Miracle-dz/UniFAR-FTUs huggingface dataset:
The wealth of cities : How risky is our suburbanized residential built environment ? ( Draft )

This paper treats the stock of residential units as portfolios of neighbourhood built environments. It describes the frontiers of the portfolios in Toronto and examines their changes between 1991 and 1996. In this paper, I argue that suburbanization has brought about homogenous residential built environments and might have increased the risk of owning a home – an argument that is in contrary to what has been commonly believed: homogeneity has been welcomed by households because it is believed that households can better anticipate changes in the physical condition of the neighbourhood environment and can therefore derive a better sense of control over their housing asset. However, what has been neglected is that homogeneity has also increased the substitutability of nearby units. Increased substitutability implies that any changes in dwelling values can easily affect the value of other dwellings in the neighbourhood. That is, homogeneity might have increased housing values on the one hand, but has at the same time magnified the risk of housing values.
Influence of AGN jets on the magnetized ICM

Galaxy clusters are the largest structures for which there is observational evidence of a magnetised medium. Central cores seem to host strong magnetic fields ranging from a few 0.1 microG up to several 10 microG in cooling flow clusters. Numerous clusters harbor central powerful AGN which are thought to prevent cooling flows in some clusters. The influence of such feedback on the magnetic field remains unclear: does the AGN-induced turbulence compensate the loss of magnetic amplification within a cool core? And how is this turbulence sustained over several Gyr? Using high resolution magneto-hydrodynamical simulations of the self-regulation of a radiative cooling cluster, we study for the first time the evolution of the magnetic field within the central core in the presence of a powerful AGN jet. It appears that the jet-induced turbulence strongly amplifies the magnetic amplitude in the core beyond the degree to which it would be amplified by pure compression in the gravitational field of the cluster. The AGN produces a non-cooling core and increases the magnetic field amplitude in good agreement with microG field observations.
Stability and Uniqueness for the Crack Identification Problem

This paper deals with the identifiability of nonsmooth defects by boundary measurements, and the stability of their detection. We introduce and analyze a new pointwise regularity concept at the boundary of an open set which turns out to play a crucial role in the identifiability of defects by two boundary measurements. As a consequence, we prove the unique identifiability for a large class of closed sets, including sets with an infinite number of connected components of positive capacity and totally disconnected sets. In order to rigorously justify numerical approximation results of defects by optimal design methods, we prove a geometric stability result of the defect identification process, without any a priori smoothness assumptions.
A space contracting particle swarm optimization and its application in investment prediction

Multi-layer feed-forward network has a good ability of function approximation, but the usual training algorithm, BP algorithm may easily fall into local minimum and it has weak generalization ability. While the space contraction particle swarm optimization (SCPSO) algorithm has a good capability of global search, the training algorithm for multi-layer feed-forward network is constructed on the basis of the SCPSO. Considering the nonlinear feature of the investment issue, a multi-layer feed-forward network model is established. The SCPSO algorithm as learning algorithm is applied to training of multi-layer feed-ward network and then a simulated prediction is made. The comparison of the prediction result between the network based on SCPSO and BP network indicates that the former has high prediction accuracy.
The Use of Expected Future Variables in Macroeconometric Models

A more sophisticated expectational hypothesis than is traditionally used in the specification of macroeconometric models is tested in this paper. Economic agents are assumed to use a vector of variables Z(t) in forming their expectations for periods t+l and beyond. These expectations may or may not be rational in the Muth sense. The results provide some evidence in favor of the more sophisticated hypothesis, but they are not strong enough to allow much weight tobe put on the hypothesis as yet. The evidence in favor of the hypothesis is strongest for households' response to future wages and prices in their consumption and labor supply decisions and for the Fed's response to future inflation rates.The sensitivity of the policy properties of my macroeconometric model to the more sophisticated hypothesis is also examined in the paper. The properties are not sensitive for a policy action in which government expenditures are changed. They are somewhat sensitive for an action in which personal tax rates are changed. In the latter case the properties are also sensitive to whether or not the policy action is anticipated.
Approximate Bayesian Computation (ABC) in R: A Vignette

