Miracle-dz/UniFAR-FTUs huggingface dataset:
The geometry of numbers over algebraic number fields

1. The Geometry of Numbers was founded by Minkowski in order to attack certain arithmetical problems, and is normally concerned with lattices over the rational integers. Minkowski himself, however, also treated a special problem over complex quadratic number fields [5], and a number of writers have since followed him. They were largely concerned with those fields which have class-number h= 1; and this simplification removes many of the characteristic features of the more general case. Hermann Weyl [10] gave a thorough account of the extension of Minkowski's theory of the reduction of quadratic forms to "gauge functions" over general algebraic number fields and quaternion algebras, and we shall follow part of his developments, though our definition of a lattice is quite different. The desirability of extending the Geometry of Numbers to general algebraic number fields was emphasized by Mahler in a seminar at Princeton. In this paper we shall carry out this program, extending the fundamental results of Mahler [4] to our more general case and applying them to specific problems. Certain new ideas are necessary, but much of this paper must be regarded as expository. In particular, when the proof of a result is essentially analogous to that for the real case we have merely given a reference to the latter. 2. Let K be an algebraic extension of the rationals of degree m. We regard K as an algebra over the rationals, which we can extend to an algebra K* over the reals. It is well known that K* is commutative and semi-simple (being in fact isomorphic to the direct sum of r copies of the reals and s copies of the complex numbers, where r and 2s are the number of real and complex conjugates of K); and the integers of K* are just those of K. We now define the n-dimensional space Kn over K as being the set of ordered n-tuples of elements in K*. Any tCK* is of the form t=x1i1+ * * * +xwmU, where the x, are real and co,, * * *, com is an integral basis for K; and hence there is a natural map of Kn onto Rmn in which each component t is mapped onto m of the components of the point in Rmn, namely xi, x, m as above. We can define a metric and a measure in Kn by means of those in Rmn, with the above map, and so Kn is a locally compact complete metric space.
A study on significant sources of the "bournout syndrome" in workers at occupational centres for the mentally disabled

En este estudio se analiza el sindrome de quemarse por el trabajo desde la perspectiva de los modelos interaccionistas de estres laboral. Como variables del entorno se han considerado el conflicto de rol, la ambiguedad de rol, y del apoyo social en el trabajo desde el supervisor y desde los companeros. Como variables de personalidad se han considerado los sentimientos de autoconfianza de los sujetos. La muestra del estudio esta compuesta por 95 trabajadores de centros ocupacionales para discapacitados psiquicos. Los analisis de regresion han presentado que la ambiguedad de rol junto con los sentimientos de autoconfianza son predictores significativos de la realizacion personal en el trabajo, mientras que el conflicto de rol ha resultado un predictor significativo de agotamiento emocional. Se han obtenido efectos significativos de modulacion de la autoconfianza para la relacion entre la ambiguedad de rol y la realizacion personal en el trabajo.
Incoherent Discriminative Dictionary Learning for Speech Enhancement

Speech enhancement is one of the many challenging tasks in signal processing, especially in the case of nonstationary speech-like noise. In this paper a new incoherent discriminative dictionary learning algorithm is proposed to model both speech and noise, where the cost function accounts for both “source confusion” and “source distortion” errors, with a regularization term that penalizes the coherence between speech and noise sub-dictionaries. At the enhancement stage, we use sparse coding on the learnt dictionary to ﬁnd an estimate for both clean speech and noise amplitude spectrum. In the ﬁnal phase, the Wiener ﬁlter is used to reﬁne the clean speech estimate. Experiments on the Noizeus dataset, using two objective speech enhancement measures: frequency-weighted segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) demonstrate that the proposed algorithm outperforms other speech enhancement methods tested.
Consensus of Heterogeneous Multiagent Systems with Arbitrarily Bounded Communication Delay

This paper focuses on the consensus problem of high-order heterogeneous multiagent systems with arbitrarily bounded communication delays. Through the method of nonnegative matrices, we get a sufficient consensus condition for the systems with dynamically changing topology. The results of this paper show, even when there are arbitrarily bounded communication delays in the systems, all agents can reach a consensus no matter whether there are spanning trees for the corresponding communication graphs at any time.
Genus one GW invariants of quintic threefolds via MSP localization

The moduli stack of Mixed Spin P-fields (MSP) provides an effective algorithm to evaluate all genus Gromov-Witten invariants of quintic Calabi-Yau threefolds. This paper is to apply the algorithm in genus one case. We use the localization formula, the proposed algorithm in [CLLL1, CLLL2], and Zinger's packaging technique to compute the genus one Gromov-Witten invariants of quintic Calabi-Yau threefolds. New hypergeometric series identities are also discovered in the process.
PZT AND PNZT-BASED THIN FILM CAPACITORS AND TRANSMISSION LINES FOR MICROWAVE INTEGRATED CIRCUIT APPLICATIONS

