Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic layers and neutral points near a rotating black hole

Magnetic layers are narrow regions where the field direction changes sharply. They often occur in the association with neutral points of the magnetic field. We show that an organized field can produce these structures near a rotating black hole (BH), and we identify them as potential sites of magnetic reconnection. To that end, we study the field lines affected by the frame-dragging effect, twisting the magnetic structure and changing the position of neutral points. We consider oblique fields in vacuum. We also include the possibility of translational motion of the black hole which may be relevant when the black hole is ejected from the system. The model settings apply to the innermost regions around black holes with the ergosphere dominated by a super-equipartition magnetic field and loaded with a negligible gas content.
High performance V-band GaAs power amplifier and low noise amplifier using low-loss transmission line technology

We present the design and measurement results of millimeter-wave integrated circuits implemented in 0.15-μm baseline GaAs pHEMT. Both active and passive test structures were measured. We present the design of an on-chip slow wave transmission line and RF amplifier from low loss with low noise parameter measurement results at V-band. Finally, the design and measurement result of two amplifiers for low noise amplifier (LNA) and power amplifier (PA). The three-stage LNA matching networks and RF-chock were based upon slow wave transmissions lines (TLs). Peak gain of 13.2 dB at 66 GHz, in-band minimum noise figure less then 5 dB under 3-V supply voltage were obtained at a power consumption of 89 mW. The two stage power amplifier with push-pull combination in balun structure achieves a peak gain of 16.8 dB at 58 GHz OP1dB of 11.5 dBm, Psat of 16.7 dBm, and PAE of 19.8% under 3-V supply voltage were obtained at a power consumption of 188 mW. The chip size, the LNA and PA die area including all Pads are 1.25×0.6 and 1.34 × 0.6 mm2, respectively. The LNA and PA MMICs demonstrate the superior gain and power performance in low-loss TLs technology.
SPECTROSCOPIC ANALYSIS OF DA WHITE DWARFS: STARK BROADENING OF HYDROGEN LINES INCLUDING NONIDEAL EFFECTS

We present improved calculations for the Stark broadening of hydrogen lines in dense plasmas typical of white dwarf atmospheres. Our new model is based on the unified theory of Stark broadening from Vidal, Cooper, and Smith. For the first time, we account for the nonideal effects in a consistent way directly inside the line profile calculations. The Hummer and Mihalas theory is used to describe the nonideal effects due to perturbations on the absorber from protons and electrons. We use a truncation of the electric microfield distribution in the quasi-static proton broadening to take into account the fact that high electric microfields dissociate the upper state of a transition. This approach represents a significant improvement over previous calculations that relied on the use of an ad hoc parameter to mimic these nonideal effects. We obtain the first model spectra with line profiles that are consistent with the equation of state. We revisit the properties of DA stars in the range 40,000 K >Teff> 13,000 K by analyzing the optical spectra with our improved models. The updated atmospheric parameters are shown to differ substantially from those published in previous studies, with a mean mass shifted by +0.034 M☉. We also show that these revised atmospheric parameters yield absolute visual magnitudes that remain in excellent agreement with trigonometric parallax measurements.
Incarcerated Mothers’ Labor Market Outcomes

This study investigates how motherhood and foster care records of their children influence women’s transitions into the labor market after incarceration. Our fixed effects models examine the relative progress of incarcerated mothers in earnings and employment after incarceration, accounting for the difference between mothers and women without children and controlling for time-constant individual characteristics. Our analysis indicates that incarcerated mothers make impressive progress in quarterly employment during the second and third year after incarceration. Most of these increases are from mothers whose children started, but did not resolve, foster care before incarceration.
3-D Rigid Models from Partial Views - Global Factorization

The so-called factorization methods recover 3-D rigid structure from motion by factorizing an observation matrix that collects 2-D projections of features. These methods became popular due to their robustness - they use a large number of views, which constrains adequately the solution - and computational simplicity - the large number of unknowns is computed through an SVD, avoiding non-linear optimization. However, they require that all the entries of the observation matrix are known. This is unlikely to happen in practice, due to self-occlusion and limited field of view. Also, when processing long videos, regions that become occluded often appear again later. Current factorization methods process these as new regions, leading to less accurate estimates of 3-D structure. In this paper, we propose a global factorization method that infers complete 3-D models directly from the 2-D projections in the entire set of available video frames. Our method decides whether a region that has become visible is a region that was seen before, or a previously unseen region, in a global way, i.e., by seeking the simplest rigid object that describes well the entire set of observations. This global approach increases significantly the accuracy of the estimates of the 3-D shape of the scene and the 3-D motion of the camera. Experiments with artificial and real videos illustrate the good performance of our method.
Tracking Positioning Algorithm for Direction of Arrival Based on Direction Lock Loop

