We briefly describe -- mainly through very simple examples -- different kinds of answer-set programs with annotations that have been proposed for specifying: database repairs and consistent query answering; secrecy view and query evaluation with them; counterfactual interventions for causality in databases; and counterfactual-based explanations in machine learning.

In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing 10 hand gestures, was used. The distinguishing power of the feature variables for the tested gestures was evaluated according to their p values obtained from the Kruskal-Wallis (KW) test. It is concluded that the mean, variance, skewness, and kurtosis of TF matrices can be candidate feature sets for the recognition of hand gestures.

We introduce a corpus of short texts in Mandarin, in which quantified expressions figure prominently. We illustrate the significance of the corpus by examining the hypothesis (known as Huang's "coolness" hypothesis) that speakers of East Asian Languages tend to speak more briefly but less informatively than, for example, speakers of West-European languages. The corpus results from an elicitation experiment in which participants were asked to describe abstract visual scenes. We compare the resulting corpus, called MQTUNA, with an English corpus that was collected using the same experimental paradigm. The comparison reveals that some, though not all, aspects of quantifier use support the above-mentioned hypothesis. Implications of these findings for the generation of quantified noun phrases are discussed.

We consider the problem of allocating a distribution of items to $n$ recipients where each recipient has to be allocated a fixed, prespecified fraction of all items, while ensuring that each recipient does not experience too much envy. We show that this problem can be formulated as a variant of the semi-discrete optimal transport (OT) problem, whose solution structure in this case has a concise representation and a simple geometric interpretation. Unlike existing literature that treats envy-freeness as a hard constraint, our formulation allows us to \emph{optimally} trade off efficiency and envy continuously. Additionally, we study the statistical properties of the space of our OT based allocation policies by showing a polynomial bound on the number of samples needed to approximate the optimal solution from samples. Our approach is suitable for large-scale fair allocation problems such as the blood donation matching problem, and we show numerically that it performs well on a prior realistic data simulator.

Applied mathematics and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch from their old paradigms to machine learning. Interestingly, those data scientists spend more time scrapping, annotating and cleaning data than fine-tuning models. This thesis is motivated by the following question: can we derive a more generic framework than the one of supervised learning in order to learn from clutter data? This question is approached through the lens of weakly supervised learning, assuming that the bottleneck of data collection lies in annotation. We model weak supervision as giving, rather than a unique target, a set of target candidates. We argue that one should look for an ``optimistic'' function that matches most of the observations. This allows us to derive a principle to disambiguate partial labels. We also discuss the advantage to incorporate unsupervised learning techniques into our framework, in particular manifold regularization approached through diffusion techniques, for which we derived a new algorithm that scales better with input dimension then the baseline method. Finally, we switch from passive to active weakly supervised learning, introducing the ``active labeling'' framework, in which a practitioner can query weak information about chosen data. Among others, we leverage the fact that one does not need full information to access stochastic gradients and perform stochastic gradient descent.

Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a key role in SPL but where to optimally terminate this process is still non-trivial to determine. A natural idea is to compute the solution path w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are either limited to the simplest regularizer, or lack solid theoretical understanding as well as computational efficiency. To address this challenge, we propose a novel \underline{G}eneralized \underline{Ag}e-path \underline{A}lgorithm (GAGA) for SPL with various self-paced regularizers based on ordinary differential equations (ODEs) and sets control, which can learn the entire solution spectrum w.r.t. a range of age parameters. To the best of our knowledge, GAGA is the first exact path-following algorithm tackling the age-path for general self-paced regularizer. Finally the algorithmic steps of classic SVM and Lasso are described in detail. We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines.

Recently, (Rissanen et al., 2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise. In making this connection, we bridge the gap between inverse heat dissipation and denoising diffusion, and we shed light on the inductive bias that results from this modeling choice. Finally, we propose a generalized class of diffusion models that offers the best of both standard Gaussian denoising diffusion and inverse heat dissipation, which we call Blurring Diffusion Models. Diffusion models are becoming increasingly successful for image generation, audio synthesis and video generation. Diffusion models define a (stochastic) process that destroys a signal such as an image.

This article grants a comprehensive understanding of the applications and prospects of AI Blockchain technology in smart cities. I have researched and compiled information regarding the Internet of things (IoT) technologies for smart cities, smart cities as innovation ecosystems sustained by the future internet, traffic management by automation of street lights, and the future of waste management in sustainable cities. The implementation of these technologies has shown a decrease in carbon emissions, traffic, and error in recycling systems. Blockchain applications in smart cities have been extensively researched in order to clearly explain the immense influence this technology will have on all future data-related activities. This includes but is not limited to, governments, banks, hospitals, civilian services, and energy trades. The literature compiled gives a closer understanding of how AI blockchain is a viable option for batching and automating the analysis of large data packets, to allow for streamlined damage control and a massive reduction in outbreaks. The material showcases how a city's eco-system could benefit and sustain itself using these innovative IT solutions. It also gives insight into how individuals could begin profiting off tangible assets once these technologies are implemented. As the populace in cities becomes considerably denser, governments and civil engineers are looking for more modern solutions for monitoring city operations such as traffic, waste management, and air quality.

AI thrives on the processing of large volumes of data to be able to deliver focused and targeted solutions. Last year in April, the European Commission (EC) unveiled a legal framework for AI, the Artificial Intelligence Act (AI Act), the first of its kind. The AI Act aims to implement an ecosystem of trust by proposing a legal framework within which people use AI-based solutions while encouraging businesses to develop them. When it comes to technology, Europe has made no secret of its desire to export its values across the world, at least at a principle level. Similar to the General Data Protection Regulation (GDPR), which has become the conventional norm, the AI Act could also become a global precedent, determining to what extent AI may seep into our general day-to-day functioning, or whether it will be limited to automated use by larger entities only.

The ongoing revolution in artificial intelligence (AI)--in image recognition, natural language processing and translation, and much more--has been driven by neural networks, specifically many-layer versions known as deep learning. These systems have well-known weaknesses, but their capability continues to grow, even as they demand ever more data and energy. At the same time, other critical applications need much more than just powerful pattern recognition, and deep learning does not provide the sorts of performance guarantees that are customary in computer science. To address these issues, some researchers favor combining neural networks with older tools for artificial intelligence. In particular, neurosymbolic AI incorporates the long-studied symbolic representation of objects and their relationships.