Miracle-dz/UniFAR-FTUs huggingface dataset:
X‐ray bright sources in the Chandra Small Magellanic Cloud Wing Survey – detection of two new pulsars

ABSTRACT We investigate the X-ray and optical properties of a sample of X-ray bright sourcesfrom the Small Magellanic Cloud (SMC) Wing Survey. We have detected two newpulsars with pulse periods of 65.8 s (CXOU J010712.6-723533) and 700 s (CXOUJ010206.6-714115), and present observations of two previously known pulsars RXJ0057.3-7325 (SXP101) and SAX J0103.2-7209 (SXP348). Our analysis has led tothree new optical identiﬁcations for the detected pulsars. We ﬁnd long-term opticalperiods for two of the pulsars, CXOU J010206.6-714115 and SXP101, of 267 and 21.9d, respectively. Spectral analysis of a sub-set of the sample shows that the pulsarshave harder spectra than the other sources detected. By employing a quantile-basedcolour-colour analysis we are able to separate the detected pulsars from the rest ofthe sample. Using archival catalogues we have been able to identify counterparts forthe majority of the sources in our sample. Combining this with our results from thetemporal analysis of the Chandra data and archival optical data, the X-ray spectralanalysis, and by determining the X-ray to optical ﬂux ratios we present preliminaryclassiﬁcations for the sources. In addition to the four detected pulsars, our sampleincludes two candidate foreground stars, 12 probable active galactic nuclei, and ﬁveunclassiﬁed sources.Keywords: X-rays:binaries– stars:emission-line,Be – (galaxies:)MagellanicClouds
Characterizing XML Twig Queries with Examples

Typically, a (Boolean) query is a finite formula that defines a possibly infinite set of database instances that satisfy it (positive examples), and implicitly, the set of instances that do not satisfy the query (negative examples). We investigate the following natural question: for a given class of queries, is it possible to characterize every query with a finite set of positive and negative examples that no other query is consistent with. We study this question for twig queries and XML databases. We show that while twig queries are characterizable, they generally require exponential sets of examples. Consequently, we focus on a practical subclass of anchored twig queries and show that not only are they characterizable but also with polynomially-sized sets of examples. This result is obtained with the use of generalization operations on twig queries, whose application to an anchored twig query yields a properly contained and minimally different query. Our results illustrate further interesting and strong connections between the structure and the semantics of anchored twig queries that the class of arbitrary twig queries does not enjoy. Finally, we show that the class of unions of twig queries is not characterizable.
A characterization of the performance of MEMS vibratory gyroscope in different fields

Compared with traditional spinning disk or wheel mechanical gyroscopes, MEMS gyroscopes are lighter in weight, smaller in dimension, lower in cost and energy saving. MEMS gyroscopes have been important components in consumer electronics, automotive electronics, etc. MEMS gyroscope is a rotation sensing component, and that means working in dynamic environment where acceleration, temperature and electromagnetic field may vary by time. It's necessary for us to know how much influence the environmental conditions have on the performance of MEMS gyroscopes. Knowing that characterization helps engineers to calibrate MEMS gyroscopes and get better outputs. The environmental conditions in this paper are magnetostatic field, electrostatic field and alternating magnetic field. The MEMS gyroscope's output noise is highly dependent on the number of sampling data as well as the duration of the measurement. To reduce random error, the angular rate to be analyzed is an average of 60 to 90 seconds of the initial output. By long term static test in field-free environment and comparison with external field applied test, it is possible to characterize the noise feature of MEMS gyroscopes' output. Comb structures in the MEMS gyroscope are driven by electrostatic force. The electromagnetic field will distract the motion parts from the ideal direction of motion, which leads to output deviation [1]. Three types of electromagnetic field are concerned in this article, which are electrostatic field, magnetostatic field and alternating magnetic field. The electric field is generated by a parallel plate capacitor with its zero potential reference connected to the GND pin of the MEMS gyroscope. The magnetostatic field is generated by magnets. The alternating magnetic field is generated by a coil. The output deviation under magnetostatic filed is within 0.6 degree/s, and there's obvious linear relationship between magnetic induction intensity. The output deviation under electrostatic filed is within 0.13 degree/s, which is smaller than that of under magnetostatic filed, and is irregular. An output deviation peak appears nearby the resonance frequency of the gyroscope. Characterization of the behavior of the MEMS gyroscope in such filed environments is an essential process for ensuring their reliable use and is helpful for acquiring more accurate results.
Surface- and Tip-Enhanced Raman Spectroscopy in Catalysis

