Miracle-dz/UniFAR-FTUs huggingface dataset:
Orientifolds of K3 and Calabi-Yau manifolds with intersecting D-branes

We investigate orientifolds of type II string theory on K3 and Calabi-Yau 3-folds with intersecting D-branes wrapping special lagrangian cycles. We determine quite generically the chiral massless spectrum in terms of topological invariants and discuss both orbifold examples and algebraic realizations in detail. Intriguingly, the developed techniques provide an elegant way to figure out the chiral sector of orientifold models without computing any explicit string partition function. As a new example we derive a non-supersymmetric Standard-like Model from an orientifold of type IIA on the quintic Calabi-Yau 3-fold with wrapped D6-branes. In the case of supersymmetric intersecting brane models on Calabi-Yau manifolds we discuss the D-term and F-term potentials, the effective gauge couplings and the Green-Schwarz mechanism. The mirror symmetric formulation of this construction is provided within type IIB theory. We finally include a short discussion about the lift of these models from type IIB on K3 to F-theory and from type IIA on Calabi-Yau 3-folds to M-theory on G2 manifolds.
Acoustic vector-sensor beamforming and Capon direction estimation

We consider beamforming and Capon direction of arrival (DOA) estimation using arrays of acoustic vector sensors. We derive an expression for the Cramer-Rao bound (CRB) on the DOA parameters of a single source. Using this, we give conditions that minimize the lower bound on the asymptotic mean-square angular error, and conditions that ensure it is isotropic. The asymptotic performance of the Capon (1969, 1971) and beamforming estimators is analyzed and compared with a scalar-sensor array. The vector-sensor array is seen to have improved performance due to its elements' directional sensitivity. Large sample approximations for the mean-square error (MSE) matrices of the estimators are derived. Throughout, we compare vector-sensor arrays with their scalar-sensor counterparts.
Estimation of the fundamental matrix based on complex wavelets

In this paper, an automatic fundamental matrix estimation method based on complex wavelets is presented. The fundamental matrix is considered important because it reflects the intrinsic projective geometry of the scene. It is widely used in computer vision areas, such as camera calibration, object reconstruction, visual navigation, stereo vision etc. In comparison with the Discrete Wavelet Transform (DWT), the dual-tree complex wavelet transform (DT CWT) possesses two key properties for computer vision: shift invariance, which makes it possible to extract stable local features in an image; and good directional selectivity, making it possible to measure image energy accurately in multiple directions. First, a feature detector based on complex wavelets is used to find the points of interest, and then complex-wavelet-based polar matching is used to find putative correspondences. Compared with the classic ‘Harris corner’ interest point detector, the interest point detector based on DT CWT is a multiscale interest point detector, able to detect different kinds of features, including corners, edges, blobs etc. and the number of interest points can be made scale-dependent. Polar matching is a rotation invariant descriptor derived from the DT CWT coefficients; and scale invariance is induced by adjusting the wavelet levels and sampling radius according to the scale estimated by the detector. A minimum of only 7 correspondence points are needed to compute the fundamental matrix. Preliminary tests on some classic building scene images show that the method works well.
Net Neutrality

We analyze UK and US experiences as they relate to two central net neutrality questions: (1) whether competition serves as a deterrent to the discriminatory treatment of Internet traffic, and (2) whether discrimination creates barriers to application development and innovation. Relying on consumer switching behavior to provide more comprehensive competitive discipline was insufficient for a variety of reasons, including the presence of switching costs. The process of correcting errors in the technology used for application-specific management revealed that such management creates costs for application developers and innovators, regardless of whether their products are targeted for traffic management.
Universality of the SIS prevalence in networks

Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.
A new paradigm for synthesis and propagation of clock gating conditions

Clock gating has become a standard practice for saving dynamic power in the clock network. Due to design reuse, it is common to find designs that have already some partial clock gating. We propose to exploit the existing clock gating in order to extract stronger gating conditions for blocks that are poorly gated or not gated at all. A second contribution of our paper is a robust and scalable approach to extract stability conditions for clock gating. Finally, we present a uniform treatment of unobservability and stability as dual approaches for propagating gating conditions forward and backward. Experimental results demonstrate significant power reduction (in the range of 14% - 55% of the clock power) on Intel micro-processor designs.
New condition monitoring procedure for lifting cranes using motor current signal analysis

