Yb}\mathrm{Mg}\mathrm{Ga}{\mathrm{O}}_{4}$.
Variants in the KCNE 1 or KCNE 3 gene and risk of Ménière ’ s disease : A meta-analysis

BACKGROUND: Ménière’s disease (MD) is defined as an idiopathic disorder of the inner ear characterized by the triad of tinnitus, vertigo, and sensorineural hearing loss. Although many studies have evaluated the association between variants in the KCNE1 or KCNE3 gene and MD risk, debates still exist. OBJECTIVE: Our aim is to evaluate the association between KCNE gene variants, including KCNE1 rs1805127 and KCNE3 rs2270676, and the risk of MD by a systematic review. METHODS: We searched the literature in PubMed, SCOPUS and EMBASE through May 2015. We calculated pooled odds ratios (OR) and 95% confidence intervals (CIs) using a fixed-effects model or a random-effects model for the risk to MD associated with different KCNE gene variants. The heterogeneity assumption decided the effect model. RESULTS: A total of three relevant studies, with 302 MD cases and 515 controls, were included in this meta-analysis. The results indicated that neither the KCNE1 rs1805127 variant (for G vs. A: OR = 0.724, 95%CI 0.320, 1.638, P = 0.438), nor the KCNE3 rs2270676 variant (for T vs. C: OR = 0.714, 95%CI 0.327, 1.559, P = 0.398) was associated with MD risk. CONCLUSIONS: Based on current evidence from published studies, neither of the two variants from KCNE was significantly associated with the risk of MD. Larger studies with mixed ethnicity subjects and stratified by clinical and sub-clinical characteristics are needed to validate our findings.
Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition

The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations.
Untangling tradeoffs between recurrence and self-attention in neural networks

Attention and self-attention mechanisms, inspired by cognitive processes, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role in model optimization and computation, and rely on considerable memory and computational resources that scale poorly. In this work, we present a formal analysis of how self-attention affects gradient propagation in recurrent networks, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies. Building on these results, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. While providing guarantees to avoid vanishing gradients, we use simple numerical experiments to demonstrate the tradeoffs in performance and computational resources by efficiently balancing attention and recurrence. Based on our results, we propose a concrete direction of research to improve scalability of attentive networks.
Sources of Business Cycle Fluctuations

What shocks account for the business cycle frequency and long-run movements of output and prices? This paper addresses this question using the identifying assumption that only supply shocks, such as shocks to technology, oil prices, and labor supply affect output in the long-run. Real and monetary aggregate demand shocks can affect output, but only in the short-run. This assumption sufficiently restricts the reduced form of key macroeconomic variables to allow estimation of the shocks and their effect on output and price at all frequencies. Aggregate demand shocks account for about 20 percent to 30 percent of output fluctuations at business cycle frequencies. Technological shocks account for about 1/4 of cyclical fluctuations, and about 1/3 of output's variance at low frequencies. Shocks to oil prices are important in explaining episodes in the 1970s and 1980s. Shocks that permanently affect labor input account for the balance of fluctuations in output, namely, about half of its variance at all frequencies.
Infrared-detected AGNs in the local Universe

Context. Spitzer/IRAC color selection is a promising technique to identify hot accreting nuclei, that is, active galactic nuclei (AGN), in galaxies. We demonstrate this using a small sample of SAURON galaxies and then explore this technique further. Aims. The goal of this study is to find a simple and efficient way to reveal optically obscured nuclear accretion in (nearby) galaxies. Methods. We applied an infrared selection method to the Spitzer Survey of Stellar Structures in Galaxies (S4G) sample of more than 2500 galaxies, together with its extension sample of more than 400 galaxies. We used the Spitzer colors to find galaxies in the S4G survey containing a hot core, suggesting the presence of a strong AGN, and we studied the detection fraction as a function of morphological type. We tested this infrared color selection method by examining the radio properties of the galaxies via the VLA NVSS and FIRST surveys. Results. Using the radio data, we demonstrate that galaxies displaying hot mid-infrared nuclei stand out as (candidate) active galaxies. We reproduce these results when using colors from the lower spatial resolution WISE mission instead of Spitzer. Hence multiband infrared imaging represents a useful tool to uncover optically obscured nuclear activity in galaxies.
A Comprehensive Study on Security issues in Android Mobile Phone — Scope and Challenges

Due to tremendous development and growth in mobile phone software and hardware technologies now Security issues is a very big challenge to all concerned persons such as scientists, manufacturers, designers, industrialists and so on. Usually, such technology takes time to be absorbed into the market and this gives time to the security teams to develop effective security controls. The rapid growth of the smart-phone market and the use of these devices for email, online banking, and accessing other forms of sensitive content has led to the emergence of a new and ever-changing threat landscape [1]. Along with this, the fact that anyone can be a user has led to the smart-phone appearing in the hands of almost every person before the proper security controls can be developed. Currently, android has the biggest share in the market among all the smart-phone operating systems. As the powers and features of such phones increase, their vulnerability also increases and makes them prone towards security threats. In the present paper, the authors have made a systematic study on why android security is important, what some of the potential vulnerabilities are and what security measures have been adopted currently to ensure security. Keywords— Android Security, Botnets, Cryptography, DroidDream, Online banking, Repackaging
What do families want to improve in the management of paediatric febrile neutropenia during anti-cancer treatment? Report of a patient/public involvement group

