In this paper, we investigate a bitopological duality for algebras of Fitting's multi-valued logic. We also extend the natural duality theory for ISP I( L) by developing a duality for ISP(L), where L is a finite algebra in which underlying lattice is bounded distributive. Keywords: Bitopology, Fitting's logic, Natural duality theory. 1 Introduction Stone's pioneering work in the mid 1930 [19] on the dual equivalence between the category of Boolean algebras and homomorphism, and the category of Stone spaces(compact zero-dimensional Hausdorff spaces) and continuous maps, is being considered as the origin of duality theory. Stone further developed a general work [12] for the category of bounded distributive lattices in 1937. Priestley in 1970 [18] investigate another duality for the category of bounded distributive lattices with the help of ordered Stone spaces(known as Priesley spaces), which overcome difficulties in Stone's work [12].

Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data-view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that will inherit the strong statistical, mathematical and empirical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data-view that are best for determining the groups, often leading to improved integrative clustering. To fit our model, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Process Regression and TT-cross approximation technique. The proposed approach can be used in conjunction with any optimization algorithm that is usually utilized in tensor completion problems. We empirically justify that in this case the reconstruction error improves compared to the tensor completion with random initialization. As an additional benefit, our technique automatically selects rank thanks to using the TT-cross approximation technique.

Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test by an ensemble method that uses the sum of squared symmetry statistics and distance correlation. Simulation studies suggest that this method improves the power while preserving the clear interpretation of the binary expansion testing. We extend this method to tests of independence of random vectors in arbitrary dimension. By random projections, the proposed binary expansion randomized ensemble test transforms the multivariate independence testing problem into a univariate problem. Simulation studies and data example analyses show that the proposed method provides relatively robust performance compared with existing methods.

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment . This paper considers ea ch task comprised of two sequential subtasks: detection and completion, where e ach subtask can only be carried out by a certain type of agent . We address th is problem using a novel natur e - inspired approach called "hunter and gathere r" . Th e proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another dexterous in completing (gathere r s) the tasks . To minimize the collective cost of task accomplishments in a distributed manner, a game - theor etic solution is introduced to couple agents from complementary teams . We utiliz e market - based negotiation models to develop incentive - based decision - making algorithms rely ing on innovative notions of " certainty and uncertainty profit margins " . The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collec tive cost of accomplishments is minimized . In addition, t he stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively . It is also numerically show n that the proposed solution s function fairly, i.e. for each type of agent, the overall w orkload is distributed equally . Index Terms -- Distributed multiagent system, dynamic task allocation, game theory, negotiation. Multirobot systems are expected to undertake imperative roles in a wide variety of fields such as urban search and rescue (USAR) [1, 2], agricultural field operations [3], security patrols [4, 5], environmental monitoring [6], and industrial procedures [7] . Studies have shown that multi - robot systems have advantage over single - robot systems by offering more reliability, redundancy, and time efficiency when the nature of the tasks is inherently dist ributed [8] . Nonetheless, the problem of multi - robot task - allocation (MRTA) poses many critical challenges that has called for investigation in the past two decades [9 - 11] . In this regards, t he complexity of MRTA problems increases significantly in a dynamic environment, where the number and location of tasks are unknown for agents [12, 13] . Thus, robot s need to explore the environment to find tasks before accomplishing them.

Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying machine learning to human texts can extract deontological ethical reasoning about "right" and "wrong" conduct by calculating a moral bias score on a sentence level using sentence embeddings. The machine learned that it is objectionable to kill living beings, but it is fine to kill time; It is essential to eat, yet one might not eat dirt; it is important to spread information, yet one should not spread misinformation. However, the evaluated moral bias was restricted to simple actions -- one verb -- and a ranking of actions with surrounding context. Recently BERT ---and variants such as RoBERTa and SBERT--- has set a new state-of-the-art performance for a wide range of NLP tasks. But has BERT also a better moral compass? In this paper, we discuss and show that this is indeed the case. Thus, recent improvements of language representations also improve the representation of the underlying ethical and moral values of the machine. We argue that through an advanced semantic representation of text, BERT allows one to get better insights of moral and ethical values implicitly represented in text. This enables the Moral Choice Machine (MCM) to extract more accurate imprints of moral choices and ethical values.

My first look at Python was deliberate as I was following advice to learn the language from my mentor. Within a few hours of doing a deep dive into the language i got hooked and felt that the language was made for me. I made a decision that i would make Python my main language and put in all the work to understand it.My main resource when it came to Python Programming was Python's Documentation which i would advice any newbie to use.After months of intensive coding,I really good at Python that my friends and lecturers noticed, i familiarized myself with Python's frameworks;Django and Flask but i felt that this wasn't enough to make me a Python Guru.At this moment,I desperately needed to be good at Python. Oops,I stepped on Machine learning…. It was the beginning of a new semester,as part of our school curriculum we had to have project ideas for our third year.

Is my organisation a member? The Whitehall & Industry Group's AI Collaboration Forum will bring together a wide audience from our 230 members, spanning the private, public and not-for-profit sectors, as well as academic institutions. Supported by the Office for Artificial Intelligence and kindly hosted by EY. The agenda will explore the vital role of cross-sector collaboration to ensure the endless possibilities of AI are harnessed and regulated effectively, generating maximum positive economic and societal impacts for the UK. Holding a BSc in Computer Science and an MBA from the Massachusetts Institute of Technology, Sana Khareghani has over 20 years' experience in technology and business across the private and public sectors.

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