Miracle-dz/UniFAR-FTUs huggingface dataset:
Women in Student Affairs: Navigating the Roles of Mother and Administrator.

Women in Student Affairs: Navigating the Roles of Mother and Administrator. (December 2011) Krista Jorge Bailey, B.S.; M.Ed., Texas A&M University Chair of Advisory Committee: Dr. Jia Wang The purpose of this study was to understand the experiences of women who have children and work in mid-level student affairs positions. The study of this phenomenon was driven by four problems: (a) women face barriers in rising to upper-level leadership positions, (b) women are more likely than men to leave the field of student affairs, (c) there is a dearth of research related to women who have children and work in student affairs, and (d) the mid level has received inadequate research attention. These issues for women in student affairs called for further examination of career development strategies and work-life balance support and initiatives. Without meaningful support for career development and work-life balance, women professionals may continue to leave the field at a higher rate than men. Within the naturalistic inquiry research paradigm, I adopted a phenomenological approach. Fifteen women at colleges and universities in Texas, who held mid-level student affairs administrator positions and were mothers, were interviewed. Data were analyzed using the content analysis method. The findings indicated that the dual roles of being a mother and an administrator presented challenges and rewards for each participant. The women often experienced overlap or collision between the two roles and the navigation of the role collision prompted the women to develop strategies to address these challenges. The five most common strategies that participants used were (a) building support systems, (b) defining
A taxonomy and adversarial model for attacks against network log anonymization

In recent years, it has become important for researchers, security incident responders and educators to share network logs, and many log anonymization tools and techniques have been put forth to sanitize this sensitive data source in order to enable more collaboration. Unfortunately, many new attacks have been created, in parallel, that try to exploit weaknesses in the anonymization process. In this paper, we present a taxonomy that relates similar kinds of attacks in a meaningful way. We also present a new adversarial model which we can map into the taxonomy by the types of attacks that can be perpetrated by a particular adversary. This has helped us to negotiate the trade-offs between data utility and trust, by giving us a way to specify the strength of an anonymization scheme as a measure of the types of adversaries it protects against.
Potential of two‐dimensional electrophoresis in routine identification of closely related durum wheat lines

Four closely related curum wheat varieties were compared by computer‐assisted analysis of two‐dimensional electrophoretic maps of leaf proteins. A low inter‐varietal polymorphism was revealed and seven reliable qualitatively varying proteins allowed rapid visual identification of genotypes. For numerous spots, presence/absence or quantitative variations were greatly affected by a batch effect. Several criteria that should be used to discard unreliable spots or gels a priori were reviewed. Nevertheless, it was shown that, provided that the experimental design allows the integration of the batch effect, screening for discriminant markers as well as computing distances based on protein quantity variations are possible and allow variety identification. Euclidean and Mahalanobis distances allowed variety discrimination and single gel classification with a minimum risk of error, not only by taking into account the quantitative variations in discriminant proteins selected by analysis of variance, but also by taking into account all reproducible spots. The possible applications of two‐dimensional electrophoresis in variety identification are discussed.
Automatic landing of unmanned aerial vehicle using fuzzy control

In order to improve the precision of trajectory tracking and the security of landing, a longitudinal control system based on fuzzy control for automatic landing of UAV is presented in the paper. An optimization method based on PSO (Particle Swarm Optimization) algorithm is proposed to design the Integrated Landing Reference System, which guarantees the security margin and deflection margin. As traditional control structure only controls sink rate during flare phase, which can't guarantee safe touchdown pitch angle under disturbances. A fuzzy logic scheme is employed to make a compromise between trajectory tracking and touchdown security. Then a fuzzy control method based on GA (Genetic Algorithm) using analytically optimal function without quantization is proposed to design fuzzy controllers. Simulation results prove that the designed automatic landing control system performs good robust property under wind disturbance, aerodynamics parameters perturbation and sensor measurement errors, besides it improve the precision of landing point.
Bacterial named entity recognition based on dictionary and conditional random field

There are intensive computational efforts to discover large-scale microbial interactions from metagenomic abundance data, however, it is often difficult to validate such inferred interactions without a manually curated dataset. There are also a number of small-scale microbial interactions reported in massive literature with experimental confidence. Text mining can be employed to extract such microbial interactions from biomedical literature which could be a significant complement to abundance-based method. The key tasks of text mining include named entity recognition and relation extraction. Named entity recognition identifies the name of the specified type from the text. We manually annotated a corpus with 1344 abstracts from microbial literature for the task of bacterial named entity recognition. Six new features were added in addition to the general features of the biomedical field. Based on a bacterial dictionary and conditional random field (CRF), the bacterial named entity recognition model was trained and it achieved a performance with precision 89.118%, recall 81.598 % and F-measure 85.192%. The system and template are available at https://github.com/bluelilywxy/BacNER-V1.0. git.
Visceral and Cutaneous Leishmaniasis Recommendations for Solid Organ Transplant Recipients and Donors.