In order to solve the problem of poor real-time performance, low accuracy and high computational complexity in the traditional process of locating and tracking of Direction of Arrival (DOA) of moving targets, this paper proposes a DOA algorithm based on the Direction Lock Loop (DILL) which adopts Lock Loop structure to realize the estimation and location of DOA and can adjust the direction automatically along with the changes of a signal’s angular variation to track the position of the signal. Meanwhile, to reduce the influence of nonlinearity and noise on its performance, the UKF filter is designed for eliminating interference of the estimated target signal to improve accuracy of the signal tracking and stability of the system. Simulation results prove that the algorithm can not only get a high resolution DOA estimate signal, but can also locate and track multiple mobile targets effectively with enhanced accuracy, efficiency and stability.
High genetic diversity is not essential for successful introduction

Some introduced populations thrive and evolve despite the presumed loss of diversity at introduction. We aimed to quantify the amount of genetic diversity retained at introduction in species that have shown evidence of adaptation to their introduced environments. Samples were taken from native and introduced ranges of Arctotheca populifolia and Petrorhagia nanteuilii. Using microsatellite data, we identified the source for each introduction, estimated genetic diversity in native and introduced populations, and calculated the amount of diversity retained in introduced populations. These values were compared to those from a literature review of diversity in native, confamilial populations and to estimates of genetic diversity retained at introduction. Gene diversity in the native range of both species was significantly lower than for confamilials. We found that, on average, introduced populations showing evidence of adaptation to their new environments retained 81% of the genetic diversity from the native range. Introduced populations of P. nanteuilii had higher genetic diversity than found in the native source populations, whereas introduced populations of A. populifolia retained only 14% of its native diversity in one introduction and 1% in another. Our literature review has shown that most introductions demonstrating adaptive ability have lost diversity upon introduction. The two species studied here had exceptionally low native range genetic diversity. Further, the two introductions of A. populifolia represent the largest percentage loss of genetic diversity in a species showing evidence of substantial morphological change in the introduced range. While high genetic diversity may increase the likelihood of invasion success, the species examined here adapted to their new environments with very little neutral genetic diversity. This finding suggests that even introductions founded by small numbers of individuals have the potential to become invasive.
Prognostic Value of Cancer Stem Cell Marker Aldehyde Dehydrogenase in Ovarian Cancer: A Meta-Analysis

Objective Aldehyde dehydrogenase (ALDH) has recently been reported as a marker of cancer stem-like cells in ovarian cancer. However, the prognostic role of ALDH in ovarian cancer still remains controversial. In this study, we aimed to evaluate the association between the expression of ALDH and the outcome of ovarian cancer patients by performing a meta-analysis. Methods We systematically searched for studies investigating the relationships between ALDH expression and outcome of ovarian cancer patients. Only articles in which ALDH expression was detected by immunohistochemical staining were included. A meta-analysis was performed to generate combined hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and disease-free survival (DFS). Results A total of 1,258 patients from 7 studies (6 articles) were included in the analysis. Our results showed that high ALDH expression in patients with ovarian cancer was associated with poor prognosis in terms of Os (HR, 1.25; 95% CI, 1.07-1.47; P = 0.005) and DFS (HR, 1.58; 95% CI, 1.00-2.49; P = 0.052), though the difference for DFS was not statistically significant. In addition, there was no evidence of publication bias as suggested by Begg’s and Egger’s tests (Begg’s test, P = 0.707; Egger’s test, P = 0.355). Conclusion The present meta-analysis indicated that elevated ALDH expression was associated with poor prognosis in patients with ovarian cancer.
Ten years later: Exploring the lived experiences of college-aged Muslim American women after September 11th

Using the feminist ecological model (Ballou, Matsumoto, & Wagner, 2002), I explore the lived experiences of ten college-aged Muslim American women in the context of a post-September 11 th United States in this qualitative narrative research study. The informants are individually interviewed and their voices, stories, personal experiences, and perceptions are brought forth in this study. Based on a critical review of the existing literature, the interviews address salient issues for coming of age as Muslim women after 9/11. I use thematic analysis (Braun & Clarke, 2006) to analyze the narratives collected in the interviews. I investigate the sociocultural contexts and structural conditions that enable the personal narratives provided. The resulting themes demonstrate that Muslim American women have diverse experiences of identity development influenced by their access to family and community, and their resources for coping with and reacting to discrimination and violence in their daily lives. Major factors that impact identity development include the practice of hijab, the power of media and government, and gendered stereotypes about Muslim women. This study provides a critique of multicultural research and literature in mainstream psychology by contrasting the personal narratives of my informants with existing knowledge about Muslim Americans and theories of cultural identity development and acculturation.
Emotional Variation in Speech-Based Natural Language Generation

