Mariner Mars 1971 optical navigation demonstration

The feasibility of using a combination of spacecraft-based optical data and earth-based Doppler data to perform near-real-time approach navigation was demonstrated by the Mariner Mars 71 Project. The important findings, conclusions, and recommendations are documented. A summary along with publications and papers giving additional details on the objectives of the demonstration are provided. Instrument calibration and performance as well as navigation and science results are reported

Genetic and clinical characteristics of NEFL-related Charcot-Marie-Tooth disease

OBJECTIVES: To analyse and describe the clinical and genetic spectrum of Charcot-Marie-Tooth disease (CMT) caused by mutations in the neurofilament light polypeptide gene (NEFL). METHODS: Combined analysis of newly identified patients with NEFL-related CMT and all previously reported cases from the literature. RESULTS: Five new unrelated patients with CMT carrying the NEFL mutations P8R and N98S and the novel variant L311P were identified. Combined data from these cases and 62 kindreds from the literature revealed four common mutations (P8R, P22S, N98S and E396K) and three mutational hotspots accounting for 37 (55%) and 50 (75%) kindreds, respectively. Eight patients had de novo mutations. Loss of large-myelinated fibres was a uniform feature in a total of 21 sural nerve biopsies and 'onion bulb' formations and/or thin myelin sheaths were observed in 14 (67%) of them. The neurophysiological phenotype was broad but most patients with E90K and N98S had upper limb motor conduction velocities <38 m/s. Age of onset was ≤3 years in 25 cases. Pyramidal tract signs were described in 13 patients and 7 patients were initially diagnosed with or tested for inherited ataxia. Patients with E90K and N98S frequently presented before age 3 years and developed hearing loss or other neurological features including ataxia and/or cerebellar atrophy on brain MRI. CONCLUSIONS: NEFL-related CMT is clinically and genetically heterogeneous. Based on this study, however, we propose mutational hotspots and relevant clinical-genetic associations that may be helpful in the evaluation of NEFL sequence variants and the differential diagnosis with other forms of CMT

New Perspectives on Customer “Death” Using a Generalization of the Pareto/NBD Model

Several researchers have proposed models of buyer behavior in noncontractual settings that assume that customers are “alive” for some period of time and then become permanently inactive. The best-known such model is the Pareto/NBD, which assumes that customer attrition (dropout or “death”) can occur at any point in calendar time. A recent alternative model, the BG/NBD, assumes that customer attrition follows a Bernoulli “coin-flipping” process that occurs in “transaction time” (i.e., after every purchase occasion). Although the modification results in a model that is much easier to implement, it means that heavy buyers have more opportunities to “die.” In this paper, we develop a model with a discrete-time dropout process tied to calendar time. Specifically, we assume that every customer periodically “flips a coin” to determine whether she “drops out” or continues as a customer. For the component of purchasing while alive, we maintain the assumptions of the Pareto/NBD and BG/NBD models. This periodic death opportunity (PDO) model allows us to take a closer look at how assumptions about customer death influence model fit and various metrics typically used by managers to characterize a cohort of customers. When the time period after which each customer makes her dropout decision (which we call period length) is very small, we show analytically that the PDO model reduces to the Pareto/NBD. When the period length is longer than the calibration period, the dropout process is “shut off,” and the PDO model collapses to the negative binomial distribution (NBD) model. By systematically varying the period length between these limits, we can explore the full spectrum of models between the “continuous-time-death” Pareto/NBD and the naïve “no-death” NBD. In covering this spectrum, the PDO model performs at least as well as either of these models; our empirical analysis demonstrates the superior performance of the PDO model on two data sets. We also show that the different models provide significantly different estimates of both purchasing-related and death-related metrics for both data sets, and these differences can be quite dramatic for the death-related metrics. As more researchers and managers make managerial judgments that directly relate to the death process, we assert that the model employed to generate these metrics should be chosen carefully

Customer-Base Analysis using Repeated Cross-Sectional Summary (RCSS) Data

We address a critical question that many firms are facing today: Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers’ transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customer-base analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data. Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of the RCSS data structure is that individual customers cannot be identified, which makes it desirable from a data privacy and security viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. We find that the RCSS format of four quarterly histograms serves as a suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer

