22,135 research outputs found
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due
to its application in many knowledge areas. One of the major challenges on
mining data streams is concept drift, which requires the learner to discard the
current concept and adapt to a new one. Ensemble-based drift detection
algorithms have been used successfully to the classification task but usually
maintain a fixed size ensemble of learners running the risk of needlessly
spending processing time and memory. In this paper we present improvements to
the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for
regression that employs social networks theory. In order to detect concept
drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show
improvements in accuracy, especially in concept drift situations and better
performance compared to other state-of-the-art algorithms in both real and
synthetic data
Fair Grading Algorithms for Randomized Exams
This paper studies grading algorithms for randomized exams. In a randomized
exam, each student is asked a small number of random questions from a large
question bank. The predominant grading rule is simple averaging, i.e.,
calculating grades by averaging scores on the questions each student is asked,
which is fair ex-ante, over the randomized questions, but not fair ex-post, on
the realized questions. The fair grading problem is to estimate the average
grade of each student on the full question bank. The maximum-likelihood
estimator for the Bradley-Terry-Luce model on the bipartite student-question
graph is shown to be consistent with high probability when the number of
questions asked to each student is at least the cubed-logarithm of the number
of students. In an empirical study on exam data and in simulations, our
algorithm based on the maximum-likelihood estimator significantly outperforms
simple averaging in prediction accuracy and ex-post fairness even with a small
class and exam size
A hybrid quantum algorithm to detect conical intersections
Conical intersections are topologically protected crossings between the
potential energy surfaces of a molecular Hamiltonian, known to play an
important role in chemical processes such as photoisomerization and
non-radiative relaxation. They are characterized by a non-zero Berry phase,
which is a topological invariant defined on a closed path in atomic coordinate
space, taking the value when the path encircles the intersection
manifold. In this work, we show that for real molecular Hamiltonians, the Berry
phase can be obtained by tracing a local optimum of a variational ansatz along
the chosen path and estimating the overlap between the initial and final state
with a control-free Hadamard test. Moreover, by discretizing the path into
points, we can use single Newton-Raphson steps to update our state
non-variationally. Finally, since the Berry phase can only take two discrete
values (0 or ), our procedure succeeds even for a cumulative error bounded
by a constant; this allows us to bound the total sampling cost and to readily
verify the success of the procedure. We demonstrate numerically the application
of our algorithm on small toy models of the formaldimine molecule
(\ce{H2C=NH}).Comment: 15 + 10 pages, 4 figure
Assessing the potential of golf among university students to leverage SDG 3 in Planbelas : a consulting project
Mestrado Bolonha em ManagementThis consulting project was executed under the partnership of ISEG school of economics and
Planbelas, with the main goal of addressing Planbelas’ main concern, which was the potential of
profitability of Belas’ new plots of land. In order to disintegrate the case, the project focused on a
key component, which was the Assessment of the Potential of Golf Among University Students
to Leverage SDG 3 in Planbelas. To resolve this issue, both an internal and external analysis
were executed in Belas, comprising a SWOT analysis and the five forces of porter, where it was
possible to access the potential and further comprehend the on-going status of Belas.
The methodology of the project encompassed both interviews and surveys, where the interviews
were semi-structured. The surveys conducted were used solely to support the already available
data obtained from the interviews conducted, no deep analysis was conducted. The data was
analysed to make new observations and provide a more comprehensive insight of the consulting
project.
The data analysed reinforces the position that Belas targeting SDG 3 and making use of the golf
course to promote itself could also prove beneficial to university students. Golf being able to offer
advantages both physically and mentally, would give students a chance not only to socialize but
also to lead a healthy lifestyle. Therefore, Belas would be promoting both golf and a healthy
lifestyle, as socialisation.info:eu-repo/semantics/publishedVersio
Unpredictable Needs are Associated with Lower Expectations of Repayment
Sometimes people help one another expecting to be repaid, while at other times people help without an expectation of repayment. What might underlie this difference in expectations of repayment? We investigate this question in a nationally representative sample of US adults (N = 915), and find that people are more likely to expect repayment when needs are perceived to be more predictable. We then replicate these findings in a new sample of US adults (N = 417), and show that people have higher expectations of repayment when needs are perceived to be more predictable because people assign greater responsibility to others for experiencing such predictable needs (e.g., needing money for utilities). This is consistent with previous work based on smaller-scale societies, which shows that the predictability of needs influences expectations of repayment. Our results also add to this previous work by (1) showing that the positive relationship between predictability of needs and expectations of repayment previously found in smaller-scale communities is generalizable to the US population, and (2) showing that attributions of responsibility partially mediate this relationship. This work shows that the predictability of needs and attributions of responsibility for that need are important factors underlying the psychology of helping in times of need
Ausubel's meaningful learning re-visited
