308 research outputs found

    Parental Involvement And Academic Outcomes Among First Generation College Students

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    Higher education can be a challenging culture to navigate for any college student. One group of students that may be at a disadvantage when navigating the this culture is first generation students, those whose parents have not earned a four year degree, compared to their continuing generation peers, those who have at least one parent with a four year degree. The key purpose of this study is to use the theory of cultural capital, with parental involvement as a proxy, to examine relationships between these groups of students, parental involvement, and academic outcomes (academic motivation, class preparedness, and academic performance). Using the College Student Health and Stress Survey (2015), relationships were explored using independent samples t tests and OLS regression analyses. Findings from the t tests suggested there were no differences in academic outcomes between continuing generation and first generation students, but continuing generation students received more parental involvement that first generation students. None of the OLS regression models were significant, indicating that parental involvement did not predict academic outcomes. Findings suggest that although continuing generation students reported more parental involvement, parental involvement did not predict academic outcomes. Perhaps first generation students are becoming as affluent in navigating higher education as continuing generation students and future research may benefit from exploring other forms of cultural capital such as peer support

    Dental Student Report: Rotations at a Federally Qualified Health Center in North Dakota

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    North Dakota has no dental school to encourage student enrollment. Recognizing the need to address dental workforce shortages, and barriers to recruiting new dental professionals to the state, the North Dakota Department of Health & Human Services Oral Health Program (OHP) and the North Dakota Area Health Education Center financially support dental rotations at one Federally Qualified Health Center (FQHC) in North Dakota: Spectra Healt

    Quantum Entanglement and the Two-Photon Stokes Parameters

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    A formalism for two-photon Stokes parameters is introduced to describe the polarization entanglement of photon pairs. This leads to the definition of a degree of two-photon polarization, which describes the extent to which the two photons act as a pair and not as two independent photons. This pair-wise polarization is complementary to the degree of polarization of the individual photons. The approach provided here has a number of advantages over the density matrix formalism: it allows the one- and two-photon features of the state to be separated and offers a visualization of the mixedness of the state of polarization.Comment: 15 pages, 2 figures, accepted for publication in Opt. Com

    Quantum state discrimination

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    It is a fundamental consequence of the superposition principle for quantum states that there must exist non-orthogonal states, that is states that, although different, have a non-zero overlap. This finite overlap means that there is no way of determining with certainty in which of two such states a given physical system has been prepared. We review the various strategies that have been devised to discriminate optimally between non-orthogonal states and some of the optical experiments that have been performed to realise these.Comment: 43 pages, submitted to Advances in Optics and Photonic

    Black Holes, Qubits and Octonions

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    We review the recently established relationships between black hole entropy in string theory and the quantum entanglement of qubits and qutrits in quantum information theory. The first example is provided by the measure of the tripartite entanglement of three qubits, known as the 3-tangle, and the entropy of the 8-charge STU black hole of N=2 supergravity, both of which are given by the [SL(2)]^3 invariant hyperdeterminant, a quantity first introduced by Cayley in 1845. There are further relationships between the attractor mechanism and local distillation protocols. At the microscopic level, the black holes are described by intersecting D3-branes whose wrapping around the six compact dimensions T^6 provides the string-theoretic interpretation of the charges and we associate the three-qubit basis vectors, |ABC> (A,B,C=0 or 1), with the corresponding 8 wrapping cycles. The black hole/qubit correspondence extends to the 56 charge N=8 black holes and the tripartite entanglement of seven qubits where the measure is provided by Cartan's E_7 supset [SL(2)]^7 invariant. The qubits are naturally described by the seven vertices ABCDEFG of the Fano plane, which provides the multiplication table of the seven imaginary octonions, reflecting the fact that E_7 has a natural structure of an O-graded algebra. This in turn provides a novel imaginary octonionic interpretation of the 56=7 x 8 charges of N=8: the 24=3 x 8 NS-NS charges correspond to the three imaginary quaternions and the 32=4 x 8 R-R to the four complementary imaginary octonions. N=8 black holes (or black strings) in five dimensions are also related to the bipartite entanglement of three qutrits (3-state systems), where the analogous measure is Cartan's E_6 supset [SL(3)]^3 invariant.Comment: Version to appear in Physics Reports, including previously omitted new results on small STU black hole charge orbits and expanded bibliography. 145 pages, 15 figures, 41 table

    On the importance of sluggish state memory for learning long term dependency

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    The vanishing gradients problem inherent in Simple Recurrent Networks (SRN) trained with back-propagation, has led to a significant shift towards the use of Long Short-term Memory (LSTM) and Echo State Networks (ESN), which overcome this problem through either second order error-carousel schemes or different learning algorithms respectively. This paper re-opens the case for SRN-based approaches, by considering a variant, the Multi-recurrent Network (MRN). We show that memory units embedded within its architecture can ameliorate against the vanishing gradient problem, by providing variable sensitivity to recent and more historic information through layer- and self-recurrent links with varied weights, to form a so-called sluggish state-based memory. We demonstrate that an MRN, optimised with noise injection, is able to learn the long term dependency within a complex grammar induction task, significantly outperforming the SRN, NARX and ESN. Analysis of the internal representations of the networks, reveals that sluggish state-based representations of the MRN are best able to latch on to critical temporal dependencies spanning variable time delays, to maintain distinct and stable representations of all underlying grammar states. Surprisingly, the ESN was unable to fully learn the dependency problem, suggesting the major shift towards this class of models may be premature

    Learning Shapes Spontaneous Activity Itinerating over Memorized States

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    Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural activities observed in the absence of inputs nor changes caused in the neural activity when an input is provided were studied extensively in the past. However, recent experimental studies have reported existence of structured spontaneous neural activity and its changes when an input is provided. With this background, we propose that memory recall occurs when the spontaneous neural activity changes to an appropriate output activity upon the application of an input, and this phenomenon is known as bifurcation in the dynamical systems theory. We introduce a reinforcement-learning-based layered neural network model with two synaptic time scales; in this network, I/O relations are successively memorized when the difference between the time scales is appropriate. After the learning process is complete, the neural dynamics are shaped so that it changes appropriately with each input. As the number of memorized patterns is increased, the generated spontaneous neural activity after learning shows itineration over the previously learned output patterns. This theoretical finding also shows remarkable agreement with recent experimental reports, where spontaneous neural activity in the visual cortex without stimuli itinerate over evoked patterns by previously applied signals. Our results suggest that itinerant spontaneous activity can be a natural outcome of successive learning of several patterns, and it facilitates bifurcation of the network when an input is provided
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