Estimating CLV Using Aggregated Data: The Tuscan Lifestyles Case Revisited

The Tuscan Lifestyles case (Mason, 2003) offers a simple twist on the standard view of how to value a newly acquired customer, highlighting how standard retention-based approaches to the calculation of expected customer lifetime value (CLV) are not applicable in a noncontractual setting. Using the data presented in the case (a series of annual histograms showing the aggregate distribution of purchases for two different cohorts of customers newly “acquired” by a catalog marketer), it is a simple exercise to compute an estimate of “expected 5 year CLV.” If we wish to arrive at an estimate of CLV that includes the customer\u27s “life” beyond five years or are interested in, say, sorting out the purchasing process (while “alive”) from the attrition process, we need to use a formal model of buying behavior that can be applied on such coarse data. To tackle this problem, we utilize the Pareto/NBD model developed by Schmittlein, Morrison, and Colombo (1987). However, existing analytical results do not allow us to estimate the model parameters using the data summaries presented in the case. We therefore derive an expression that enables us to do this. The resulting parameter estimates and subsequent calculations offer useful insights that could not have been obtained without the formal model. For instance, we were able to decompose the lifetime value into four factors, namely purchasing while active, dropout, surge in sales in the first year and monetary value of the average purchase. We observed a kind of “triple jeopardy” in that the more valuable cohort proved to be better on the three most critical factors

A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams

In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capabilities and victim requirements suffer from time complexity due to repetitive planning steps. To overcome these challenges, we introduce a comprehensive framework__the Multi-Stage Multi-Robot Task Assignment. This framework integrates scouting, task assignment, and path-planning stages, optimizing task allocation based on robot capabilities, victim requirements, and past robot performance. Our iterative approach ensures objective fulfillment within problem constraints. Evaluation across four maps, comparing with a state-of-the-art baseline, demonstrates our algorithm's superiority with a remarkable 97 percent performance increase. Our code is open-sourced to enable result replication.Comment: The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023 Advances in Multi-Agent Learning - Coordination, Perception, and Control Workshop

Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies.Comment: Presented at Multi-Agent Dynamic Games (MADGames) workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Impact of Relational Networks in Multi-Agent Learning: A Value-Based Factorization View

Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, agents possess varying abilities and constraints, making it necessary to prioritize agents based on their specific properties to ensure successful coordination and cooperation within the team. However, most existing cooperative multi-agent algorithms do not take into account these individual differences, and lack an effective mechanism to guide coordination strategies. We propose a novel multi-agent learning approach that incorporates relationship awareness into value-based factorization methods. Given a relational network, our approach utilizes inter-agents relationships to discover new team behaviors by prioritizing certain agents over other, accounting for differences between them in cooperative tasks. We evaluated the effectiveness of our proposed approach by conducting fifteen experiments in two different environments. The results demonstrate that our proposed algorithm can influence and shape team behavior, guide cooperation strategies, and expedite agent learning. Therefore, our approach shows promise for use in multi-agent systems, especially when agents have diverse properties.Comment: Accepted to International Conference on Decision and Control (IEEE CDC 2023

Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective

Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coordination, different agents (e.g., robots) necessitate different priorities based on their positioning within the team. Yet, many of the existing multi-robot cooperation algorithms regard agents as interchangeable and lack a mechanism to guide the type of cooperation strategy the agents should exhibit. To account for the team structure in cooperative tasks, we propose a novel algorithm that uses a relational network comprising inter-agent relationships to prioritize certain agents over others. Through appropriate design of the team's relational network, we can guide the cooperation strategy, resulting in the emergence of new behaviors that accomplish the specified task. We conducted six experiments in a multi-robot setting with a cooperative task. Our results demonstrate that the proposed method can effectively influence the type of solution that the algorithm converges to by specifying the relationships between the agents, making it a promising approach for tasks that require cooperation among agents with a specified team structure.Comment: Accepted to Multi-Robot and Multi-Agent Systems (IEEE MRS 2023

Identification of priority health conditions for field-based screening in urban slums in Bangalore, India

BACKGROUND: Urban slums are characterised by unique challenging living conditions, which increase their inhabitants' vulnerability to specific health conditions. The identification and prioritization of the key health issues occurring in these settings is essential for the development of programmes that aim to enhance the health of local slum communities effectively. As such, the present study sought to identify and prioritise the key health issues occurring in urban slums, with a focus on the perceptions of health professionals and community workers, in the rapidly growing city of Bangalore, India. METHODS: The study followed a two-phased mixed methods design. During Phase I of the study, a total of 60 health conditions belonging to four major categories: - 1) non-communicable diseases; 2) infectious diseases; 3) maternal and women's reproductive health; and 4) child health - were identified through a systematic literature review and semi-structured interviews conducted with health professionals and other relevant stakeholders with experience working with urban slum communities in Bangalore. In Phase II, the health issues were prioritised based on four criteria through a consensus workshop conducted in Bangalore. RESULTS: The top health issues prioritized during the workshop were: diabetes and hypertension (non-communicable diseases category), dengue fever (infectious diseases category), malnutrition and anaemia (child health, and maternal and women's reproductive health categories). Diarrhoea was also selected as a top priority in children. These health issues were in line with national and international reports that listed them as top causes of mortality and major contributors to the burden of diseases in India. CONCLUSIONS: The results of this study will be used to inform the development of technologies and the design of interventions to improve the health outcomes of local communities. Identification of priority health issues in the slums of other regions of India, and in other low and lower middle-income countries, is recommended