Surface- and tip-enhanced Raman spectroscopy (SERS and TERS) techniques exhibit highly localized chemical sensitivity, making them ideal for studying chemical reactions, including processes at catalytic surfaces. Catalyst structures, adsorbates, and reaction intermediates can be observed in low quantities at hot spots where electromagnetic fields are the strongest, providing ample opportunities to elucidate reaction mechanisms. Moreover, under ideal measurement conditions, it can even be used to trigger chemical reactions. However, factors such as substrate instability and insufficient signal enhancement still limit the applicability of SERS and TERS in the field of catalysis. By the use of sophisticated colloidal synthesis methods and advanced techniques, such as shell-isolated nanoparticle-enhanced Raman spectroscopy, these challenges could be overcome.
Improving pose manifold and virtual images using bidirectional neural networks in face recognition using single image per person

In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a single stage, but via two bottom-up and top-down phases and the recognition results of first stage is used for model adaptation. We have applied this novel adapting model in combination with clustering person and pose information technique to separate person and pose information and to estimate corresponding manifolds. To increase the number of training samples in the classifier neural network, virtual views of frontal images in the test dataset are synthesized using estimated manifolds. Training classifier network via virtual images obtained from bidirectional network, gives an accuracy rate of 86.36% on the test dataset which shows 15.46% improvement in accuracy of face recognition compared to training classifier with only frontal view images.
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition

Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.
Building and combining document and music spaces for music query-by-webpage system

Building and combining document and music spaces of songs are discussed for a new music recommendation application, which uses commonly read texts such as Web log as query input. The most important application of this flexible recommendation system is its music query-by-Webpage, from which a song that appropriately matches Webpage is automatically played. The key idea of the proposed system is to train a linear transformation between document and music spaces so that query documents can be mapped onto a music space in which similarities based on acoustic characteristics is represented. The basic system has been trained using 2,650 pairs of song and review texts. Through experimental evaluations, we show the effectiveness of the system, which is three times better than the previous system. Web text as a training corpus and a bigram representation for the document vector are also investigated for the purpose of improving the system, and their effectiveness is also confirmed.
A General Unified Niche-Assembly / Dispersal-Assembly Theory of Forest Species Biodiversity

A generalised niche-assembly/dispersal-assembly theory is outlined. The aim is an explanatory theory of island/sample species abundance curves, of species-area curves, and of island biogeography phenomena. A niche space is defined over a spatial domain in terms of, for example, a stratification obtained from an environmental variable defined over that domain. Within each niche a neutral individual behaviour model is adopted (i.e. species-independent). Individual mortality is random and replacement is stochastic, depending on local species density per niche. A general formulation of a spatial model of stochastic inter-niche (seed) dispersal is presented; a Markovian inter-niche dispersal probability matrix is introduced. By stages, a simple spatially symmetric model of dispersal is defined which is neutral within niches, but not inter-niche neutral. Finally, this may be reduced to a distance-independent niche-dispersal (1 order) model which is essentially a simplest “truly unified model” (Hubbell (2001, p319) which generalises Hubbell’s “unified theory” (the 0 order model).
On predicted mean vote optimization in building climate control

Low energy buildings have been attracting much attention lately. Most of the research is focused on the building construction or alternative energy sources. Recently, there has been an intense research in the area of Model Predictive Control (MPC) for buildings. The main principle of such a controller is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, the thermal comfort is represented by a static range for the operative temperature according to the international standards. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index, however, is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, compare the control performance both to the conventional and predictive control strategies, point out that the proposed optimal control problem formulation shifts the savings potential of classical MPC by additional 11% and finally, the quality of the fulfillment of the thermal comfort will be addressed.
The Conquest of U.S. Inflation: Its Implications for the Fisher Hypothesis and the Term Structure of Nominal Interest Rates∗