This review provides a critique of David Ausubel’s theory of meaningful learning and the use of advance organizers in teaching. It takes into account the developments in cognition and neuroscience which have taken place in the 50 or so years since he advanced his ideas, developments which challenge our understanding of cognitive structure and the recall of prior learning. These include (i) how effective questioning to ascertain previous knowledge necessitates in-depth Socratic dialogue; (ii) how many findings in cognition and neuroscience indicate that memory may be non-representational, thereby affecting our interpretation of student recollections; (iii) the now recognised dynamism of memory; (iv) usefully regarding concepts as abilities or simulators and skills; (v) acknowledging conscious and unconscious memory and imagery; (vi) how conceptual change involves conceptual coexistence and revision; (vii) noting linguistic and neural pathways as a result of experience and neural selection; and (viii) recommending that wider concepts of scaffolding should be adopted, particularly given the increasing focus on collaborative learning in a technological world
Grasping nothing: a study of minimal ontologies and the sense of music
If music were to have a proper sense – one in which it is truly given – one might reasonably place this in sound and aurality. I contend, however, that no such sense exists; rather, the sense of music takes place, and it does so with the impossible. To this end, this thesis – which is a work of philosophy and music – advances an ontology of the impossible (i.e., it thinks the being of what, properly speaking, can have no being) and considers its implications for music, articulating how ontological aporias – of the event, of thinking the absolute, and of sovereignty’s dismemberment – imply senses of music that are anterior to sound. John Cage’s Silent Prayer, a nonwork he never composed, compels a rerethinking of silence on the basis of its contradictory status of existence; Florian Hecker et al.’s Speculative Solution offers a basis for thinking absolute music anew to the precise extent that it is a discourse of meaninglessness; and Manfred Werder’s [yearn] pieces exhibit exemplarily that music’s sense depends on the possibility of its counterfeiting. Inso-much as these accounts produce musical senses that take the place of sound, they are also understood to be performances of these pieces. Here, then, thought is music’s organon and its instrument
Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction
Saliency Prediction aims to predict the attention distribution of human eyes
given an RGB image. Most of the recent state-of-the-art methods are based on
deep image feature representations from traditional CNNs. However, the
traditional convolution could not capture the global features of the image well
due to its small kernel size. Besides, the high-level factors which closely
correlate to human visual perception, e.g., objects, color, light, etc., are
not considered. Inspired by these, we propose a Transformer-based method with
semantic segmentation as another learning objective. More global cues of the
image could be captured by Transformer. In addition, simultaneously learning
the object segmentation simulates the human visual perception, which we would
verify in our investigation of human gaze control in cognitive science. We
build an extra decoder for the subtask and the multiple tasks share the same
Transformer encoder, forcing it to learn from multiple feature spaces. We find
in practice simply adding the subtask might confuse the main task learning,
hence Multi-task Attention Module is proposed to deal with the feature
interaction between the multiple learning targets. Our method achieves
competitive performance compared to other state-of-the-art methods
Cost effectiveness of difelikefalin compared to standard care for treating chronic kidney disease associated pruritus (CKD-aP) in people with kidney failure receiving haemodialysis
Background: Chronic kidney disease-associated pruritus (CKD-aP) is associated with an increased risk of depression, poor sleep and reduced health-related quality of life. Two phase III studies (KALM-1 and KALM-2) of difelikefalin showed reduced CKD-aP severity and improved itch-related health-related quality of life in patients with moderate and severe CKD-aP receiving haemodialysis for kidney failure. Objective: We aimed to estimate the cost effectiveness of difelikefalin for patients with CKD-aP receiving haemodialysis for kidney failure compared to standard care from a UK National Health Service perspective. Methods: A cohort model was developed with four health states representing levels of pruritus intensity over time, based on the KALM trials augmented with longer term CKD-aP severity data from another haemodialysis trial (SHAREHD) for standard care. Utilities were estimated from a mapping study of 5-D Itch to EQ-5D-5L in 487 patients receiving haemodialysis, costs were estimated based on resource use alongside the SHAREHD and 2018 unit costs, and inflated to 2021 costs. Costs and quality-adjusted life-years were discounted at 3.5% per annum. A de novo economic model was developed in Microsoft Excel with scenario analyses performed using a range of assumptions. Results: In the base-case analysis over a time horizon of 64 weeks, using a placeholder cost of £75 per 28-days for difelikefalin, the incremental cost-effectiveness ratio of difelikefalin compared with standard care was £19,558/quality-adjusted life-year (QALY). Scenario analyses resulted in incremental cost-effectiveness ratios that ranged from £10,154/QALY (severe only) to £16,957/QALY (5-year horizon) for difelikefalin compared to standard care. Probabilistic sensitivity analyses suggested difelikefalin has a 48.6% probability of being cost effective at a threshold of £20,000/QALY and a 57.2% probability of being cost effective at a threshold of £30,000/QALY. Conclusions: The cost effectiveness of difelikefalin in a range of scenarios could make it an important pharmacotherapy to address the high burden of disease and unmet need for treatments associated with CKD-aP in the UK
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
This paper focuses on the comparison of networks on the basis of statistical
inference. For that purpose, we rely on smooth graphon models as a
nonparametric modeling strategy that is able to capture complex structural
patterns. The graphon itself can be viewed more broadly as density or intensity
function on networks, making the model a natural choice for comparison
purposes. Extending graphon estimation towards modeling multiple networks
simultaneously consequently provides substantial information about the
(dis-)similarity between networks. Fitting such a joint model - which can be
accomplished by applying an EM-type algorithm - provides a joint graphon
estimate plus a corresponding prediction of the node positions for each
network. In particular, it entails a generalized network alignment, where
nearby nodes play similar structural roles in their respective domains. Given
that, we construct a chi-squared test on equivalence of network structures.
Simulation studies and real-world examples support the applicability of our
network comparison strategy.Comment: 25 pages, 6 figure
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