This research aims to develop machine learning models for students academic performance and study strategies prediction which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models. Determining the broad effect of these attributes on students' academic performance and study strategy is the center of our interest. To investigate this, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for this problem, the importance of pre-processing medical images, the use of transfer learning and data augmentation techniques as tools to reduce the effects of the problem of having too little data, and the simultaneous use of multiple medical imaging modalities. We also propose the application of an evaluation method that guarantees a good degree of repeatability of the results even when using a small dataset. Following this evaluation method, our best final model, which makes use of transfer learning with COVID-19 data, achieves an accuracy d 68\%. In addition, in an independent test set, this same model achieves 70\% accuracy, a promising result given the small size of our dataset. We further conclude that augmenting the depth of the networks helps with this problem, that image pre-processing is a fundamental process to address this type of medical problem, and that the use of data augmentation and the use of pre-trained networks with images of other diseases can provide significant improvements.

The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that captures both local and global features. However, through a series of systematic studies, we find that the Conformer architecture's design choices are not optimal. After re-examining the design choices for both the macro and micro-architecture of Conformer, we propose Squeezeformer which consistently outperforms the state-of-the-art ASR models under the same training schemes. In particular, for the macro-architecture, Squeezeformer incorporates (i) the Temporal U-Net structure which reduces the cost of the multi-head attention modules on long sequences, and (ii) a simpler block structure of multi-head attention or convolution modules followed up by feed-forward module instead of the Macaron structure proposed in Conformer. Furthermore, for the micro-architecture, Squeezeformer (i) simplifies the activations in the convolutional block, (ii) removes redundant Layer Normalization operations, and (iii) incorporates an efficient depthwise downsampling layer to efficiently sub-sample the input signal. Squeezeformer achieves state-of-the-art results of 7.5%, 6.5%, and 6.0% word-error-rate (WER) on LibriSpeech test-other without external language models, which are 3.1%, 1.4%, and 0.6% better than Conformer-CTC with the same number of FLOPs. Our code is open-sourced and available online [25].

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities. To guarantee effective knowledge injection, previous studies integrate models with knowledge encoders for representing knowledge retrieved from knowledge graphs. The operations for knowledge retrieval and encoding bring significant computational burdens, restricting the usage of such models in real-world applications that require high inference speed. In this paper, we propose a novel KEPLM named DKPLM that Decomposes Knowledge injection process of the Pre-trained Language Models in pre-training, fine-tuning and inference stages, which facilitates the applications of KEPLMs in real-world scenarios. Specifically, we first detect knowledge-aware long-tail entities as the target for knowledge injection, enhancing the KEPLMs' semantic understanding abilities and avoiding injecting redundant information. The embeddings of long-tail entities are replaced by "pseudo token representations" formed by relevant knowledge triples. We further design the relational knowledge decoding task for pre-training to force the models to truly understand the injected knowledge by relation triple reconstruction. Experiments show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. We further show that DKPLM has a higher inference speed than other competing models due to the decomposing mechanism.

How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How can we perform both tasks in-house, i.e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other? Presenting outliers in separate micro-clusters is informative to analysts in many real-world applications. However, a na\"ive solution based on post-hoc clustering of the outliers detected by any existing method suffers from two main drawbacks: (a) appropriate hyperparameter values are commonly unknown for clustering, and most algorithms struggle with clusters of varying shapes and densities; (b) detection and assignment cannot benefit from one another. In this paper, we propose D.MCA to $\underline{D}$etect outliers with explicit $\underline{M}$icro-$\underline{C}$luster $\underline{A}$ssignment. Our method performs both detection and assignment iteratively, and in-house, by using a novel strategy that prunes entire micro-clusters out of the training set to improve the performance of the detection. It also benefits from a novel strategy that avoids clustered outliers to mask each other, which is a well-known problem in the literature. Also, D.MCA is designed to be robust to a critical hyperparameter by employing a hyperensemble "warm up" phase. Experiments performed on 16 real-world and synthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art competitors, especially on the explicit outlier micro-cluster assignment task.