Epidemiology and Risk Factors Leishmaniasis is a protozoan disease transmitted through the bite of infected female sandflies of the genera Phlebotomus (Old World) and Lutzomyia (New World). Infection with Leishmania species (Figure 1) may cause cutaneous, mucocutaneous, or visceral leishmaniasis (VL). Cutaneous leishmaniasis (CL) is characterized by single or multiple skin ulcers, satellite lesions, or nodular lymphangitis and is associated with multiple species (Figure 2). Mucocutaneous leishmaniasis (MCL) is often caused by L. braziliensis and L. panamensis, and leads to metastasis of the disease to the mucosal tissues of the mouth and upper respiratory tract via lymphatic or hematogenous dissemination (Figure 3). VL in the New World is caused by L. infantum (= L. chagasi). In this form, the parasite can infect internal organs, such as the liver, spleen, and bone marrow, causing life-threatening diseases. However, in immunocompromised hosts, such as organ transplant recipients, the clinical presentation of this disease and the response to treatment can be greatly altered. Annually, there are 1.3 million new cases of leishmaniasis worldwide, predominantly in tropical andMediterranean regions, where the disease is endemic. Over 1000000 new cases of CL and 200000 to 400000 new cases of VL are reported annually. Brazil has a high burden of both VL and CL. In 2014, 3453 cases of VL were reported (0.41
Serial effects of high-resistance and prolonged endurance training on Na+-K+ pump concentration and enzymatic activities in human vastus lateralis.

The purpose of this study was to compare two contrasting training models, namely high-resistance training and prolonged submaximal training on the expression of Na+-K+ ATPase and changes in the potential of pathways involved in energy production in human vastus lateralis. The high-resistance training group (VO2peak = 45.3 +/- 1.9 mL kg(-1) min(-1), mean +/- SE, n = 9) performed three sets of six to eight repetitions maximal, each of squats, leg presses and leg extensions, three times per week for 12 weeks, while the prolonged submaximal training group (VO2peak = 44.4 +/- 6.6 mL kg(-1) min(-1), n = 7) cycled 5-6 times per week for 2 h day(-1) at 68% VO2peak for 11 weeks. In the HRT group, Na+-K+ ATPase (pmol g(-1) wet wt), measured with the 3H-ouabain binding technique, showed no change from 0 (289 +/- 22) to 4 weeks (283 +/- 15), increased (P < 0.05) by 16% at 7 weeks and remained stable until 12 weeks (319 +/- 19). For prolonged submaximal training, a 22% increase (P < 0.05) was observed from 0 (278 +/- 31) until 3 weeks (339 +/- 29) with no further changes observed at either 9 weeks (345 +/- 25) or 11 weeks (359 +/- 34). In contrast to high-resistance training, where a 15% increase (P < 0.05) was observed, only in the maximal activity of phosphorylase, prolonged submaximal training resulted in increases in malate dehydrogenase, beta-hydroxyl-CoA dehydrogenase, hexokinase and phosphofructokinase. In contrast to high-resistance training which failed to result in an increase in VO2peak, prolonged submaximal training increased VO2peak by approximately 15%. Only for prolonged exercise training was a relationship observed for VO2peak and Na+-K+-ATPase (r = 0.59; P < 0.05). Correlations between VO2peak and mitochondrial enzyme activities were not significant (P > 0.05) for either training programme. It is concluded that although both training programmes stimulate an up-regulation in Na+-K+ ATPase concentration, only the prolonged submaximal training programme enhances the potential for beta-oxidation, oxidative phosphorylation and glucose phosphorylation.
Learning Spatial Constraints using Gaussian Process for Shared Control of Semi-autonomous Mobile Robots

In this paper, a novel human-robot shared control approach is proposed to solve the problem of semiautonomous mobile robot navigation with the spatial constraints of maintaining reliable Wi-Fi connection. In particular, the presented approach benefits from using Gaussian Process Regression method to learn the distribution of indoor Wi-Fi signal strength (WSS) and to fuse it with the environmental occupancy probability. The resulting WSS-Occupancy hybrid map is further utilized for generating paths that prevent the robot from violating the spatial restriction. A shared control strategy is designed to implement the WSS-aware navigation behaviour. The approach is evaluated by both simulation and real-world experiments, in which the results validate the practicability and effectiveness of the approach.
Memory impairment in older adults’ diversionary thoughts

