This study explores the problem of Multi-Agent Path Finding with continuous and stochastic travel times whose probability distribution is unknown. Our purpose is to manage a group of automated robots that provide package delivery services in a building where pedestrians and a wide variety of robots coexist, such as delivery services in office buildings, hospitals, and apartments. It is often the case with these real-world applications that the time required for the robots to traverse a corridor takes a continuous value and is randomly distributed, and the prior knowledge of the probability distribution of the travel time is limited. Multi-Agent Path Finding has been widely studied and applied to robot management systems; however, automating the robot operation in such environments remains difficult. We propose 1) online re-planning to update the action plan of robots while it is executed, and 2) parameter update to estimate the probability distribution of travel time using Bayesian inference as the delay is observed. We use a greedy heuristic to obtain solutions in a limited computation time. Through simulations, we empirically compare the performance of our method to those of existing methods in terms of the conflict probability and the actual travel time of robots. The simulation results indicate that the proposed method can find travel paths with at least 50% fewer conflicts and a shorter actual total travel time than existing methods. The proposed method requires a small number of trials to achieve the performance because the parameter update is prioritized on the important edges for path planning, thereby satisfying the requirements of quick implementation of robust planning of automated delivery services.

Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state. Recent Transformers-based summarization models have presented a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal with more complicated tasks such as novel word generation and sentence paraphrasing. Nonetheless, these models have a prominent shortcoming; their training strategy is not quite efficient, which restricts the model's performance. In this paper, we include a curriculum learning approach to reweigh the training samples, bringing about an efficient learning procedure. We apply our model on extreme summarization dataset of MentSum posts -- a dataset of mental health related posts from Reddit social media. Compared to the state-of-the-art model, our proposed method makes substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding 3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative improvements.

We study a deep linear network endowed with a structure. It takes the form of a matrix $X$ obtained by multiplying $K$ matrices (called factors and corresponding to the action of the layers). The action of each layer (i.e. a factor) is obtained by applying a fixed linear operator to a vector of parameters satisfying a constraint. The number of layers is not limited. Assuming that $X$ is given and factors have been estimated, the error between the product of the estimated factors and $X$ (i.e. the reconstruction error) is either the statistical or the empirical risk. In this paper, we provide necessary and sufficient conditions on the network topology under which a stability property holds. The stability property requires that the error on the parameters defining the factors (i.e. the stability of the recovered parameters) scales linearly with the reconstruction error (i.e. the risk). Therefore, under these conditions on the network topology, any successful learning task leads to stably defined features and therefore interpretable layers/network.In order to do so, we first evaluate how the Segre embedding and its inverse distort distances. Then, we show that any deep structured linear network can be cast as a generic multilinear problem (that uses the Segre embedding). This is the {\em tensorial lifting}. Using the tensorial lifting, we provide necessary and sufficient conditions for the identifiability of the factors (up to a scale rearrangement). We finally provide the necessary and sufficient condition called \NSPlong~(because of the analogy with the usual Null Space Property in the compressed sensing framework) which guarantees that the stability property holds. We illustrate the theory with a practical example where the deep structured linear network is a convolutional linear network. As expected, the conditions are rather strong but not empty. A simple test on the network topology can be implemented to test if the condition holds.

Hudson Institute senior fellow Michael Pillsbury tells "Fox News @ Night" that war games conducted by the Center for Strategic and International Studies on a Chinese invasion of Taiwan is "all the more reason to try to deter" an invasion. You Si-kun, the speaker of Taiwan's Parliament, spoke at the International Religious Freedom Summit on Wednesday and voiced why it is important that free nations protect Taiwan. "If Taiwan falls into the sphere of influence of CCP (Chinese Communist Party), then the beacon of democracy will be destroyed. And China may invade the first island chain and will cause a threat to the entire world," he said. You Si-kun, the speaker of Taiwan's Parliament, addresses the International Religious Freedom Summit in Washington, D.C. (IRF Summit / Matt Rybczynski) Freedom House's 2022 Freedom in the World report ranked Taiwan a perfect score of 4 concerning religious freedom.

If you're not interested in where these images came from, simply scroll past the all the text to view them. Start reading here or watch me make some AI art with OpenAI's DALL·E 2 in the video below. Similar to DALL·E 2, Midjourney is an AI art generator that takes text prompts as input and generates four images per prompt as output. In order to use it, you need a Discord account connected to the Midjourney bot. While this was annoying and took me 10 minutes to set up, I think it's worth the effort. To try it out, you'll type the command /imagine and then enter your prompts directly into a Midjourney newbies channel for free. The downside of the free version is that you get only 25 prompts and you'll keep your eyes glued to the screen while your creations flash by and are lost in a chaotic jumble of results from everyone else who's on the channel with you. Any art you make is also posted publicly on their website under your username and you may use your images under a Creative Commons license as long as you cite Midjourney as the source. There's a lot of joy you can get out of 25 free prompts, but do take a bit of time to see what comes out of other people's prompts so you get the hang of some "prompt engineering" (how to phrase your prompt to get the result you want) before you start, else you might waste your 25 opportunities.

Innov8 Hub in collaboration with the Nigerian Communication Commission, hosted the maiden edition of the Nigerian Girls Can Code competition. The competition was created for girls in secondary schools across all geopolitical zones in Nigeria. It is strictly made for Nigerian girls interested in the practice and theory of Robotics & Coding. At the opening event, the Communications Advisor at Innov8 Hub, Mr. Deji Ige, gave the opening remark, motivating the girls to achieve the best and dare the impossible. The representative of the NCC, Chinwe Maduabum, welcomed all the participants and their mentors to the competition.

When we started AI Expo Africa here in South Africa back in 2018, it would be fair to say the atmosphere was one of excitement with a fair degree of hype mixed with solid doses of reality. There was still talk of "AI Winters" and that adoption would be slow. Well, 5 years on, the landscape has radically changed. Tools and techniques that were once the exclusive domain of "the developer" are now freely accessible via zero cost platforms / apps / APIs allowing business users to leverage all kinds of AI related tech, be that AI generated presentations or logos, to art, videos, music and animations to name but a few. Even in the time we have been running the show, the creativity and use cases have exploded and it would be fair to say, we are now well into the AI Spring!

Across the developing world, more children than ever are in school – but they are not learning. A recent study estimated that less than 1% of school children in Sub-Saharan Africa attend a school where the teaching meets basic standards of quality. At Rising Academies, we're changing that, and we want your help. We are a growing network of inspiring schools in West Africa. Our mission is to unleash the full potential of every student, equipping them with the knowledge, skills, and character to succeed in further study, work, and day-to-day life.

Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Supporting code available at https://github.com/swarnaHub/SummarizationPrograms

Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac Subaveerapandiyan A Ph.D. Research Scholar Department of Library and Information Science Yenepoya (Deemed to be University), Mangalore, Karnataka, India Email: subaveerapandiyan@gmail.com ORCiD: https://orcid.org/0000-0002-2149-9897 Abstract This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept it. The study is beneficial for academicians, content developers, and librarians. Keywords: Conversational Generative Pre-training Transformer (ChatGPT), Artificial Intelligence in Academia, Academic Writing with ChatGPT, Library Services Introduction The OpenAI-developed GPT (Generative Pre-trained Transformer) model has a variation called ChatGPT. The GPT model was initially released in 2018 and trained using the Common Crawl, a sizable dataset of text from the internet. The Transformer design, revealed in a 2017 study by Google researchers, served as the model's foundation. Unsupervised learning was used to train the initial GPT model, which meant that it was trained on a sizable text dataset without any explicit labels or annotations.