Ferroelectric materials have superior dielectric properties but the processing conditions of thin ferroelectric films influence their dielectric properties and thus affect the performance of devices which employ them. A detailed characterization is carried out for lead zirconate titanate (PZT) and lead niobate zirconate titanate (PNZT) thin films in this work by employing planar-circuit structures. The films were applied to built microwave capacitors and co-planar waveguide transmission lines. S-parameter measurements were performed from 40 MHz to 20 GHz using wafer probes in conjunction with a vector network analyzer. The results show the loss tangent and relative permittivity of the films vary with frequency, with typical permittivity values of the order of 110 to 350 and 200 to 780 for PZT and PNZT, respectively, over the said frequency range. The investigation revealed the effect of dielectric polarization of the films over the broad frequency range. The transmission lines showed acceptable insertion losses of the order of 17 dB from 5 to 20 GHz for lines of length 100 μm and width 5 μm fabricated on PNZT films 1 μm thick. These figures demonstrate the feasibility of using thin ferroelectric films as a new substrate material for monolithic microwave integrated circuits (MMIC).
Matlab Wi-Fi simulations considering interferences in dense urban environments

In recent years, there has been a high growth in the number of mobile devices and the transit of global data, with a forecast of exponential growth. It has also been observed that this growth takes place in urban environments such as offices or apartment buildings where the deployment of WLAN networks and the IEEE 802.11 standard prevails. Those facts have consolidated the standard as a widely used technology and have generated the need for companies to understand and optimize the performance of WLANs. This thesis is part of a larger project, which consists in the creation of a measurement software tool for WLANs in urban environments (TUWIPA©) as a graphical user interface developed in MATLAB. Urban environments constitute a hostile environment for the operation of Wi-Fi networks in which, among several drawback, stands out the broad spectrum of interference sources which can highly condition the provided service. The thesis is mainly focused on complementing the tool with the characterization and implementation of different interference sources with diverse properties, such as microwaves or neighboring Wi-Fi networks. It is also proposed the implementation of additional free-choice features that can complement the program. The obtained results show that the sources of interference have been successfully characterized, making TUWIPA© a more complete and robust tool that can provide more precise and reliable numerical results.
Nanoformulation and antibiotic releasing property of cefotaxime nanoparticles

The objective of this study was to design nano-antibiotic to enhance their release from biomaterial agents. Cefotaxime was used as a model antibiotic substance in this carrier system. These nanoparticles were preformulated using different concentrations of polycaprolactone (PCL) and poly (vinyl alcohol) as coating material and prepared using double emulsion solvent evaporation method. The physiochemical properties of cefotaxime nano-antibiotic (Cefo-NPs) stability were determined. Results showed that the encapsulation efficiency of nanoparticles increased with increase in polymer concentration. In addition, dynamic light scattering (DLS) and atomic force microscope (AFM) indicated that the particles size were in the range of 189 to 219 nm. The drug release profile of Cefo-NPs shows rapidly the release behaviour under acidic environment. And thus make it a promising tool for control bacterial infection. Key words : Polycaprolactone, poly (vinyl alcohol), cefotaxime, nanoparticle.
Learning Deep Policies for Physics-Based Manipulation in Clutter

Uncertainty in modeling real world physics makes transferring traditional open-loop motion planning techniques from simulation to the real world particularly challenging. Available closed-loop policy learning approaches, for physics-based manipulation tasks, typically either focus on single object manipulation, or rely on imitation learning, which inherently constrains task generalization and performance to the available demonstrations. In this work, we propose an approach to learn a policy for physics-based manipulation in clutter, which enables the robot to react to the uncertain dynamics of the real world. We start with presenting an imitation learning technique which compiles demonstrations from a sampling-based planner into an action-value function encoded as a deep neural network. We then use the learned action-value function to guide a look-ahead planner, giving us a control policy. Lastly, we propose to refine the deep action-value function through reinforcement learning, taking advantage of the look-ahead planner. We evaluate our approach in a physics-enabled simulation environment with artificially injected uncertainty, as well as in a real world task of manipulation in clutter.
THE ESCAPE FRACTION OF IONIZING RADIATION FROM GALAXIES

The escape of ionizing radiation from galaxies plays a critical role in the evolution of gas in galaxies, and the heating and ionization history of the intergalactic medium. We present semi-analytic calculations of the escape fraction of ionizing radiation for both hydrogen and helium from galaxies ranging from primordial systems to disk-type galaxies that are not heavily dust-obscured. We consider variations in the galaxy density profile, source type, location, and spectrum, and gas overdensity/distribution factors. For sufficiently hard first-light sources, the helium ionization fronts closely track or advance beyond that of hydrogen. Key new results in this work include calculations of the escape fractions for He i and He ii ionizing radiation, and the impact of partial ionization from X-rays from early active galactic nuclei or stellar clusters on the escape fractions from galaxy halos. When factoring in frequency-dependent effects, we find that X-rays play an important role in boosting the escape fractions for both hydrogen and helium, but especially for He ii. We briefly discuss the implications of these results for recent observations of the He ii reionization epoch at low redshifts, as well as the UV data and emission-line signatures from early galaxies anticipated from future satellite missions.
DeepPos: Deep Supervised Autoencoder Network for CSI Based Indoor Localization