The diversion paradigm was created in the context of explaining the effect of the instruction to forget some recently encoded material in the list-method of the directed forgetting paradigm. The current study of healthy older adults employed the diversion paradigm with two main goals: to determine whether thinking about an autobiographical memory interferes with the recall of recently encoded information and to explore whether the degree of forgetting depends on the temporal distance created by the diversionary thought. Ninety non-institutionalized Portuguese older adults (47 females and 43 males), aged 65–69 years, with education levels of between 3 and 6 years participated in this study. The exclusion criteria were as follows: presence of depressive symptomatology (assessed with the Geriatric Depression Scale-30) and global cognitive deterioration (assessed with the Mini–Mental State Examination). Concerning the diversion paradigm, one group was instructed to think about an autobiographical event (remembering one’s childhood home or the last party that one had attended) after studying one word list (List 1) and before viewing the second word list (List 2). After a brief distraction task, the participant had to recall the words from both of the studied lists. In the control group, the procedure was the same, but the diversionary thought was substituted by a speed reading task. The obtained results showed the amnesic effect of diversionary thought but did not show a greater degree of forgetting when the autobiographical events in the diversionary thoughts were temporally more distant. Considering the practical implications of these results, this study alerts us to the importance of promoting strategies that enable older adults to better remember important information and effectively forget irrelevant information.
Characterizing Acupuncture De Qi in Mild Cognitive Impairment: Relations with Small-World Efficiency of Functional Brain Networks

As an intermediate state between normal aging and dementia, mild cognitive impairment (MCI) became a hot topic and early treatments can improve disease prognosis. Acupuncture is shown to have possible effect in improving its cognitive defect. However, the underlying neural mechanism of acupuncture and relations between De Qi and different needling depths are still elusive. The present study aimed to explore how acupuncture can exert effect on the reorganization of MCI and to what extent needling depths, associating with De Qi sensations, can influence the acupuncture effects for MCI treatment. Our results presented that MCI patients exhibited losses of small-world attributes indicated by longer characteristic path lengths and larger clustering coefficients, compared with healthy controls. In addition, acupuncture with deep needling can induce much stronger and a wide range of De Qi sensations both in intensity and prevalence. Acupuncture with deep needling showed modulatory effect to compensate the losses of small-world attributes existed in MCI patients while acupuncture with superficial needling did not. Furthermore, acupuncture with deep needling enhanced the nodal centrality primarily in the abnormal regions of MCI including the hippocampus, postcentral cortex as well as anterior cingulate cortex. This study provides evidence to understand neural mechanism underlying acupuncture and the key role of De Qi for MCI treatment.
Sums of squares and moment problems in equivariant situations

We begin a systematic study of positivity and moment problems in an equivariant setting. Given a reductive group G over R acting on an affine R-variety V, we consider the induced dual action on the coordinate ring R[V] and on the linear dual space of R[V]. In this setting, given an invariant closed semialgebraic subset K of V(R), we study the problem of representation of invariant nonnegative polynomials on K by invariant sums of squares, and the closely related problem of representation of invariant linear functionals on R[V] by invariant measures supported on K. To this end, we analyse the relation between quadratic modules of R[V] and associated quadratic modules of the (finitely generated) subring R[V[ G of invariant polynomials. We apply our results to investigate the finite solvability of an equivariant version of the multidimensional K-moment problem. Most of our results are specific to the case where the group G(R) is compact.
AN INTENSELY STAR-FORMING GALAXY AT z ∼ 7 WITH LOW DUST AND METAL CONTENT REVEALED BY DEEP ALMA AND HST OBSERVATIONS

We report deep ALMA observations complemented by associated Hubble Space Telescope (HST) imaging for a luminous (mUV = 25) galaxy, “Himiko,” at a redshift of z = 6.595. The galaxy is remarkable for its high star formation rate, 100 M☉ yr−1, which has been securely estimated from our deep HST and Spitzer photometry, and the absence of any evidence for strong active galactic nucleus activity or gravitational lensing magnification. Our ALMA observations probe an order of magnitude deeper than previous IRAM observations, yet fail to detect a 1.2 mm dust continuum, indicating a flux of <52 μJy, which is comparable to or weaker than that of local dwarf irregulars with much lower star formation rates. We likewise provide a strong upper limit for the flux of [C ii] 158 μm, , which is a diagnostic of the hot interstellar gas that is often described as a valuable probe for early galaxies. In fact, our observations indicate that Himiko lies off the local –star formation rate scaling relation by a factor of more than 30. Both aspects of our ALMA observations suggest that Himiko is a unique object with a very low dust content and perhaps nearly primordial interstellar gas. Our HST images provide unique insight into the morphology of this remarkable source, highlighting an extremely blue core of activity and two less extreme associated clumps. Himiko is undergoing a triple major merger event whose extensive ionized nebula of Lyα emitting gas, discovered in our earlier work with Subaru, is powered by star formation and the dense circumgalactic gas. We are likely witnessing an early massive galaxy during a key period of its mass assembly close to the end of the reionization era.
SampleRank : Learning Preferences from Atomic Gradients

