This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual's likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.

Approaches to audio classification and retrieval tasks largely rely on detectionbased discriminative models. We submit that such models make a simplistic assumption in mapping acoustics directly to semantics, whereas the actual process is likely more complex. We present a generative model that maps acoustics in a hierarchical manner to increasingly higher-level semantics. Our model has two layers with the first layer modeling generalized sound units with no clear semantic associations, while the second layer models local patterns over these sound units. We evaluate our model on a large-scale retrieval task from TRECVID 2011, and report significant improvements over standard baselines.

In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we propose autoencoder (AE)-based joint optimization of both preamble generation networks (PGNs) in the encoder side and data-aided AUD in the decoder side. The core architecture of the proposed AE is a novel user activity extraction network (UAEN) in the decoder that extracts a priori user activity information from the SCMA codeword data for the data-aided AUD. An end-to-end training of the proposed AE enables joint optimization of the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and SCMA-based data transmission. Furthermore, we propose a self-supervised pre-training scheme for the UAEN prior to the end-to-end training, to ensure the convergence of the UAEN which lies deep inside the AE network. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of $\bf{{10}^{-3}}$ compared to the state-of-the-art DL-based AUD schemes.

A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize unlabeled images from unseen classes (i.e transductive zero-shot) during training to enable generalization. However, this limits their use in practical scenarios where data from target unseen classes is unavailable or infeasible to collect. In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning. We leverage a formulation based on product-of-experts and introduce a new AUD module that enables us to use unlabeled samples from out-of-data classes which are usually easily available and practically entail no annotation cost. In addition, we also demonstrate the applicability of our model to address a more practical and challenging, Generalized Zero-shot under a limited supervision setting, where even base seen classes do not have sufficient annotated samples.

A novel general intelligence model is proposed with three types of learning. A unified sequence of the foreground percept trace and the command trace translates into direct and time-hop observation paths to form the basis of Raw learning. Raw learning includes the formation of image-image associations, which lead to the perception of temporal and spatial relationships among objects and object parts; and the formation of image-audio associations, which serve as the building blocks of language. Offline identification of similar segments in the observation paths and their subsequent reduction into a common segment through merging of memory nodes leads to Generalized learning. Generalization includes the formation of interpolated sensory nodes for robust and generic matching, the formation of sensory properties nodes for specific matching and superimposition, and the formation of group nodes for simpler logic pathways. Online superimposition of memory nodes across multiple predictions, primarily the superimposition of images on the internal projection canvas, gives rise to Innovative learning and thought. The learning of actions happens the same way as raw learning while the action determination happens through the utility model built into the raw learnings, the utility function being the pleasure and pain of the physical senses.

Each course is listed as taking about 1-month. Five-months in total means $250 USD. If you're fast enough, you could get it done in 1-month. I managed to get it done in three. I could've finished in under two months, however, life.

An anti-drone system developed by a trio of UK companies is to receive its first public test by America's Federal Aviation Authority (FAA), in an effort to protect airports from the risks of hobbyist's unmanned aerial vehicles. The system, called the Anti-UAV Defence System (Auds), looks like a mounted turret but instead of shooting drones out of the sky with bullets, it fires nothing more menacing than radio waves. Auds has three barrels of descending sizes, which act as a set of directional radio antennas. The portion of the radio spectrum used by drones is narrow, and so a short, loud (in electromagnetic terms) blast of energy is enough to completely prevent the drone from being able to communicate with its controller. Conventional multidirectional jamming systems work on drones, but have the downside of preventing the use of anything else in the area that relies on the same portion of the radio spectrum, such as mobile phones.

Approaches to audio classification and retrieval tasks largely rely on detectionbased discriminativemodels. We submit that such models make a simplistic assumption inmapping acoustics directly to semantics, whereas the actual process is likely more complex. We present a generative model that maps acoustics in a hierarchical manner to increasingly higher-level semantics. Our model has two layers with the first layer modeling generalized sound units with no clear semantic associations, while the second layer models local patterns over these sound units. We evaluate our model on a large-scale retrieval task from TRECVID 2011, and report significant improvements over standard baselines.