Miracle-dz/UniFAR-FTUs huggingface dataset:
The Verlinde formula for PGLp

The Verlinde formula expresses the number of linearly independent conformal blocks in any rational conformal field theory. I am concerned here with a quite particular case, the Wess-Zumino-Witten model associated to a complex semi-simple group 2 G . In this case the space of conformal blocks can be interpreted as the space of holomorphic sections of a line bundle on a particular projective variety, the moduli space MG of holomorphic G-bundles on the given Riemann surface. The fact that the dimension of this space of sections can be explicitly computed is of great interest for mathematicians, and a number of rigorous proofs of that formula (usually called by mathematicians, somewhat incorrectly, the “Verlinde formula”) have been recently given (see e.g. [F], [B-L], [L-S]). These proofs deal only with simply-connected groups. In this paper we treat the case of the projective group PGLr when r is prime. Our approach is to relate to the case of SLr , using standard algebro-geometric methods. The components MdPGLr (0 ≤ d < r) of the moduli space MPGLr can be identified with the quotients Mr/Jr , where M d r is the moduli space of vector bundles on X of rank r and fixed determinant of degree d , and Jr the finite group of holomorphic line bundles α on X such that α⊗r is trivial. The space we are looking for is the space of Jr -invariant global sections of a line bundle L on Mr ; its dimension can be expressed in terms of the character of the representation of Jr on H(Mr ,L) . This is given by the Lefschetz trace formula, with a subtlety for d = 0 , since Mr is not smooth. The key point (already used in [N-R]) which makes the computation quite easy is that the fixed point set of any non-zero element of Jr is an abelian variety – this is where the assumption on the group is essential. Extending the method to other cases would require a Chern classes computation on the moduli space MH for some semi-simple subgroups H of G ; this may be feasible, but goes far beyond the scope of the present paper. Note that the case of M1PGL2 has been previously worked out in [P] (with an unfortunate misprint in the formula).
Object Class Recognition with Many Local Features

In this paper we present a method to recognize an object class by learning a statistical model of the class. The probabilistic model decomposes the appearance of an object class into a set of local parts and models the appearance, relative location, co-occurrence, and scale of these parts. However, in many object classification approaches that use local features, learning the parameters is exponential in the number of parts because of the problem of matching local features in the image to parts in the model. In this paper we present a learning method that overcomes this difficulty by adding new parts to the model incrementally, using the Maximum-Likelihood framework. When we add a part to the model, a set of candidate parts are selected and the part that increases the likelihood of the data the most is added to the model. Once this part is added to the model, the parameters for all parts up to this point are updated using EM. The learning and recognition in this approach are translation and scale invariant, robust to background clutter, and has less restriction on the number of parts in the model. The validity of the approach is demonstrated on a real world dataset, where the approach is competitive with others, and where the learning for a rich model is much faster than previous approaches.
Child Labor and Schooling Decisions in Ghana

In this paper we investigate the choices involved in the tradeoff between a childs labor outside of household work and their schooling hours from a sample of data from Ghana in the late 1980s. While households are interviewed only once during the survey, the data were collected over an 11 month period for both the Northern and Southern regions of Ghana. Northern and Southern Ghana have rather distinct long-run rainfall patterns over the year. These two facts allow us to derive an identiÞcation strategy with good power for the child labor decisions made by households. Essentially, we use the month by region variation in child labor intensities as a source of exogenous variation in child labor, while controlling for secular month and region variation in child labor intensities. This approach uses the monthly span of the data to construct an identiÞcation strategy that uses the synthetic panel approach of Deaton (1985) to average out the unobserved preference and short-run income ßuctuations across month by region cells. We also consider a reÞned version of this strategy, in using both the realized rainfall (including sets of month and region dummies), as well as realized rainfall deviated from its long-term month by region means, as direct rainfall shock measures. These differing ways of using the rainfall data extract the different behavioral responses to short run and long run rainfall, and 1Boozer is at the Department of Economics and the Economic Growth Center, Yale University. Email: michael.boozer@yale.edu. Suri is at the Department of Economics, Yale University. Email: tavneet.suri@yale.edu. We are thankful to the Ghana Statistical Service for permission to use their data and also to David Lister at the University of East Anglia, UK for access to the rainfall data. We thank Markus Goldstein, Chris Udry and the Development Lunch participants at Yale for helpful comments. thus to a degree income patterns. We interpret the strategy that uses simply month by region variations as being indicative of an average of the long and short run effects and that using the rainfall shock as indicative of the pure short run effect. We Þnd that both strategies give statistically similar impacts: an hour of child labor reduces school attendance by approximately 0.38 hours. Surprisingly, this effect is actually larger than the OLS estimate of the effect, 0.21. Also, these results imply that income, or poverty, may not be as important in determining child labor. A direct analysis of this effect indeed provides no evidence of heterogeneity of this substitution effect by our observed income measure.
Prevalence of silicosis at death in underground coal miners.