We present a framework for handling emotional variations in a speech-based natural language system for use in the MRE virtual training environment. The system is a first step toward addressing issues in emotion-based modeling of verbal communicative behavior. We cast the problem of emotional generation as a distance minimization task, in which the system chooses between multiple valid realizations for a given input based on the emotional distance of each realization from the speaker’s attitude toward that input. Emotional Variation in Speech-Based Natural Language Generation Michael Fleischman and Eduard Hovy USC Information Science Institute 4676 Admiralty Way Marina del Rey, CA 90292-6695 U.S.A. {fleisch, hovy} @ISI.edu Abstract We present a framework for handling emotional variations in a speech-based natural language system for use in the MRE virtual training environment. The system is a first step toward addressing issues in emotion-based modeling of verbal communicative behavior. We cast the problem of emotional generation as a distance minimization task, in which the system chooses between multiple valid realizations for a given input based on the emotional distance of each realization from the speaker’s attitude toward that input. We discuss evaluations of the system and future work that includes modeling personality and empathy within the same framework.We present a framework for handling emotional variations in a speech-based natural language system for use in the MRE virtual training environment. The system is a first step toward addressing issues in emotion-based modeling of verbal communicative behavior. We cast the problem of emotional generation as a distance minimization task, in which the system chooses between multiple valid realizations for a given input based on the emotional distance of each realization from the speaker’s attitude toward that input. We discuss evaluations of the system and future work that includes modeling personality and empathy within the same framework.
Post-Traumatic Stress and Alcohol-Related Problems Following a College Shooting: Effect of Help-Seeking

ABSTRACT Our aim was to test the hypothesized relationships between trauma exposure and alcohol-related problems, mediated by PTS symptoms, moderated by prior trauma exposure and help-seeking. Participants were 244 members of a private university who were enrolled or employed at the school on the date of a campus shooting. Online measures were distributed via e-mail in October 2014, and included the AUDIT, BTQ, PCL-5, and self-report measures of trauma exposure and counseling services utilized. The overall moderated mediation model examining the indirect effect of exposure to trauma on alcohol-related problems through PTSD symptoms, moderated by prior trauma and help seeking was significant, β(SE) = . 310(.3554), p = . 0001. This study highlights the importance of crisis interventions following trauma exposure.
The Complexity of Approximately Solving Influence Diagrams

Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the treewidth and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.
An efficient Bayesian algorithm for joint target tracking and classification

It is pointed our in this paper that a more appropriate description of the joint target tracking and classification (JTC) problem would be the simultaneous probability density function (pdfs) of target state and target class instead of the joint target state-class pdf. In this paper, models of different classes are combined info a unified set. The Bayesian optimal JTC algorithm based on pdfs of target state and target class is derived, which integrates a Bayesian multiple-model filter and a Bayesian classifier. Also given is a suboptimal JTC algorithm with much lesser computational complexity, which is suitable for real-time application. Simulation results reveal that the proposed JTC algorithm provides a theoretically attractive solution to a class of joint target, tracking and classification problems.
Managing Crowdsourcing Workers

The emergence of online crowdsourcing services such as Amazon Mechanical Turk, presents us huge opportunities to distribute micro-tasks at an unprecedented rate and scale. Unfortunately, the high verification cost and the unstable employment relationship give rise to opportunistic behaviors of workers, which in turn exposes the requesters to quality risks. Currently, most requesters rely on redundancy to identify the correct answers. However, existing techniques cannot separate the true (unrecoverable) error rates from the (recoverable) biases that some workers exhibit, which would lead to incorrect assessment of worker quality. Furthermore, massive redundancy is expensive, increasing significantly the cost of crowdsourced solutions. In this paper, we present an algorithm that can easily separate the true error rates from the biases. Also, we describe how to seamlessly integrate the existence of “gold” data for learning the quality of workers. Next, we bring up an approach for actively testing worker quality in order to quicky identify spammers or malicious workers. Finally, we present experimental results to demonstrate the performance of our proposed algorithm.
Anonymization technique through record elimination to preserve privacy of published data