The widespread mobile devices facilitated the emergence of many new applications and services. Among them are location-based services (LBS) that provide services based on user's location. Several techniques have been presented to enable LBS even in indoor environments where Global Positioning System (GPS) has low localization accuracy. These methods use some environment measurements (like Channel State Information (CSI) or Received Signal Strength (RSS)) for user localization. In this paper, we will use CSI and a novel deep learning algorithm to design a robust and efficient system for indoor localization. More precisely, we use supervised autoencoder (SAE) to model the environment using the data collected during the training phase. Then, during the testing phase, we use the trained model and estimate the coordinates of the unknown point by checking different possible labels. Unlike the previous fingerprinting approaches, in this work, we do not store the {CSI/RSS} of fingerprints and instead we model the environment only with a single SAE. The performance of the proposed scheme is then evaluated in two indoor environments and compared with that of similar approaches.
EXAMINING THE EFFECTS OF A HEALTHY RESTAURANT INTERVENTION ON CUSTOMERS' PURCHASES OF HEALTHIER FOOD OPTIONS IN LATINO FAMILY-OWNED RESTAURANTS

Shital Pandya Department of Applied Behavioral Science University of Kansas This study examined the effects of a Healthy Restaurant Intervention on customers’ purchases of healthier food options in Latino family-owned restaurants. As part of the context for this study, the Nutrition Committee of the Latino Health for All Coalition collaborated in a community-based participatory research approach to implement a Healthy Restaurant Award Program. The objective of the Healthy Restaurant program was to make healthier food options available and accessible in restaurants in low-income, predominantly Latino neighborhood. The Healthy Restaurant Intervention in this study consisted of placing small menu stickers for identified healthier food options and assuring that healthier food options were available by modifying recipes/servings in the two participating family-owned restaurants. The effects of the intervention were examined with one primary dependent variable: customers’ purchases of healthier food options. In addition, to assess the context of healthier food options available to customers, the study also measured modifications made by the restaurant owners in traditional recipes/menu items based on the Dietary Guidelines for Americans, 2010. Results showed that the Healthy Restaurant Intervention produced no discernible improvements in customers’ purchasing behaviors in either restaurant. Assessments showed that there were a number of menu modifications made by participating restaurant owners to assure healthier food options on their menus. The findings suggested that the Healthy Restaurant Intervention—consisting of small menu stickers and access to healthier food options—was not effective in increasing purchasing behaviors of customers visiting participating restaurants. Limitations are discussed in addition to strengths and implications for future research and practice.
A Collaborative System for Composing Music via Motion Using a Kinect Sensor and Skeletal Data

This paper describes MoveSynth, a performance system for two players, who interact with it and collaborate with each other in various ways, including full-body movements, arm postures and continuous gestures, to compose music in real time. The system uses a Kinect sensor, in order to track the performers’ positions, as well as their arm and hand movements. In the system’s current state, the musical parameters that the performers can inﬂuence include the pitch and the volume of the music, the timbre of the sound, as well as the time interval between successive notes. We extensively experimented using various classiﬁers in order to detect the one that gives the optimal results regarding the task of continuous gesture and arm posture recognition, accomplishing 92.11% for continuous gestures and 99.33% for arm postures, using an 1-NN classiﬁer with a condensed search space in both cases. Additionally, the qualitative results of the usability testing of the ﬁnal sys-tem, which was performed by 9 users, are encouraging and identify possible avenues for further exploration and improvement.
Union-Firm Bargaining Under Alternative Pay Schemes: Does Performance Related Pay Fair Better?

This paper compares and contrasts equilibrium outcomes under right-to-manage bargaining (RTM) and efficient bargaining (EB) corresponding to two alternative pay schemes, fixed wage vis-a-vis piece-rate. [WP No.8]. URL:[http://www.gipe. ac.in/pdfs/working%20papers/wp8.pdf].
A seismic ray-tracing method based on Fibonacci search