This study was initiated by the National Institute for Occupational Safety and Health (NIOSH) and the Bureau of Mines (BOM) to determine the prevalence and pathological features of silicosis in coal miners. The population base was 3,365 autopsied underground miners whose records were submitted to the U.S. National Coal Workers' Autopsy Study between 1971 and 1980. This program is voluntary and covers an estimated 10% of all coal workers who die. The mean age at death of the population was 62 years, of whom 75% were current or ex-smokers at the time of death. The average work tenure was 26 years. Lung sections from all cases were reviewed and the type and severity of pneumoconiosis documented. These findings were correlated with years of mining, job history, and geographic location of mine. Classical silicotic nodules were found in 12.5% of the population. There was a significant relationship between length of underground mining and prevalence and severity of silicosis consistent with a dose-response effect. The study also showed that job category and geographic location of the mine were important determinants of silicosis prevalence and that silicosis was strongly associated with higher categories of coal workers' pneumoconiosis.
[Fe II] and H2 Filaments in the Supernova Remnant G11.2–0.3: Supernova Ejecta and Presupernova Circumstellar Wind

We present the results of near-infrared imaging and spectroscopic observations of the young core-collapse supernova remnant (SNR) G11.2-0.3. In the [Fe II] 1.644 μm image, we first discover long, clumpy [Fe II] filaments within the radio shell of the SNR, together with some faint, knotty features in the interior of the remnant. The filaments are thick and roughly symmetric with respect to the northeast-southwest elongation axis of the central pulsar wind nebula. We have detected several [Fe II] lines and a H I Brγ line toward the peak position of the bright southeastern [Fe II] filament. The derived extinction is large (AV = 13 mag), and it is the brightest [Fe II] 1.644 μm filament detected toward SNRs to date. By analyzing two [Fe II] 1.644 μm images obtained 2.2 yr apart, we detect a proper motion corresponding to an expansion rate of 0.035′′ ± 0.013′′ yr-1 (830 ± 310 km s-1). In addition to the [Fe II] features, we also discover two small H2 2.122 μm filaments. One is bright and along the southeastern boundary of the radio shell, while the other is faint and just outside its northeastern boundary. We have detected the H2 (2-1) S(3) line toward the former filament and derive an excitation temperature of 2100 K. We suggest that the H2 filaments are dense clumps in a presupernova circumstellar wind swept up by the SNR shock, while the [Fe II] filaments are probably composed of both shocked wind material and shocked supernova (SN) ejecta. The distribution of [Fe II] filaments may indicate that the SN explosion in G11.2-0.3 was asymmetric, as in Cassiopeia A. Our results support the suggestion that G11.2-0.3 is a remnant of a SN IIL/b interacting with a dense red supergiant wind.
Spectral Element Model Updating for Damage Identification Using Clonal Selection Algorithm

A spectral element model updating procedure is presented to identify damage in a structure using Guided wave propagation results. Two damage spectral elements (DSE1 and DSE2) are developed to model the local (cracks in reinforcement bar) and global (debonding between reinforcement bar and concrete) damage in one-dimensional homogeneous and composite waveguide, respectively. Transfer matrix method is adopted to assemble the stiffness matrix of multiple spectral elements. In order to solve the inverse problem, clonal selection algorithm is used for the optimization calculations. Two displacement-based functions and two frequency-based functions are used as objective functions in this study. Numerical simulations of wave propagation in a bare steel bar and in a reinforcement bar without and with various assumed damage scenarios are carried out. Numerically simulated data are then used to identify local and global damage of the steel rebar and the concrete-steel interface using the proposed method. Results show that local damage is easy to be identified by using any considered objective function with the proposed method while only using the wavelet energy-based objective function gives reliable identification of global damage. The method is then extended to identify multiple damages in a structure. To further verify the proposed method, experiments of wave propagation in a rectangular steel bar before and after damage are conducted. The proposed method is used to update the structural model for damage identification. The results demonstrate the capability of the proposed method in identifying cracks in steel bars based on measured wave propagation data.
Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.
The priori protein gene: a role in mouse embryogenesis?