Vibration analysis does not cover all defects able to affect a rotating machine, including those that induce torque or instantaneous rotation speed fluctuations. Indeed the vibrational energy contributions of this type of defects such as a natural and gradual degradation of the gears are often weak. Therefore the appearance of these defects is very difficult to detect from the vibrational energy indicators commonly used in machine condition monitoring. In these conditions, it is often late when the symptoms of failure are reflected in the vibration signal. Several signal processing techniques have emerged to improve the vibration diagnostic result by proposing the use of angular resampling techniques to overcome the difficulties of analyzing nonstationary signals as, for example, velocity fluctuations that can hide the appearance of defects. Other researchers suggest the direct measurement of torque to make the diagnosis, however this technique is intrusive and too expensive. In the recent years there were large interests in diagnosis techniques using motor current signal analysis since these signals are known to be most representative of the torque, easily acquired, not expensive and especially not intrusive. This paper deals with a new diagnostic method based on current signal analysis by associating a simple demodulation technique with an angular resampling technique generally used for vibration signals. Finally the performance of the new method is illustrated through experimental data treating gears wear detection in lifting cranes.
An implementation of a Distributed Stochastic Gradient Descent for Recommender Systems based on Map-Reduce

This work presents an implementation of a Distributed Stochastic Gradient Descent (DSGD) for Recommender Systems based on Hadoop/MapReduce. Recommender Systems aim at presenting first the information in which users may be more interested. To do this, they analyse a great volume of data that represent the users preferences (e.g. ratings). Thus, this stirs up the need of load-balancing. DSGD is a Matrix Factorization technique that has demonstrated high accuracy and scalability. In this work we expose this algorithm and modify it to improve its accuracy and adaptability to a hadoop cluster. The experimentation phase uses Movie-Lens datasets. Comparisons with other algorithms are given. Results show the good performance of the implementation.
Contextually Meaningful Representation of Color for the Description of Visual Content

A flexible framework for color representation is developed with the goal of lending perceptual or context specific meaning to visual content. Any given color representation in this framework consists of a set of definable base colors chosen from an underlying tri-chromatic color system. The mathematical foundation of the framework allows for a simple yet powerful assessment of visual similarity. A specific color representation within this framework is devised with the objective of enhancing content-based retrieval applications involving photographic content. Examples are presented and discussed.
Estimating Tropical Cyclone Intensity from Infrared Image Data

AbstractThis paper describes results from a near-real-time objective technique for estimating the intensity of tropical cyclones from satellite infrared imagery in the North Atlantic Ocean basin. The technique quantifies the level of organization or axisymmetry of the infrared cloud signature of a tropical cyclone as an indirect measurement of its maximum wind speed. The final maximum wind speed calculated by the technique is an independent estimate of tropical cyclone intensity. Seventy-eight tropical cyclones from the 2004–09 seasons are used both to train and to test independently the intensity estimation technique. Two independent tests are performed to test the ability of the technique to estimate tropical cyclone intensity accurately. The best results from these tests have a root-mean-square intensity error of between 13 and 15 kt (where 1 kt ≈ 0.5 m s−1) for the two test sets.
Integrating six sigma and theory of constraints for continuous improvement: a case study

Purpose – To investigate the potential of combining six sigma (SS) and theory of constraints (TOC) to improve production system performance.Design/methodology/approach – Based on the literature, this paper proposes an integrated TOC/SS framework and applies this framework to an axle manufacturing company to improve its gear‐cutting operation.Findings – The results of the case study indicate that the company benefited tremendously from its emphasis on global improvement guided by the TOC concept. Managers were able to select a continuous improvement (CI) project that had greater impact on bottom line performance. On the other hand, SS provided various statistical tools and engineering techniques (such as value analysis, Pareto diagram and control charts) for defining the specific process to be improved, analyzing the root causes, and designing actions for making improvement.Research limitations/implications – For the future effort of integrating TOC with SS and other CI techniques, we suggest the incorpora.
The Calibration of the Swift UVOT Optical Observations: A Recipe for Photometry