An implementation of Approximate Bayesian Computation (ABC) methods in the R language is available in the package abc . The aim of this vignette is to provide an extended overview of the capabilities of the package, with a detailed example of the analysis of real data. Some general information on installation procedures and object oriented programming in R are given in Section Introduction . The ABC methodology is brieﬂy illustrated in Section A brief introduction to ABC . A detailed example of an ABC analysis, where we aim to estimate the ancestral human population size is provided in Section A walk-though example: human demographic history . Users already familiar with the R and ABC methods may skip the ﬁrst two Sections and start with the example.
An Overview of Diabetes Management in Schizophrenia Patients: Office Based Strategies for Primary Care Practitioners and Endocrinologists

Diabetes is common and seen in one in five patients with schizophrenia. It is more prevalent than in the general population and contributes to the increased morbidity and shortened lifespan seen in this population. However, screening and treatment for diabetes and other metabolic conditions remain poor for these patients. Multiple factors including genetic risk, neurobiologic mechanisms, psychotropic medications, and environmental factors contribute to the increased prevalence of diabetes. Primary care physicians should be aware of adverse effects of psychotropic medications that can cause or exacerbate diabetes and its complications. Management of diabetes requires physicians to tailor treatment recommendations to address special needs of this population. In addition to behavioral interventions, medications such as metformin have shown promise in attenuating weight loss and preventing hyperglycemia in those patients being treated with antipsychotic medications. Targeted diabetes prevention and treatment is critical in patients with schizophrenia and evidence-based interventions should be considered early in the course of treatment. This paper reviews the prevalence, etiology, and treatment of diabetes in schizophrenia and outlines office based interventions for physicians treating this vulnerable population.
Credit, Prices, and Crashes: Business Cycles with a Sudden Stop

The 1990s emerging-markets crises were characterized by sudden reversals in inflows of foreign capital followed by unusually large declines in current account deficits, private expenditures, production, and prices of nontradable goods relative to tradables. This paper shows that these Sudden Stops can be the outcome of the equilibrium dynamics of a flexible-price economy with imperfect credit markets. Foreign debt is denominated in units of tradables and a liquidity constraint links credit-market access to the income generated in the nontradables sector and the relative price of nontradables. Sudden Stops occur when real shocks of foreign or domestic origin, or policy-induced shocks make this constraint binding. Sudden Stops are not reflected in long-run business cycle statistics but still they entail nontrivial welfare costs. These results question crises-management policies seeking to impose direct controls on private capital flows and favor those that work to weaken credit frictions.
Dynamic Model and Robust Control of Flexible Link Robot Manipulator

The problems of a flexible link manipulator are uncertainties and parametric nonlinearities. This paper presents design and development of a robust control based on linear quadratic regulator (LQR) for a flexible link manipulator. System performances were evaluated in terms of input tracking capability of hub angular position response, end-point displacement, end-point residual and hub velocity. For the controller of the system, LQR was developed to solve flexible link robustness and input tracking capability of hub angular position. The results achieved by the proposed controller are compared with conventional PID, to substantiate and verify the advantages of the proposed scheme and its promising potential in control of a flexible link manipulator. The robust control presented faster settling time and smaller overshoot responses and tracking performances of the proposed controller compared with PID controllers.
Calsyntenin-1 shelters APP from proteolytic processing during anterograde axonal transport

Summary Endocytosis of amyloid-&bgr; precursor protein (APP) is thought to represent the major source of substrate for the production of the amyloidogenic A&bgr; peptide by the &bgr;-secretase BACE1. The irreversible nature of proteolytic cleavage implies the existence of an efficient replenishment route for APP from its sites of synthesis to the cell surface. We recently found that APP exits the trans-Golgi network in intimate association with calsyntenin-1, a transmembrane cargo-docking protein for Kinesin-1-mediated vesicular transport. Here we characterized the function of calsyntenin-1 in neuronal APP transport using selective immunoisolation of intracellular trafficking organelles, immunocytochemistry, live-imaging, and RNAi. We found that APP is co-transported with calsyntenin-1 along axons to early endosomes in the central region of growth cones in carriers that exclude the &agr;-secretase ADAM10. Intriguingly, calsyntenin-1/APP organelles contained BACE1, suggesting premature cleavage of APP along its anterograde path. However, we found that APP contained in calsyntenin-1/APP organelles was stable. We further analyzed vesicular trafficking of APP in cultured hippocampal neurons, in which calsyntenin-1 was reduced by RNAi. We found a markedly increased co-localization of APP and ADAM10 in axons and growth cones, along with increased proteolytic processing of APP and A&bgr; secretion in these neurons. This suggested that the reduced capacity for calsyntenin-1-dependent APP transport resulted in mis-sorting of APP into additional axonal carriers and, therefore, the premature encounter of unprotected APP with its ectodomain proteases. In combination, our results characterize calsyntenin-1/APP organelles as carriers for sheltered anterograde axonal transport of APP.
Unmanned Aerial Vehicle Flight Point Classification Algorithm Based on Symmetric Big Data

Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.
Network application identification based on wavelet transform and K-means algorithm

The identification of network application based on port number is constrained by the status that more and more peer-to-peer applications make use of dynamic port numbers. Some clustering methods are used to cope with this problem, but they are time-consuming. In this paper, a method based on wavelet transforming and K-means is proposed. Its basic idea is doing data preprocessing with wavelet transformation prior to K-means algorithm. As the experiment results show, the time for data processing is shortened; moreover, the storage of data has been reduced; and finally, the classification precision of this method remain unaffected.
On the Capability of Sense Space Model for Word Sense Disambiguation

We had proposed a general model for WSD which is novel in some interesting ways and we have customized the model in form of a method (algorithm group) which is further customized in form of a number of algorithms to study the model's capability. We have introduced an optimization which has resulted in an unsupervised system which is the second best when evaluated against the renowned Senseval-3 international evaluation scheme, while it is the best non-hybrid unsupervised one.
Revenues and Asymmetric Information

ABSTRACT When planning and making travel decisions, tourists often lack sufficient information about two important aspects of their purchase decision: the quality of the service they are about to purchase and the possibility that their desired destination will be sold out before they place their reservation. An economic model is developed to explain the implications of these uncertainties where tourists resort to using the price of the service as an indicator of the two unknown quantities. The analysis indicates that the extent to which rates serve as a signal determines the maximum revenue the service provider can achieve. It further demonstrates that the impact of the signals on revenues can be either positive or negative. The tourism service provider controls the size of the information gap and, as such, the asymmetry should be viewed as a strategic issue. For efficient revenue maximization, decision makers ought to integrate the control and management of these two elements of information into their revenue management policies.
The Distribution of Public Sector Wage Premia: New Evidence Using Quantile Regression Methods

This paper documents the changing pattern of wage differentials between state and local government employees and their private sector counterparts during the 1979-1992 period. While the relative wages of women employed in the two sectors changed very little during this period, the relative wages of men employed in the state and local sector rose nearly 8%. There is substantial heterogeneity in the changes in relative wages of public and private sector employees during the 1980s. For highly educated workers, private sector wages rose significantly faster than public sector wages, while for those with at most a high school education, the public sector wage premium increased. We present both least squares and quantile regression estimates of the public sector premium. While the level of this premium is sensitive to our choice of quantile, the change in the premium, and the estimated pattern across skill levels, is not substantially affected by varying the quantile.
Research on underwater terrain-matching method based on double round template

Underwater terrain-matching aided navigation is one of the key technologies for navigational positioning of underwater vehicle. Compared to the underwater terrain-matching algorithm based on single-beam sounding system, the two-dimensional array single template matching algorithm based on multi-beam sounding system is characteristic of more information contents of terrain matching, higher matching accuracy and longer matching time. In this paper, a double round template was extracted from single template and two-dimensional elevation information were mapped into a grey-scale image. The Zernike matrix of this grey-scale image was calculated and the optimum matching position was determined through correlation computation. The simulation results demonstrated that this new method could shorten matching time effectively while maintaining high underwater matching accuracy.
Vehicle Tracking Incorporating Low-Rank Sparse into Particle Filter in Haze Scene

Aiming at difficulties for vehicle tracking in haze Scene, a new particle filter algorithm for tracking object vehicle is proposed in this paper. We incorporate low-rank sparse and adaptive dictionary learning template with the classical particle filter algorithm. We apply enhanced algorithm to track selected vehicle in urban traffic scene, demonstrate the performance of our method on the process of vehicle tracking in haze Scene. Our approach is different from conventional improved particle filter algorithm. Compared with related L1 and online MIL methods, experimental results show that the proposed low-rank sparse particle filter algorithm improves the synthesized efficiency of tracking system, the tracking experiments based on standard testing videos involving Dtneu_nebel demonstrate that tracking error is significantly reduced in the case of less time tracking.
Redescription of the meiofaunal gastropod Parhedyle cryptophthalma, with focus on nervous system and sensory organs