Summary The neural membrane glycoprotein PrP (prion protein) has a key role in the development of scrapie and related neurodegenerative diseases. During pathogenesis, PrP accumulates in and around cells of the brain from which it can be isolated in a disease-specific, protease-resistant form. Although the involvement of PrP in the pathology of these diseases has long been known, the normal function of PrP remains unknown. Previous studies have shown that the PrP gene is expressed tissue specifically in adult animals, the highest levels in the brain, with intermediate levels in heart and lung and low levels in spleen. Prenatally, PrP mRNA has been detected in the brain of rat and hamster just prior to birth. In this study we have examined the expression of the PrP gene during mouse embryonic development by in situ hybridisation and observed dramatic regional and temporal gene expression in the embryo. Transcripts were detected in developing brain and spinal cord by 13.5 days. In addition, PrP gene expression was detected in the peripheral nervous system, in ganglia and nerve trunks of the sympathetic nervous system and neural cell populations of sensory organs. Expression of the PrP gene was not limited to neuronal cells, but was also detected in specific non-neuronal cell populations of the 13.5 and 16.5 day embryos and in extra-embryonic tissues from 6.5 days. This cell-specific expression suggests a pleiotropic role for PrP during development.
Image Capture: Modelling and Calibration of Sensor Responses and their Synthesis from Multispectral Images

digital cameras, hyperspectral, multispectral, sensors, simulation, modelling, calibration This paper describes (a) models for digital cameras, (b) the calibration of the spectral response of a camera and (c) the performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to light intensity over most of its operating range. The input to the simulator is a set of narrow-band images of the scene taken with a custom-designed hyperspectral camera system [1]. The parameters for the simulator are: the number of sensor classes; the sensor spectral sensitivities; the noise statistics and number of quantization levels for each sensor class; the spatial arrangement of the sensors; and the exposure duration. The output of the simulator is the raw image data that would have been acquired by the simulated image capture device.
Performance evaluation of quality degradation indicators on super-wideband speech signals

This paper presents the performance of quality degradation indicators to be used in the context of super-wideband (50-14000 Hz) telephony. After an overview of these indicators, two analyses are undertaken: the first one considers conditions containing a single degradation and the second one considers conditions comprising several degradations at the same time, reflecting more realistic communications. This study highlights the major role of some indicators, and particularly those designed for quantifying the perceived additive noise, the frequency-response distortion and also the speech level. We show that these indicators are robust to multiple types of degradations and reveal relevant for advanced diagnosis of telecommunication systems.
Semantic Role Recognition Using Kernels on Weighted Marked Ordered Labeled Trees

We present a method for recognizing semantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its importance. We improve the accuracy by giving more weights on subtrees that contain the predicate and the argument nodes with this ability. Although Kazama and Torisawa (2005) presented fast training with tree kernels, the slow classification during runtime remained to be solved. In this paper, we give a solution that uses an efficient DP updating procedure applicable in argument recognition. We demonstrate that the WMOLT kernel improves the accuracy, and our speed-up method makes the recognition more than 40 times faster than the naive classification.
Truck/Pavement/Economic Modeling and In-Situ Field Test Data Analysis Applications ― Volume 3: Stiffness and Modulus Estimation for Different Soil Types Using FWD Deflection Criteria

In current U.S. practice, quality of subgrade in a pavement system is commonly evaluated using the degree of compaction in the field and comparing with the laboratory determined maximum dry density from Standard/Modified Proctor tests [American Association of State Highway and Transportation Officials (AASHTO T-99/T-180)] to confirm the degree of compaction. However density is inherently an indirect measurement of the subgrade quality. This report studies the use of nondestructive testing (NDT) devices, such as the Falling Weight Deflectometer (FWD), to directly and quickly measure subgrade and base stiffness and/or modulus. This study’s main objective was assessment of material-specific stiffness and soil modulus of unbound granular soil materials using deflection data from 146 sections in ten states in the nationwide pavement database. In some cases, insufficient or unavailable in-situ soil data were supplemented with laboratory measurements. Calculation of stiffness and soil modulus was based on the linear elastic theory of granular soil material. To achieve high reliability of output results, establishment of the data screening procedures was another major concern in this study. Also, correlations between major engineering properties and stiffness or modulus of unbound granular materials were performed for validation of output results. Statistics for modulus and stiffness of 11 types of subgrade soils are presented, including mean with 95% confidence interval, median, standard deviation, and interquartile range. Similar statistics are given for bases made from aggregate or aggregate mixed with soil. The subgrade modulus is then correlated with various engineering properties, including moisture content, maximum dry density, plasticity index, and grain size distribution. Calculated mean stiffness and modulus values for base materials and coarse-grained soils agreed with typical previously published values, while those for fine-grained soils did not. For coarse-grained soils, modulus increased as moisture decreased, except below a threshold below which soil was too dry and modulus decreased. Well-graded soil had a higher modulus. The correlation analysis showed that for fine-grained soils, modulus was sensitive to the plasticity index, the weighted plasticity index, and number of fines. Stiffness and modulus for coarse-grained soils showed a good agreement and reliable relationship with other soil properties. Measured stiffness and modulus values in the pavement database exceeded the European standards, and all soils had been approved for construction. For practical purposes, subgrade stiffness and soil modulus values at the 25th percentile could be considered as the minimum limiting criteria for AASHTO soil types A-1, A-1-a, A-1-b, A-2-4, and A-2-6. Similarly, base stiffness and base modulus values at the 25% percentile appear to be reasonable as a minimum criterion. A table of values is given.
Using developers' feedback to improve code smell detection