A regime shift in the inflation process is the collective outcome of shifts in individual agents’ rational expectations resulting from an important policy initiative. Taking this view, we find decisive evidence of a shift in the inflation process during the Volcker experiment reflecting the Federal Reserve’s enforcement of an anti-inflationary interest rate policy consistent with the Taylor (1993) rule. Subsequent to the regime shift, inflation and real interest rates move together, especially in the long run, contradicting both the Fisher hypothesis and the presence of Mundell-Tobin effects. Contrary to conventional wisdom, today’s yield curve is not informative about expected inflation and yields now command a significant inflation risk premium suggesting yet another violation of the Fisher hypothesis. ∗This is a significant revision of our earlier manuscript, “Evolving Inflation Dynamics, Monetary Policy, and The Fisher Hypothesis. ” We would like to thank Cliff Ball, Anton Braun, Chip Chappell, Wayne Ferson, Scott Harrington, Ted Moore, Lee Ohanian, Richard Roll and Rossen Valkanov for helpful comments and suggestions on the earlier draft. All errors are our own. †The Moore School of Business, University of South Carolina, 1705 College Street, Columbia, SC 29208. Phone: 803-777-4927. Email: shingo.goto@moore.sc. edu. ‡The Anderson School at UCLA. 110 Westwood Plaza, Los Angeles, CA 90095-1481. Phone: 310-825-4059. Email: walter.n. torous@anderson.ucla. edu.
Representing Multidimensional Trees

This paper develops a formal definition of multidimensional trees as abstract structures in “left-child, right-sibling” form. After developing this abstract definition, we show how it can be directly implemented as an ADT suitable for use in parsing applications. Additionally, we show how, when viewed in a slightly different way, our definition yields a flat form suitable for serialized input and output.
Gastrointestinal transport of calcium and glucose in lactating ewes

During lactation, mineral and nutrient requirements increase dramatically, particularly those for Ca and glucose. In contrast to monogastric species, in ruminants, it is rather unclear to which extend this physiological change due to increased demand for milk production is accompanied by functional adaptations of the gastrointestinal tract (GIT). Therefore, we investigated potential modulations of Ca and glucose transport mechanisms in the GIT of lactating and dried‐off sheep. Ussing‐chamber technique was applied to determine the ruminal and jejunal Ca flux rates. In the jejunum, electrophysiological properties in response to glucose were recorded. Jejunal brush‐border membrane vesicles (BBMV) served to characterize glucose uptake via sodium‐linked glucose transporter 1 (SGLT1), and RNA and protein expression levels of Ca and glucose transporting systems were determined. Ruminal Ca flux rate data showed a trend for higher absorption in lactating sheep. In the jejunum, small Ca absorption could only be observed in lactating ewes. From the results, it may be assumed that lactating ewes compensate for the Ca loss by increasing bone mobilization rather than by increasing supply through absorption from the GIT. Presence of SGLT1 in the jejunum of both groups was shown by RNA and protein identification, but glucose uptake into BBMV could only be detected in lactating sheep. This, however, could not be attributed to electrogenic glucose absorption in lactating sheep under Ussing‐chamber conditions, providing evidence that changes in jejunal glucose uptake may include additional factors, that is, posttranslational modifications such as phosphorylation.
Semi-supervised Kernel-Based Temporal Clustering

In this paper, we adapt two existing methods to perform semi-supervised temporal clustering: Aligned Cluster Analysis (ACA), a temporal clustering algorithm, and Constrained Spectral Clustering, a semi-supervised clustering algorithm. In the first method, we add side information in the form of pair wise constraints to its objective function, and in the second, we add a temporal search to its framework. We also extend both methods by propagating the constraints throughout the whole similarity matrix. In order to validate the advantage of the proposed semi-supervised methods to temporal clustering, we evaluate them in comparison to their original versions as well as another semi-supervised temporal cluster on three temporal datasets. The results show that the proposed methods are competitive and provide good improvement over the unsupervised approaches.
Estimating the Form of Adjustment Costs with Data on Heterogeneous Capital Goods

This paper estimates the form of the micro-level marginal costs of adjusting capital using detailed data on investment decisions in the US airline industry. The data include the capital stock, investment, retirement, market values, operating costs, and utilization rates of 16 different types of capital goods for each airline. These data on heterogeneous capital goods allows us to estimate the desired stock of capital for each type of plane while controlling for unobserved changes in airline profitability. The results show significant evidence of non-convex adjustment costs. The adjustment costs seem to combine a region of inaction for capital investment with approximately quadratic adjustment costs conditional on positive or negative investment. The adjustment costs for utilization show similar non-convexities but with smaller adjustment costs. Aggregating to the firm level or using accounting data obscures the non-convexities and biases the estimated adjustment costs upward. We would like to thank Andrew Abel, Jason Abreveya, Daron Acemoglu, Susanto Basu, Ricardo Caballero, Steve Davis, Daniel Hamermesh, Wei Jiang, Dimitrios Kavvathas, Anil Kayshap, John Leahy, and Matthew Shapiro for helpful comments. Austan Goolsbee would
The effect of experimental glaucoma and optic nerve transection on amacrine cells in the rat retina.

