Keyword spotting in edge devices is becoming increasingly important as voice-activated assistants are widely used. However, its deployment is often limited by the extreme low-power constraints of the target embedded systems. Here, we explore the Temporal Difference Encoder (TDE) performance in keyword spotting. This recent neuron model encodes the time difference in instantaneous frequency and spike count to perform efficient keyword spotting with neuromorphic processors. We use the TIdigits dataset of spoken digits with a formant decomposition and rate-based encoding into spikes. We compare three Spiking Neural Networks (SNNs) architectures to learn and classify spatio-temporal signals. The proposed SNN architectures are made of three layers with variation in its hidden layer composed of either (1) feedforward TDE, (2) feedforward Current-Based Leaky Integrate-and-Fire (CuBa-LIF), or (3) recurrent CuBa-LIF neurons. We first show that the spike trains of the frequency-converted spoken digits have a large amount of information in the temporal domain, reinforcing the importance of better exploiting temporal encoding for such a task. We then train the three SNNs with the same number of synaptic weights to quantify and compare their performance based on the accuracy and synaptic operations. The resulting accuracy of the feedforward TDE network (89%) is higher than the feedforward CuBa-LIF network (71%) and close to the recurrent CuBa-LIF network (91%). However, the feedforward TDE-based network performs 92% fewer synaptic operations than the recurrent CuBa-LIF network with the same amount of synapses. In addition, the results of the TDE network are highly interpretable and correlated with the frequency and timescale features of the spoken keywords in the dataset. Our findings suggest that the TDE is a promising neuron model for scalable event-driven processing of spatio-temporal patterns.

Complex networks, such as those in social, biological, and technological systems, often present challenges to the task of community detection. Our research introduces a novel rough clustering based consensus community framework (RC-CCD) for effective structure identification of network communities. The RC-CCD method employs rough set theory to handle uncertainties within data and utilizes a consensus clustering approach to aggregate multiple clustering results, enhancing the reliability and accuracy of community detection. This integration allows the RC-CCD to effectively manage overlapping communities, which are often present in complex networks. This approach excels at detecting overlapping communities, offering a detailed and accurate representation of network structures. Comprehensive testing on benchmark networks generated by the Lancichinetti-Fortunato-Radicchi method showcased the strength and adaptability of the new proposal to varying node degrees and community sizes. Cross-comparisons of RC-CCD versus other well known detection algorithms outcomes highlighted its stability and adaptability.

Violet Crown City Church Pastor Jay Cooper said that using AI to conduct a service at his church did not capture the essential elements required for Christian worship. A prominent Black megachurch in New York City is being accused of discriminating against a woman who lost her bid to become its senior pastor. Yale Divinity School Professor Eboni Marshall Turman filed a lawsuit against Abyssinian Baptist Church alleging she was rejected from the final round of candidates applying to lead the church after the death of Rev. Calvin O. Butts III in 2022. Marshall Turman previously served as the late reverend's assistant and was the church's youngest female Assistant Minister from 2002-2012. In her Dec. 29 lawsuit, she accuses the church and search committee chair Valerie S. Grant of acting inappropriately by "pressing issues not broached with [Marshall Turman's] male counterparts" during the interview process, the Associated Press reported.

This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch. The resulting branched VAE (BVAE) contributes a classification component based on the class labels to the total loss and therefore imparts categorical information to the latent representation. As a result, the latent space distributions of the input classes are separated and ordered, thereby enhancing the classification accuracy. The degree of improvement is quantified by numerical calculations employing the benchmark MNIST dataset for both unrotated and rotated digits. The proposed technique is then compared to and then incorporated into a VAE with fixed output distributions. This procedure is found to yield improved performance for a wide range of output distributions.