We design a fast ray tracing technique to simulate the response of seismic sources located at depth such as passive data due to fluid injection, hydraulic fracturing, volcanic tremors or earthquakes, with receivers located at the surface recording the displacement (and particle-velocity) field. The ray tracing is based on the Fibonacci search algorithm. We consider interfaces of arbitrary geometry and homogeneous layers defined by the seismic velocity, the quality factor and the mass density. Amplitude losses consider geometrical spreading, intrinsic attenuation (related to the quality factor) and transmission losses at the interfaces. The traces corresponding to time spikes are then convolved with a Ricker wavelet to obtain band-limited synthetic seismograms. We compare the results with full-wave seismograms computed with a direct algorithm based on the Fourier pseudospectral method. The ray tracing algorithm is much faster and from a practical point of view can be used as a forward modeling algorithm for the location of different sources of seismic energy.
Interactions between carbon and nitrogen dynamics in estimating net primary productivity for potential vegetation in North America

We use the terrestrial ecosystem model (TEM), a process-based model, to investigate how interactions between carbon (C) and nitrogen (N) dynamics affect predictions of net primary productivity (NPP) for potential vegetation in North America. Data on pool sizes and fluxes of C and N from intensively studied field sites are used to calibrate the model for each of 17 non-wetland vegetation types. We use information on climate, soils, and vegetation to make estimates for each of 11,299 non-wetland, 0.5° latitude × 0.5° longitude, grid cells in North America. The potential annual NPP and net N mineralization (NETNMIN) of North America are estimated to be 7.032 × 1015 g C yr−1 and 104.6 × 1012 g N yr−1, respectively. Both NPP and NETNMIN increase along gradients of increasing temperature and moisture in northern and temperate regions of the continent, respectively. Nitrogen limitation of productivity is weak in tropical forests, increasingly stronger in temperate and boreal forests, and very strong in tundra ecosystems. The degree to which productivity is limited by the availability of N also varies within ecosystems. Thus spatial resolution in estimating exchanges of C between the atmosphere and the terrestrial biosphere is improved by modeling the linkage between C and N dynamics. We also perform a factorial experiment with TEM on temperate mixed forest in North America to evaluate the importance of considering interactions between C and N dynamics in the response of NPP to an elevated temperature of 2°C. With the C cycle uncoupled from the N cycle, NPP decreases primarily because of higher plant respiration. However, with the C and N cycles coupled, NPP increases because productivity that is due to increased N availability more than offsets the higher costs of plant respiration. Thus, to investigate how global change will affect biosphere-atmosphere interactions, process-based models need to consider linkages between the C and N cycles.
Confidence-based iterative efficient large-scale stereo matching

Abstract In this study, we integrate confidence into efficient large-scale stereo (ELAS) matching to produce a more accurate approach to binocular stereo for high-resolution image matching. Elas ensures good performance in the presence of poorly textured and slanted surfaces, but one of its deficiencies is its unsatisfactory ability to capture disparity discontinuities. Our formulation explicitly models the effects of confidence as a likelihood term in a principled manner using the Bayes rule. Because it is an iterative method, we associate each point with a variable confidence value and update this value based on a given confidence updating rule. Meanwhile, complementary support points are selected from stable points whose confidence value exceeds a predefined threshold, which differs from ELAS, whose support points are matched in advance and kept unchanged in the subsequent process. Confidence also plays a vital role in avoiding expensive computation, and the adjustment of support points makes disparity estimation more flexible. Quantitative evaluation demonstrates the effectiveness and efficiency of the proposed formulation in improving the accuracy of disparity estimation.
Multiscale Bayesian state-space model for Granger causality analysis of brain signal

ABSTRACT Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer theoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the functional connections between two brain regions during a task. We use for that purpose an adaptive Kalman filter type of estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. The estimation procedure is achieved through variational Bayesian approximation and is extended for multiple trials. This Bayesian State Space (BSS) model provides a dynamical Granger-causality statistic that is quite natural. We propose to extend the BSS model to include the à trous Haar decomposition. This wavelet-based forecasting method is based on a multiscale resolution decomposition of the signal using the redundant à trous wavelet transform and allows us to capture short- and long-range dependencies between signals. Equally importantly it allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. The application of these models to intracranial local field potential data recorded during a psychological experimental task shows the complex frequency-based cross-talk between amygdala and medial orbito-frontal cortex.
The Effect of Variable Spontaneous Curvature on Dynamic Evolution of Two-Phase Vesicle

This article aims to study the effect of non-uniform distribution of spontaneous curvature on shape transformation of two-phase vesicles via an evolutionary method. Their dynamic evolution is developed based on conventional Helfrich theory, considering bending of the membrane and friction in the surrounding fluid in each phase with variable spontaneous curvature. The variation of spontaneous curvature is assumed to be a function of arc length in each domain considering the effects of inducing factors (surrounding solution concentration and the membrane-protein interactions such as scaffolding and insertion). Membrane pearling from a large vesicle is simulated by the model and compared with the result of constant curvature and also with empirical observations. It can be shown that accurate simulation of some membrane deformation mechanisms depends on careful consideration of key factors such as the SC variations. In addition, the importance of different uniform and non-uniform distributions of spontaneous curvature is discussed with reference to specific cases.