The Ultraviolet/Optical Telescope (UVOT) on board Swift has the capability to provide critical insight into the physics of the early afterglows of gamma‐ray bursts (GRBs). But without precise calibration of the UVOT to standard photometric systems, it is impossible to leverage late‐time, ground‐based follow‐up data to the early‐time UVOT observations. In this paper, we present a calibration of the Swift UVOT photometry to the standard Johnson UBV system for the UVOT U, B, and V filters, and a step‐by‐step photometry recipe for analyzing these data. We base our analysis on aperture photometry performed on the ground‐based and UVOT observations of the local standard stars in the fields of supernovae (SNe) 2005am and 2005cf and a number of Landolt standard stars. We find that the optimal photometry aperture radius for UVOT data is small (2 .″5 for unbinned data, 3 .″0 for 2 × 2 binned data), and we show that the coincidence‐loss (C‐loss) correction is important even for relatively faint magnitudes (16–19 mag). Based on a theoretically motivated model, we fit the C‐loss correction with two parameters, the photometric zero point (ZP) and the saturation magnitude (m∞), and derive tight constraints for both parameters [σ(ZP) = 0.01 mag and σ(m∞) = 0.02 mag)]. We find that the color‐term correction is not necessary for the UVOT B and V filters but that it is necessary for the U filter for blue objects [(U - V)<0.4 mag]. We analyze the UVOT UBV photometry of SN 2005am and find that the UVOT photometry is generally consistent with the ground‐based observations but that a difference of up to 0.5 mag is found when the SN became faint. We also apply our calibration results to the UVOT observations of GRB 050603. There is a scatter of ∼0.04–0.08 mag in our final UVOT photometry, the cause of which is unclear, but it may be partly due to the spatial variation in the pixel sensitivity of the UVOT detector.
Effective Use of Large High-Bandwidth Memory Caches in HPC Stencil Computation via Temporal Wave-Front Tiling

Stencil computation is an important class of algorithms used in a large variety of scientific-simulation applications. The performance of stencil calculations is often bounded by memory bandwidth. High-bandwidth memory (HBM) on devices such as those in the Intel® Xeon Phi™ ™200 processor family (code-named Knights Landing) can thus provide additional performance. In a traditional sequential time-step approach, the additional bandwidth can be best utilized when the stencil data fits into the HBM, restricting the problem sizes that can be undertaken and under-utilizing the larger DDR memory on the platform. As problem sizes become significantly larger than the HBM, the effective bandwidth approaches that of the DDR, degrading performance. This paper explores the use of temporal wave-front tiling to add an additional layer of cache-blocking to allow efficient use of both the HBM bandwidth and the DDR capacity. Details of the cache-blocking and wave-front tiling algorithms are given, and results on a Xeon Phi processor are presented, comparing performance across problem sizes and among four experimental configurations. Analyses of the bandwidth utilization and HBM-cache hit rates are also provided, illustrating the correlation between these metrics and performance. It is demonstrated that temporal wave-front tiling can provide a 2.4™ speedup compared to using HBM cache without temporal tiling and 3.3x speedup compared to only using DDR memory for large problem sizes.
Development of a New Growth Standard for Breastfed Chinese Infants: What Is the Difference from the WHO Growth Standards?

The objectives of this longitudinal study were to examine the trajectory of breastfed infants’ growth in China to update growth standards for early childhood, and to compare these updated Chinese growth standards with the growth standards recommended by the World Health Organization (WHO) in 2006.This longitudinal cohort study enrolled 1,840 healthy breastfed infants living in an "optimal" environment favorable to growth and followed up until one year of age from 2007 to 2010. The study subjects were recruited from 60 communities in twelve cities in China. A participating infant’s birth weight was measured within the first hour of the infant’s life, and birth length and head circumference within 24 hours after birth. Repeated weekly and monthly anthropometric measurements were also taken. Multilevel (ML) modelling via MLwiN2.25 was fitted to estimate the growth curves of weight-for-age (WFA), length-for-age (LFA), and head circumference-for-age (HFA) for the study sample as a whole and by child sex, controlling for mode of delivery, the gravidity and parity of the mother, infant’s physical measurements at birth, infant’s daily food intaking frequency per day, infant’s medical conditions, the season when the infant’s physical measurement was taken, parents’ ages, heights, and attained education, and family structure and income per month. During the first four weeks after birth, breastfed infants showed an increase in weight, length, and head circumference of 1110g, 4.9 cm, and 3.2 cm, respectively, among boys, and 980 g, 4.4 cm, and 2.8 cm, respectively, among girls. Throughout infancy, the total growth for these three was 6930 g, 26.4 cm, and 12.5 cm, respectively, among boys, and 6480 g, 25.5 cm, and 11.7 cm, respectively, among girls. As expected, there was a significant sex difference in growth during the first year. In comparison with the WHO growth standards, breastfed children in our study were heavier in weight, longer in length, and bigger in head circumference, with the exception of a few age points during the first two to four months on the upper two percentile curves.Our data suggested the growth curves for breastfed infants in China were significantly different in comparison with those based on the WHO standards. The adoption of the WHO infant growth standards among Chinese infants, as well as the methods used in the development of such growth standards in China, need careful and coordinated consideration.
Decision Processes in Human Performance Monitoring