Background This study reports how parents and young people who had an experience of febrile neutropenia (FN) improved the design of a trial to inform the management of this condition. Five parents, a young person who had completed treatment and three clinician-researchers contributed. Methods The group was formed after an invitation on social media and met via video conference. Many participants were from an existing childhood-cancer parent-involvement group. The initial questions asked during discussion were about the importance of the topic, the views on the need for a trial, which important outcomes should be measured and the practical aspects which would make it easier or more difficult for people to take part in it. The conversation occurred for an entire afternoon, was audio and video recorded, transcribed, analysed and checked by those involved. The fifth parent added to this via email. Results The group altered the trial structure by proposing randomising of each child to one of the two management methods through the whole of their anti-cancer treatment, rather than randomising the study sites or the child at each visit. They felt that even if people declined taking part in the study in the first weeks of diagnosis, their views might change and they should be allowed to consent later. They also proposed methods of collecting important patient and family data, enriching the medical information gained in the study. Active follow-up, negotiated for each individual family, was also suggested. Conclusion Trials for improving the management of FN in children and young people who are undergoing anti-cancer treatments should consider individual-patient randomisation, collection of ‘quality of life’ and ‘experience of care’ aspects using digital and paper-based methods, engage families in shared decision-making about management options and ensure adequate supportive information is available and accessible to all patients regardless of background, geographical location or age.
Measuring Aggregate Price Indexes with Taste Shocks: Theory and Evidence for Ces Preferences

We develop an approach to measuring the cost of living for CES preferences that treats demand shocks as taste shocks that are equivalent to price shocks. In the presence of relative taste shocks, the Sato-Vartia price index is upward biased because an increase in the relative consumer taste for a variety lowers its taste-adjusted price and raises its expenditure share. By failing to allow for this association, the Sato-Vartia index underweights drops in taste-adjusted prices and overweights increases in taste-adjusted prices, leading to what we call a “taste-shock bias. ” We show that this bias generalizes to other invertible demand systems.
Role of N-Acylethanolamines in the Neuroinflammation: Ultramicronized Palmitoylethanolamide in the Relief of Chronic Pain and Neurodegenerative DiseasesRole of N-Acylethanolamines in the Neuroinflammation: Ultramicronized Palmitoylethanolamide in the Relief of Chronic Pain and Neurodegenerative Dise

Pain and neuroinflammation are protective responses aimed at preventing and removing injurious stimuli. However, when prolonged, they can override the bounds of physiological control and become destructive. Chronic pain and neuroinflammation are critical components in the pathophysiology of neurodegenerative diseases, stroke, spinal cord injury, diabetes, and neuropsychiatric disorders. Natural mechanisms, including the production of lipid mediators, represent an endogenous protective process and a program of resolution stimulated and triggered by tissue injury or inflammation. Lipid mediators include N-acylethanolamines (NAEs) such as palmitoylethanolamide (PEA), an endocannabinoid anandamide congener which has shown to be endowed of neuroprotective and antinflammatory properties activated under several pathological states. PEA does not bind the classical cannabinoid receptors but indirectly stimulates the effects of cannabinoids. Its antinflammatory, analgesic and neuroprotective actions have been however associated with peroxisome proliferator-activated receptor-α (PPAR-α) activation. The administration of exogenous PEA requires parenteral routes owing to its lipid structure. The micronized and ultramicronized (m- and um-) formulation permits oral administration increasing the versatility, easiness and compliance of administrations in clinical studies. This review is intended to deal with the effects of m- and um-PEA on chronic pain and neuroinflammation in several animal models of chronic pain and neudegenerative disorders and in clinical studies.
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets

Deep learning models extract, before a final classification layer, features or patterns which are key for their unprecedented advantageous performance. However, the process of complex nonlinear feature extraction is not well understood, a major reason why interpretation, adversarial robustness, and generalization of deep neural nets are all open research problems. In this paper, we use wavelet transformation and spectral methods to analyze the contents of image classification datasets, extract specific patterns from the datasets and find the associations between patterns and classes. We show that each image can be written as the summation of a finite number of rank-1 patterns in the wavelet space, providing a low rank approximation that captures the structures and patterns essential for learning. Regarding the studies on memorization vs learning, our results clearly reveal disassociation of patterns from classes, when images are randomly labeled. Our method can be used as a pattern recognition approach to understand and interpret learnability of these datasets. It may also be used for gaining insights about the features and patterns that deep classifiers learn from the datasets.