Online Learning of Facility Locations

In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications. In our formulation, we are given a set of sites and an online sequence of user requests. At each trial, the learner selects a subset of sites and then incurs a cost for each selected site and an additional cost which is the price of the user’s connection to the nearest site in the selected subset. The problem may be solved by an application of the well-known Hedge algorithm. This would, however, require time and space exponential in the number of the given sites, which motivates our design of a novel quasi-linear time algorithm for this problem, with good theoretical guarantees on its performance

Learning Augmented Online Facility Location

Following the research agenda initiated by Munoz & Vassilvitskii [1] and Lykouris & Vassilvitskii [2] on learning-augmented online algorithms for classical online optimization problems, in this work, we consider the Online Facility Location problem under this framework. In Online Facility Location (OFL), demands arrive one-by-one in a metric space and must be (irrevocably) assigned to an open facility upon arrival, without any knowledge about future demands. We present an online algorithm for OFL that exploits potentially imperfect predictions on the locations of the optimal facilities. We prove that the competitive ratio decreases smoothly from sublogarithmic in the number of demands to constant, as the error, i.e., the total distance of the predicted locations to the optimal facility locations, decreases towards zero. We complement our analysis with a matching lower bound establishing that the dependence of the algorithm's competitive ratio on the error is optimal, up to constant factors. Finally, we evaluate our algorithm on real world data and compare our learning augmented approach with the current best online algorithm for the problem

Teachers' Experience of Teaching and Online Learning Via WhatsApp as a Combination of Interactive English Learning media in the Covid-19 Pandemic Era of UNU Lab Elementary School Students in Blitar

English learning in elementary schools should be engaging, interactive and fun so that students have an interest and motivation for learning especially in the current Covid-19 pandemic, but in reality, the learning patterns in SD Lab Blitar UNU still do not apply English learning patterns that are interactive and fun online because teachers have not affected the learning model that suits students' needs. They find it difficult to determine the right online media as a learning medium due to locations that do not support using the full online facility. The purpose of this study is that the authors intend to provide solutions in formulating exciting and interactive patterns of English teaching to all teachers and students during the co-19 pandemic under online learning conditions. The author uses a qualitative approach through the case study application and applies self-selection to select audiences and conduct online interviews to retrieve all data in the completeness of the study. The results revealed that Teachers' Experience of Teaching and Online Learning Via WhatsApp could create Interactive English Learning in the Covid-19 Pandemic Era, and the Combination can attract students' interest in learning online

VIRTUAL EXPERIENCE, REAL LEARNING

BACKGROUND Students rarely appreciate the interdisciplinarity of medical sciences or reflect upon broader applications of their studies. COVID-19 forced us to develop online learning experiences that are authentic, engaging and scalable because our 540 first-year Human Biology students see the Medical Science program only as a gateway to studying Medicine. Our aim was to explore an environment that challenged their perceptions of medical science. DESCRIPTION OF INTERVENTION We used a Ricoh Theta Z1 360°-camera and 3DVista© software to create a virtual tour of the Chau Chak Wing Museum, a facility co-locating The University of Sydney’s collections. The tour included mummified remains, the Jericho skull, and an anatomical clastic model, but also taxidermised thylacine, extant animals, and items of anthropologic interest. Local and remote students had identical experience of the collections. RESULTS Students engaged positively with an activity that was not demanding. Most reflected new appreciation of how historical models advance our understanding, especially with response to disease. Many also expressed a desire to visit the museum in person. CONCLUSIONS 3D virtual experiences are increasingly popular for asynchronous learning, including remote/hazardous locations and workplace induction. This is a relatively inexpensive, easily developed and deployed option for online teaching in medical sciences, especially in laboratory demonstrations

Classifiers With a Reject Option for Early Time-Series Classification

Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the "agreement" of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives

Reallocating Multiple Facilities on the Line

We study the multistage $K$-facility reallocation problem on the real line, where we maintain $K$ facility locations over $T$ stages, based on the stage-dependent locations of $n$ agents. Each agent is connected to the nearest facility at each stage, and the facilities may move from one stage to another, to accommodate different agent locations. The objective is to minimize the connection cost of the agents plus the total moving cost of the facilities, over all stages. $K$-facility reallocation was introduced by de Keijzer and Wojtczak, where they mostly focused on the special case of a single facility. Using an LP-based approach, we present a polynomial time algorithm that computes the optimal solution for any number of facilities. We also consider online $K$-facility reallocation, where the algorithm becomes aware of agent locations in a stage-by-stage fashion. By exploiting an interesting connection to the classical $K$-server problem, we present a constant-competitive algorithm for $K = 2$ facilities

New Jersey's Growing Remote Workforce and the Skill Requirements of Employers

Highlights factors driving the rise in remote work jobs, the ways remote work is affecting the workplace, and the skills workers need to be effective in remote work environments

Breaking Down Barriers: An Evaluation of Parent Engagement In School Closures and Co-Locations

The Department of Education's ("Department") decisions to close or co-locate schools frequently involves the loss of critical space and programs, which can have serious impacts on students' education. Historically, in making these decisions the Department has a poor track record of soliciting and incorporating parental and community input. Despite new parental engagement procedures added to the law in 2009 to facilitate greater parental consultation in major school change decisions, this year's story does not seem to be markedly different. The Department treated these hearings as procedural hurdles in order to satisfy the letter of the law, rather than an opportunity to engage in a productive dialogue about the impacts of proposed school closures and co-locations on students and what is in the best interests of affected students. By examining the New York State Education Law, Educational Impact Statements (EIS), transcripts from public hearings, and by conducting a parent survey of 873 parents at 34 schools affected by co-locations, the report concludes that the Department's parental engagement process provided insufficient information and left too many questions unanswered questions about how students and the school community will be affected by these major school decisions. The report's key finding is that the EIS -- the official document assessing the impact that a proposed change will have on school services -- does not provide adequate information for members of the school community to understand and comment about how students will be affected by these decisions. This finding is consistent with the courts' recent decision that the school closure process is flawed. Further, if not well-planned and coordinated, closures and co-locations can disrupt students' education and decrease their access to school facilities such as classrooms, gymnasiums and cafeterias