Large templated factor graphs with complex structure that changes during inference have been shown to provide state-of-the-art experimental results on tasks such as identity uncertainty and information integration. However, learning parameters in these models is difficult because computing the gradients require expensive inference routines. In this paper we propose an online algorithm that instead learns preferences over hypotheses from the gradients between the atomic steps of inference. Although there are a combinatorial number of ranking constraints over the entire hypothesis space, a connection to the frameworks of sampled convex programs reveals a polynomial bound on the number of rankings that need to be satisfied in practice. We further apply ideas of passive aggressive algorithms to our update rules, enabling us to extend recent work in confidenceweighted classification to structured prediction problems. We compare our algorithm to structured perceptron, contrastive divergence, and persistent contrastive divergence, demonstrating substantial error reductions on two real-world problems (20% over contrastive divergence).
Program synthesis by sketching

The goal of software synthesis is to generate programs automatically from high-level specifications. However, efficient implementations for challenging programs require a combination of high-level algorithmic insights and low-level implementation details. Deriving the low-level details is a natural job for a computer, but the synthesizer can not replace the human insight. Therefore, one of the central challenges for software synthesis is to establish a synergy between the programmer and the synthesizer, exploiting the programmer's expertise to reduce the burden on the synthesizer. This thesis introduces sketching, a new style of synthesis that offers a fresh approach to the synergy problem. Previous approaches have relied on meta-programming, or variations of interactive theorem proving to help the synthesizer deduce an efficient implementation. The resulting systems are very powerful, but they require the programmer to master new formalisms far removed from traditional programming models. To make synthesis accessible, programmers must be able to provide their insight effortlessly, using formalisms they already understand. In Sketching, insight is communicated through a partial program, a sketch that expresses the high-level structure of an implementation but leaves holes in place of the low-level details. This form of synthesis is made possible by a new SAT-based inductive synthesis procedure that can efficiently synthesize an implementation from a small number of test cases. This algorithm forms the core of a new counterexample guided inductive synthesis procedure (CEGIS) which combines the inductive synthesizer with a validation procedure to automatically generate test inputs and ensure that the generated program satisfies its specification. With a few extensions, CEGIS can even use its sequential inductive synthesizer to generate concurrent programs; all the concurrency related reasoning is delegated to an off-the-shelf validation procedure. The resulting synthesis system scales to real programming problems from a variety of domains ranging from bit-level ciphers to manipulations of linked datastructures. The system was even used to produce a complete optimized implementation of the AES cipher. The concurrency aware synthesizer was also used to synthesize, in a matter of minutes, the details of a fine-locking scheme for a concurrent set, a sense reversing barrier, and even a solution to the dining philosophers problem. The system was also extended with domain specific knowledge to better handle the problem of implementing stencil computations, an important domain in scientific computing. For this domain, we were able to encode domain specific insight as a problem reduction that converted stencil sketches into simplified sketch problems which CEGIS resolved in a matter of minutes. This specialized synthesizer was used to quickly implement a MultiGrid solver for partial differential equations containing many difficult implementation strategies from the literature. In short, this thesis shows that sketching is a viable approach to making synthesis practical in a general programming context.
Fast model tracking with multiple cameras for augmented reality

In this paper we present a technique for tracking complex models in video sequences with multiple cameras. Our method uses information derived from image gradient by comparing them with edges of the tracked object, whose 3D model is known. A score function is defined, depending on the amount of image gradient "seen" by the model edges. The sought pose parameters are obtained by maximizing this function using a non deterministic algorithm which proved to be optimal for this problem. Preliminary experiments with both synthetic and real sequences have shown small errors in pose estimations and a good behavior in augmented reality applications.
Revisiting interval graphs for Network Science

The vertices of an interval graph represent intervals over a real line where overlapping intervals denote that their corresponding vertices are adjacent. This implies that the vertices are measurable by a metric and there exists a linear structure in the system. The generalization is an embedding of a graph onto a multi-dimensional Euclidean space and it was used by scientists to study the multi-relational complexity of ecology. However the research went out of fashion in the 1980s and was not revisited when Network Science recently expressed interests with multi-relational networks known as multiplexes. This paper studies interval graphs from the perspective of Network Science.