PURPOSE To detect alterations in amacrine cells associated with retinal ganglion cell (RGC) depletion caused by experimental optic nerve transection and glaucoma. METHODS Intraocular pressure (IOP) was elevated unilaterally in 18 rats by translimbal trabecular laser treatment, and eyes were studied at 1 (n = 6), 2 (n = 5), and 3 (n = 7) months. Complete optic nerve transection was performed unilaterally in nine rats with survival for 1 (n = 4) and 3 (n = 5) months. Serial cryosections (five per eye) were immunohistochemically labeled with rabbit anti-gamma-aminobutyric acid (GABA) and anti-glycine antibodies. Cells in the ganglion cell and inner nuclear layers that labeled for GABA or glycine were counted in a masked fashion under bright-field microscopy. Additional labeling with other RGC and amacrine antigens was also performed. RGC loss was quantified by axon counts. RESULTS Amacrine cells identified by GABA and glycine labeling were not significantly affected by experimental glaucoma, with a mean decrease of 15% compared with bilaterally untreated control cells (557 +/- 186 neurons/mm [glaucoma] versus 653.9 +/- 114.4 neurons/mm [control] of retina; P = 0.15, t-test). There was no significant trend for amacrine cell counts to be lower in eyes with fewer RGCs (r = -0.39, P = 0.11). By contrast, there was highly significant loss of GABA and glycine staining 3 months after nerve transection, both in the treated and the fellow eyes (P < 0.0001, t-test). However, there was a substantial number of remaining amacrine cells in transected retinas, as indicated by labeling for calretinin and calbindin. CONCLUSIONS Experimental glaucoma causes minimal change in amacrine cells and their expression of neurotransmitters. After nerve transection, neurotransmitter presence declines, but many amacrine cell bodies remain. Differences among optic nerve injury models, as well as effects on "untreated" fellow eyes, should be recognized.
AB-MAC: A TDMA-based adaptive beaconing MAC protocol for body sensor networks

We present an Adaptive Beaconing Medium Access Control (AB-MAC) protocol based on time division multiple access (TDMA) in order to provide healthcare services. The purpose of our protocol is to gain not only energy-efficiency but also provide low delivery latency when both periodic data and event-driven data are present. In order to satisfy these requirements, we propose standby slots that are deployed during each beacon interval. The standby slots are able to identify unscheduled data with low delivery latency. An adaptive beacon is then provided that quickly reschedules the time slots. Furthermore, the AB-MAC asymmetrically assigns energy consumption to a hub instead of the sensor nodes when possible in order to reduce node's energy waste. In this paper, we evaluate IEEE 802.15. 4 and AB-MAC in energy consumption and delivery latency. The simulation results indicate that the AB-MAC outperforms than IEEE 802.15. 4 in BSNs.
2D shape matching based on B-spline curves and Dynamic Programming

In this paper, we propose an approach for two-dimensional shape representation and matching using the B-spline modelling and Dynamic Programming (DP), which is robust with respect to affine transformations such as translation, rotation, scale change and some distortions. Boundary shape is first splited into distinct parts based on the curvature. Curvature points are critical attributes for shape description, allowing the concave and convex parts of an object representation, which are obtained by the polygonal approximation algorithm in our approach. After that each part is approximated by a normalized B-spline curve using some global features including the arc length, the centroid of the shape and moments. Finally, matching and retrieval of similar shapes are obtained using a similarity measure defined on their normalized curves with Dynamic Programming. Dynamic programming not only recovers the best matching, but also identifies the most similar boundary parts. The experimental results on some benchmark databases validate the proposed approach.
Convolutional Neural Networks for Medical Clustering

A major challenge for Medical Image Retrieval (MIR) is the discovery of relationships between low-level image features (inten- sity, gradient, texture, etc.) and high-level semantics such as modal- ity, anatomy or pathology. Convolutional Neural Networks (CNNs) have been shown to have an inherent ability to automatically extract hier- archical representations from raw data. Their successful application in a variety of generalised imaging tasks suggests great potential for MIR. However, a major hurdle to their deployment in the medical domain is the relative lack of robust training corpora when compared to general imaging benchmarks such as ImageNET and CIFAR. In this paper, we present the adaptation of CNNs to the medical clustering task at Image- CLEF 2015.