Artificial Intelligence (AI) is already revolutionising the way we work and conduct research, and its future impact is challenging to predict. Concerning qualitative content analysis, recent studies demonstrate its usefulness for coding research interviews, a fundamental tool for data collection across numerous academic disciplines (Lopezosa and Codina, 2023; Zhang et al., 2023). However, its use is incipient and there are still not many experiences in the scientific literature, despite the need to analyse and closely monitor the development of tools with the potential to bring about such profound changes. Consequently, this paper illustrates its practical application in a real case where interviews were initially manually coded using expert criteria. These interviews were carried out as part of a broader study aimed at evaluating the changes in landscape quality that occurred on a small island in Cuba (Cayo Santa María) as a result of tourism development (Burgui et al., 2018).

Military and weapons researchers have developed microwave guns and lasers that can be used to disable soldiers or shoot down drones -- but a new report from the CIA and other intelligence agencies say that these kinds of weapons probably aren't responsible for the condition known as Havana Syndrome. When US personnel overseas began suffering from unexplained headaches, nausea and hearing problems in 2016, many were quick to suspect foul play by a foreign adversary. A panel of experts concluded that the anomalous health incidents that came to be known as Havana Syndrome could plausibly have been caused by "pulsed electromagnetic energy," prompting some of those afflicted with the condition to blame their symptoms on a mysterious new energy weapon, possibly wielded by Russian operatives. Now, seven intelligence agencies say that panel got it wrong. The Washington Post reports that even after reviewing about 1,000 cases across the world, the CIA and half a dozen agencies concluded that it was unlikely that the symptoms were caused by a foreign adversary.

With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read. Recently, the rise of neural fake news (AI-generated fake news) and its demonstrated effectiveness at fooling humans has prompted the development of models to detect it. One such model is the Grover model, which can both detect neural fake news to prevent it, and generate it to demonstrate how a model could be misused to fool human readers. In this work we explore the Grover model's fake news detection capabilities by performing targeted attacks through perturbations on input news articles. Through this we test Grover's resilience to these adversarial attacks and expose some potential vulnerabilities which should be addressed in further iterations to ensure it can detect all types of fake news accurately.

In machine learning, there is a workflow that involves working with data, creating models, optimizing model parameters, and saving training models. These frameworks produced the best practices for implementing a deep learning framework. With the help of Pytorch, an ML framework based on the Torch library, tensors allow for multidimensional rectangular arrays to operate on CUBA-capable Nvidia GPUs. In this tutorial basics, we will go into the FashioMINST dataset to train neural networks that the product of an input image belongs in a class. Now that you have the correct installations and Pytorch installed, you can now start the tutorial process.

When OpenAI first debuted its powerful GPT-3 natural language model in June of 2020, it debuted in a limited beta capacity and featured a waiting list where developers could sign up to use its infrastructure and capabilities. Now, the waiting list has been dropped and GPT-3's capabilities are immediately available to developers and enterprises to work on their most challenging language problems, according to a Nov. 18 (Thursday) announcement by OpenAI, an independent AI research and deployment company. But there are some caveats – the general release adds conditions to prevent GPT-3 from being used to harm people, as well as conditions that only allow its use in certain nations around the world. That means that developers in some nations, including Cuba, Iran and Russia, cannot currently access it. "OpenAI is committed to the safe deployment of AI," the organization said in a statement.

I gotta admit: I was already exhausted by the baggage of Far Cry 6 before I even picked up the controller to review it. Having played every game in the franchise to date --while following the near decade-long discourse critiquing its flawed politics, ideological cowardice, and colonialist mindset -- I admittedly came in with some assumptions about what to expect. I expected Far Cry 6 to be (like nearly every other recent title in the franchise) dumb, mindless, well-polished AAA fun, with a vapid story that uses the aesthetics of real-world issues to overinflate its own sense of self-importance. Yet to my utter shock, Far Cry 6 flipped nearly every one of those expectations on its head -- at times to its benefit but more often to its detriment. I'm not prepared to call Far Cry 6 a great game by any means. As far as gameplay, it's actually one of the least polished Ubisoft titles I've played in a while.