Parhedyle cryptophthalma (Westheide & Wawra, 1974) is a poorly known meiofaunal slug from the Mediterranean, inhabiting intertidal sands under direct wave impact. The present study is the first redescription of the acochlid P. cryptophthalma, confirming original results on general morphology, integumental spicules, and aberrant radula morphology by light and scanning electron microscopy. Our focus was on the central nervous system and sensory organs, using 3D reconstruction based on serial semi-thin sections and immunocytochemistry (staining of FMRFamide and Tyrosine Hydroxylase) in conjunction with confocal laser scanning microscopy. There is a mass of, yet undifferentiated, accessory ganglia anterior to the cerebral ganglia, typical for microhedylacean acochlidians. Apart from the common setting of ganglia (paired rhinophoral, cerebral, pedal, pleural, and buccal ganglia and three distinct ganglia on the visceral nerve cord), we found a putative osphradial ganglion for the first time in the microhedylacean clade. No osphradium, no Hancock’s organ and, in contrast to the original description, no pigmented eyes could be detected. Bundles of sensory cilia were found laterally on the head-foot complex and scattered cilia are present on the head appendages. FMRFamidergic immunoreactivity was detected in all cerebral ganglia but not in the accessory ganglia. Tyrosine Hydroxylase (TH) expression and aldehyde-induced fluorescence was observed in cerebral ganglia such as pedal ganglia, in labio tenctacular, rhinophoral, and pedal nerves and in single neurons in the anterior region of the foot sole. Central nervous and sensory features may greatly vary among acochlidians and other heterobranch taxa, and comprehensive comparative approaches are necessary to reveal their presence, function, homology, and evolution.
Safety algorithm for unmanned vehicles: Garud

The requirement of critical tasks, where it was inconvenient, dangerous or life-threatening for a human operator to be present, gave rise the development of unmanned ground vehicles. With the beginning of an era of unmanned ground vehicles, we have to be very meticulous about the safety of these vehicles and be keen on ensuring that they achieve the expected goal with the desired precision and accuracy. In order to overcome some of the major challenges of UGVs, we have come up with an extensive reliable algorithm which will not only improve the safety of UGVs but will also reduce the limitations of existing UGVs to minimal value. In this safety algorithm, we are using laser scanner, LMS110, and two CCTV cameras whose output is communicated over Ethernet, via TCP/IP protocol, to UNO-2182 controller for processing. The processed data is used during driving for steer control and brake control, as and when needed, by sending instant commands to the UGV motor. LMS110 sends the raw data through which the distance of the obstacle is calculated. The value, thus, obtained is scrutinized and checked for congruence with a pre-defined threshold list, which we have prepared after carrying out rigorous set of experiments and calculations for taking immediate decisions while navigation. With the help of this assembly, UGV will either autonomously make decisions about its path to be covered or pass the crucial information to a human operator at remote location, via Wi-Fi, who will control the vehicle through teleoperation.
A DSOM hierarchical model for re exive processing : anapplication to visual trajectory classi

Any intelligent system, whether human or robotic, must be capable of dealing with patterns over time. Temporal pattern processing can be achieved if the system has a short-term memory capacity (STM) so that diierent representations can be maintained for some time. In this work we propose a neural model wherein STM is realized by leaky integrators in a self-organizing system. The model exhibits compositionality, that is, it has the ability to extract and construct progressively complex and structured associations in an hierarchical manner, starting with basic and primitive (temporal) elements. An important feature of the proposed model is the use of temporal correlations to express dynamic bindings.
News and blog analysis with Lydia

The Lydia project seeks to build a relational model of people, places, and things through natural language processing of news sources and the statistical analysis of entity frequencies and co-locations. Our analysis is quite different from Google News. We track the temporal and spatial distribution of the entities in the news: who is being talked about, by whom, when, and where? Please visit our website http://www.textmap. com to see our analysis of recent news obtained from over 500 daily online news