Several studies are focused on the study of code smells and many detection techniques have been proposed. In this scenario, the use of rules involving software-metrics has been widely used in refactoring tools as a mechanism to detect code smells automatically. However, actual approaches present two unsatisfactory aspects: they present a low agreement in its results and, they do not consider the developers' feedback. In this way, these approaches detect smells that are not relevant to the developers. In order to solve the above mentioned unsatisfactory aspects in the state-of the-art of code smells detection, we propose the Smell Platform able to recognize code smells more relevant to developers by using its feedback. In this paper we present how such platform is able to detect four well known code smells. Finally, we evaluate the Smell Platform comparing its results with traditional detection techniques.
A physics-based approach for interactive manipulation of graph visualizations

This paper presents an interactive physics-based technique for the exploration and dynamic reorganization of graph layouts that takes into account semantic properties which the user might need to emphasize. Many techniques have been proposed that take a graph as input and produce a visualization solely based on its topology, seldom ever relying on the semantic attributes of nodes and edges. These automatic topology-based algorithms might generate aesthetically interesting layouts, but they neglect information that might be important for the user. Among these are the force-directed or energy minimization algorithms, which use physics analogies to produce satisfactory layouts. They consist of applying forces on the nodes, which move until the physical system enters a state of mechanical equilibrium. We propose an extension of this metaphor to include tools for the interactive manipulation of such layouts. These tools are comprised of magnets, which attract nodes with user-specified criteria to the regions surrounding the magnets. Magnets can be nested and also used to intuitively perform set operations such as union and intersection, becoming thus an intuitive visual tool for sorting through the datasets. To evaluate the technique we discuss how they can be used to perform common graph visualization tasks.
Privacy-Preserving Collaborative Spectrum Sensing With Multiple Service Providers

In cognitive radio networks (CRNs), collaborative sensing has been considered an attractive means to improving spectrum sensing performance. However, privacy issues arise when multiple service providers (SPs) collaborate on learning the spectrum availabilities. Specifically, sharing sensing data may enable malicious SPs or secondary users (SUs) to geolocate an SU using existing localization techniques. These malicious entities could be untrusted SPs/SUs or external attackers that compromise SPs/SUs. To incentivize SUs to contribute their sensing data, the privacy of each SU should be guaranteed. In this paper, we propose a privacy preservation framework called PrimCos for multi-SP collaborative sensing, which addresses several competing challenges not yet considered in the literature, that is, being compatible with general collaborative sensing schemes, providing privacy guarantee for each SU and ensuring worst case privacy protection under collusion. Both analytical and numerical results show that the proposed framework provides privacy protection for each SU with controllable impact on the sensing performance under different types of attacks.
Flow behavior of soda-lime glass at high pressures and high shear rates

Results are reported for a plate impact investigation of the flow behavior of soda-lime glass. A 5 μm thick layer of soda-lime glass is sandwiched between two hard, elastic Hampden steel plates and impacted by another steel plate. The impact is skewed with respect to the direction of travel of the flyer plate in order to generate both compression and shear waves in the target. The dynamic response of the glass to this combined loading is measured using laser interferometry. The measured wave profiles are used to develop a micromechanism-based deformation model for the glass. It is also hoped that the investigation will contribute to an understanding of so-called failure waves in glasses through improved understanding of the dynamic shearing resistance of glasses.