The ability to detect and compensate for errors is crucial in producing effective, goal-directed behavior. Human error processing is reflected in two event-related brain potential components, the error-related negativity (Ne/ERN) and error positivity (Pe), but the functional significance of both components remains unclear. Our approach was to consider error detection as a decision process involving an evaluation of available evidence that an error has occurred against an internal criterion. This framework distinguishes two fundamental stages of error detection—accumulating evidence (input), and reaching a decision (output)—that should be differentially affected by changes in internal criterion. Predictions from this model were tested in a brightness discrimination task that required human participants to signal their errors, with incentives varied to encourage participants to adopt a high or low criterion for signaling their errors. Whereas the Ne/ERN was unaffected by this manipulation, the Pe varied consistently with criterion: A higher criterion was associated with larger Pe amplitude for signaled errors, suggesting that the Pe reflects the strength of accumulated evidence. Across participants, Pe amplitude was predictive of changes in behavioral criterion as estimated through signal detection theory analysis. Within participants, Pe amplitude could be estimated robustly with multivariate machine learning techniques and used to predict error signaling behavior both at the level of error signaling frequencies and at the level of individual signaling responses. These results suggest that the Pe, rather than the Ne/ERN, is closely related to error detection, and specifically reflects the accumulated evidence that an error has been committed.
Brain-Controlled Robots

The idea of moving robots or prosthetic devices not by manual control, but by mere thinking (i.e., the brain activity of human subjects) has fascinated researchers for the last 30 years, but it is only now that first experiments have shown the possibility to do so. How can brainwaves be used to directly control robots? Most of the hope for braincontrolled robots comes from invasive approaches that provide detailed single neuron activity recorded from microelectrodes implanted in the brain [1]. The motivation for these invasive approaches is that it has been widely shown that motor parameters related to hand and arm movements are encoded in a distributed and redundant way by ensembles of neurons in the motor system of the braini? ½motor, premotor and posterior parietal cortex. For humans, however, it is preferable to use non-invasive approaches to avoid health risks and the associated ethical concerns. Most non-invasive brain-computer interfaces (BCI) use electroencephalogram (EEG) signals; i.e., the electrical brain activity recorded from electrodes placed on the scalp. The main source of the EEG is the synchronous activity of thousands of cortical neurons. Thus, EEG signals suffer from a reduced spatial resolution and increased noise due to measurements on the scalp. As a consequence, current EEG-based brain-actuated devices are limited by a low channel capacity and are considered too slow for controlling rapid and complex sequences of robot movements. But, recently, we have shown for the first time that online analysis of EEG signals, if used in combination with advanced robotics and machine learning techniques, is sufficient for humans to continuously control a mobile robot [2] and a wheelchair [3]. In this article we will review our work on non-invasive brain-controlled robots and discuss some of the challenges ahead.
Cynicism as a way of life: From the Classical Cynic to a New Cynicism

In light of the recent revival of interest for philosophy as a way of life, Cynicism has received relatively little attention. Classical cynicism, however, is a particularly rich and valuable school in this respect, offering a philosophy that is before anything else a way of life, combining philosophical reflection, a value system, and a practice of living. The present article articulates classical Cynicism as a philosophy as a way of life along these lines. Additionally, classical Cynicism offers a valuable contribution both to current philosophical investigation and to philosophy’s contribution to the question of the good life. In particular, classical Cynicism gives a number of guidelines for a new cynicism, distinct from and regularly in opposition to common modern cynicism.
Concept and design of the HEKTA (Harvest Energy from the Knee and Transfer it to the Ankle) transfemoral prosthesis

The main issues with the current transfemoral prostheses are the insufficient ankle plantarflexion resulting in an insufficient push-off and the lack of knee flexion during stance phase. The HEKTA (Harvest Energy from the Knee and Transfer it to the Ankle) is a prototype of a prosthesis that allows a transfemoral amputee to walk overground with a more natural gait pattern. The prototype allows knee flexion during the stance phase and harvests the energy that needs to be dissipated during the swing phase. The energy is transferred to the ankle where it provides an active push off.
Tracking performance prediction and evaluation: the modified Riccati equation

In this paper, some recent results on the modified Riccati equation are studied. This modified Riccati equation has already been associated with tracking a target under measurement uncertainty. We consider the special case of tracking a target without clutter, but with a probability of detection of less than one. This special case has received quite some attention recently, especially in relationship with Cramer Rao bounds, or equivalently expected performance. Furthermore, in some other recent works, new theoretical results on the modified Riccati equation have been derived. We will compare these results and point out their importance for performance assessment and prediction in a target tracking context.