Modeling galaxy-galaxy strong lensing observations A particular challenge with fully exploiting observations presents a number of challenges as the exact configuration of galaxy-galaxy strong gravitational lenses -- where an of both the background source and foreground extended background source is lensed by a foreground lens galaxy is unknown. A timely call, galaxy -- is that of accounting for the complex morphologies prompted by a number of upcoming surveys anticipating of lensed galaxies. Although sources in low-resolution high-resolution lensing images, demands images can be adequately modeled using phenomenological methods that can efficiently model lenses at their parameterizations such as one or several Sérsic profiles full complexity. In this work, we introduce a (Sérsic, 1963), this approach is inadequate for modeling method that uses continuous neural fields to nonparametrically higher-fidelity lensing observations such as those from ongoing, reconstruct the complex morphology upcoming, and proposed telescopes like the Hubble of a source galaxy while simultaneously inferring Space Telescope (HST), JWST, Euclid, and the Extremely a distribution over foreground lens galaxy Large Telescope (ELT). The development of new methods is configurations. We demonstrate the efficacy of especially timely, given the large number of high-resolution our method through experiments on simulated lenses that are expected to be imaged by next-generation data targeting high-resolution lensing images similar cosmological surveys (Collett, 2015) and their potential to to those anticipated in near-future astrophysical weigh in on the nature of dark matter (Simon et al., 2019).

Zoltan Istvan is a former journalist, political candidate, entrepreneur, bestselling author, and founder of the US Transhumanist Party. He has been on this podcast twice before when we discussed Istvan's presidential campaign and his bestselling novel The Transhumanist Wager. During this 1-hour conversation with Zoltan Istvan, we cover a variety of interesting topics such as the challenge of doing graduate school at Oxford, Quantum Archaeology; Trump, transhumanism, politics, and conflict; the Immortality or Bust documentary; microchipping refugees and selling off public lands; the ethics of doing damage now in the hope of fixing it later; technosolutionism and why Technology is Not Enough; longevity, entrepreneurship, and healthcare; the distinction between a body with a brain vs a brain with a body; the timeline to AGI, mind-uploading and indefinite life extension. As always you can listen to or download the audio file above or scroll down and watch the video interview in full. To show your support you can write a review on iTunes, make a direct donation, or become a patron on Patreon.

Aliens, astronauts, time travel--you name it, there's a dazzling sci-fi film about it. That makes compiling a list of the best sci-fi nearly impossible. It's almost impossible to know where to start--or where to stop. To understand where sci-fi films came from, you need to head back to the dawn of the cinema age. Right at the beginning, Metropolis, released in 1927, used groundbreaking visuals to create a reference point for all future urban dystopias--it's no fluke, for example, that the aesthetic of Blade Runner bears more than a passing resemblance to Fritz Lang's prophetic city hellscape. Then along came War of the Worlds (1953), a gripping tale of alien invasion adapted from H. G. Wells' classic novel. In 1964, Dr. Strangelove did more than most films before or since to ossify the fear of a nuclear holocaust. Below is WIRED's ever-evolving selection of the sci-fi movies everyone should watch, from the obscure to the hugely influential. You may also enjoy our guides to the best sci-fi books of all time and the best space movies. This content can also be viewed on the site it originates from. When Alfonso Cuarón wrote the screenplay for Gravity, he wasn't setting out to make a film about space itself. Rather, he was interested in exploring the concepts of adversity and human resilience, with space as a secondary background. But it was hard for audiences to not be wowed by the visuals in this Oscar-winning film about two scientists (George Clooney and Sandra Bullock) who find themselves stranded in space, and what they must endure in order to get safely back to Earth.

Humans have an innate ability to sense their surroundings, as they can extract the spatial representation from the egocentric perception and form an allocentric semantic map via spatial transformation and memory updating. However, endowing mobile agents with such a spatial sensing ability is still a challenge, due to two difficulties: (1) the previous convolutional models are limited by the local receptive field, thus, struggling to capture holistic long-range dependencies during observation; (2) the excessive computational budgets required for success, often lead to a separation of the mapping pipeline into stages, resulting the entire mapping process inefficient. To address these issues, we propose an end-to-end one-stage Transformer-based framework for Mapping, termed Trans4Map. Our egocentric-to-allocentric mapping process includes three steps: (1) the efficient transformer extracts the contextual features from a batch of egocentric images; (2) the proposed Bidirectional Allocentric Memory (BAM) module projects egocentric features into the allocentric memory; (3) the map decoder parses the accumulated memory and predicts the top-down semantic segmentation map. In contrast, Trans4Map achieves state-of-the-art results, reducing 67.2% parameters, yet gaining a +3.25% mIoU and a +4.09% mBF1 improvements on the Matterport3D dataset. Code at: https://github.com/jamycheung/Trans4Map.