The term Data Privacy is associated with data collection and dissemination of data. Privacy issues arise in various area such as health care, intellectual property, biological data etc. It is one of the challenging issues when sharing or publishing the data between one to many sources for research purpose and data analysis. Sensitive information of data owners must be protected. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks Earlier, researchers have proposed new methods namely k-anonymity, l-dlverslty, t-closeness for data privacy. K-anonymity method preserves the privacy against record linkage attack alone. It fails to address attribute linkage attack. l-diversity method overcomes the drawback of k-anonymity method. But it fails to address identity disclosure attack and attribute disclosure attack in some exceptional cases. t-closeness method preserves the privacy against attribute linkage attack but not identity disclosure attack. But it computational complexity is large. In this paper, the authors propose a new method to preserve the privacy of individuals' sensitive data from record and attribute linkage attacks. In the proposed method, privacy preservation is achieved through generalization of quasi identifier by setting range values and record elimination. The proposed method is implemented and tested with various data sets.
Space-Time-Coding and Multiple-Antenna ( MIMO )-Systems Key Elements of Future Mobile Systems

In wireless and mobile communications the dimension of space is exploited. Information theory limits are now known and indicate the possibly achievable gains. Multiple antennas on the transmit and receive side allow beamforming, diversity gains and a dramatic increase in data rate at the cost of more sophisticated processing such as space-time coding and turbo detection. These techniques allow also suboptimal solutions implemented via a sequential search or conjugate complex (widely) linear filters. Some applications and future aspects are discussed.
A Convolution Kernel Approach to Identifying Comparisons in Text

Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features seamlessly, we rely on a dependency tree representation, and investigate the applicability of a series of tree kernels. This leads to the development of a new context-sensitive tree kernel: Skip-node Kernel (SNK). We further describe both its exact and approximate computations. Through experiments on real-life datasets, we evaluate the effectiveness of our kernel-based approach for comparison identification, as well as the utility of SNK and its approximations.
Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model

This paper proposes a method for reconstructing partially damaged faces based on a morphable face model. Faces are modeled by linear combinations of prototypes of shape and texture. With the shape and texture information from an undamaged region only, we can estimate optimal coefficients for linear combinations of prototypes of shape and texture by simple projection for least-square minimization (LSM). Our experimental results show that reconstructed faces are very natural and plausible like real photos.
A Convex Guidance Algorithm for Formation Reconfiguration

In this paper, a reconfiguration guidance algorithm for formation flying spacecraft is presented. The formation reconfiguration guidance problem is first formulated as a continuous-time minimum-fuel or minirnumenergy optimal control problem with collision avoidance and control constraints. The optimal control problem is then discretized to obtain a finite dimensional parameter optimization problem. In this formulation, the collision avoidance constraints are imposed via separating planes between each pair of spacecraft. A heuristic is introduced to choose these separating planes that leads to the convexification of the collision avoidance constraints. Additionally, convex constraints are imposed to guarantee that no collisions occur between discrete time samples. The resulting finite dimensional optimization problem is a second order cone program, for which standard algorithms can compute the global optimum with deterministic convergence and a prescribed level of accuracy. Consequently, the formation reconfiguration algorithm can be implemented onboard a spacecraft for real-time operations.
Reviving Gramsci: Crisis, Communication, and Change

1. Gramsci in/for Critical Times 2. Selective Gramsci(s) 3. Gramsci and Communication: Dialectics and Translation 4. Occupy Wall Street: The Limits of War of Position 5. Movimento Cinque Stelle: Dialectics of Passive Revolution 6. From Indignados to Podemos: Sublating Vernacular Rhetoric into National Popular Rhetoric 7. A Dialectical Image
Optimizing OSPF/IS-IS weights in a changing world

A system of techniques is presented for optimizing open shortest path first (OSPF) or intermediate system-intermediate system (IS-IS) weights for intradomain routing in a changing world, the goal being to avoid overloaded links. We address predicted periodic changes in traffic as well as problems arising from link failures and emerging hot spots.
Turbo EM based equalization for multi-user multi-carrier space time system

In this paper, we propose a multi-user detector for space time uplink transmission in multi-carrier CDMA systems. We propose a new combination using STBC, OFDM and CDMA spreading. Furthermore we propose a turbo equalization scheme based on the use of LDPC for channel coding and EM algorithm for channel estimation at the LDPC decoder input. Simulation results show that our proposed receiver is able to function within 2 dB from the outage capacity of the studied multi-path channel.