On a Reduction for a Class of Resource Allocation Problems

In the resource allocation problem (RAP), the goal is to divide a given amount of a resource over a set of activities while minimizing the cost of this allocation and possibly satisfying constraints on allocations to subsets of the activities. Most solution approaches for the RAP and its extensions allow each activity to have its own cost function. However, in many applications, often the structure of the objective function is the same for each activity, and the difference between the cost functions lies in different parameter choices, such as, for example, the multiplicative factors. In this article, we introduce a new class of objective functions that captures a significant number of the objectives occurring in studied applications. These objectives are characterized by a shared structure of the cost function depending on two input parameters. We show that, given the two input parameters, there exists a solution to the RAP that is optimal for any choice of the shared structure. As a consequence, this problem reduces to the quadratic RAP, making available the vast amount of solution approaches and algorithms for the latter problem. We show the impact of our reduction result on several applications, and in particular, we improve the best-known worst-case complexity bound of two problems in vessel routing and processor scheduling from [Formula: see text] to [Formula: see text]. Summary of Contribution: The resource allocation problem (RAP) with submodular constraints and its special cases are classic problems in operations research. Because these problems are studied in many different scientific disciplines, many conceptual insights, structural properties, and solution approaches have been reinvented and rediscovered many times. The goal of this article is to reduce the amount of future reinventions and rediscoveries by bringing together these different perspectives on RAPs in a way that is accessible to researchers with different backgrounds. The article serves as an exposition on RAPs and on their wide applicability in many areas, including telecommunications, energy, and logistics. In particular, we provide tools and examples that can be used to formulate and solve problems in these areas as RAPs. To accomplish this, we make three concrete contributions. First, we provide a survey on algorithms and complexity results for RAPs and discuss several recent advances in these areas. Second, we show that many objectives for RAPs can be reduced to a (simpler) quadratic objective function, which makes available the extensive collection of fast and efficient algorithms for quadratic RAPs to solve these problems. Third, we discuss the impact that RAPs and the aforementioned reduction result can make in several application areas.

An Active Learning Seminar and Sequential Research Project Experience

I present two key components from a course designed to introduce undergraduate students to human rights: a set of group-based active learning tasks and an individual-based sequential research project. In the classroom, active learning opportunities allow students to creatively and collectively engage with course material. The sequential research project is a step-by-step guide for creating an original research paper. For the two components, the students draw from a set of primary source documents combined with additional readings to build knowledge in the classroom. With this new knowledge, the students generate ideas and content that they use to write a sequence of research essays about that course topic outside the classroom. In this manuscript, I describe the shared structure of the two learning components, discuss details about each of the sequential essays, present assessment data, and provide suggestions about how to adapt the course to other social science topics.

Crowdsourcing System using Blockchain Technology

Crowdsourcing frameworks have been receiving a lot of enthusiastic acceptance these days, and there has been a rise in significant raise in concern as well. Crowdsourcing frameworks are excellent tools that can coordinate the human insights of specific instances, and organizations together globally and help comprehend and collaborate intricate chore. Notwithstanding, these central structures subject to the inadequacies of the trusted standard like standard financial foundations. For example, single motivation behind dissatisfaction, higher organizations cost, and security disclosure. An idea is to use a shared structure for crowdsourcing basis blockchain, wherein the work of the requester is directed by the swarm of workers without relying on central freely supporting systems or foretold customers that are inclined to organizations with enrolling certifiable characters. In thought, the proposed framework design would permit Users to enroll, post, or get an endeavor securely. The design would likewise furnish clients with undeniable degree of protection, and security, and the proposed system additionally has low administration costs. By extending the adaptability and adaptability of publicly supporting the reason for existing is to show the crowdsourcing logic with intelligent contract. As per this structure, a requester needs to deposit the venture money, while posting task. The stored deposited amount is escrowed with the framework, and on the concurred task fruition, the laborer will get the undertaking amount from the escrow. Because of this system, a requester won't need to pay more than what an assignment merits, as indicated by a rule unveiled when errand is posted; and every laborer will unquestionably get an installment dependent on the rules.

Meta Dynamic Pricing: Transfer Learning Across Experiments

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T) for N different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (meta-exploration) with the need to leverage the estimated prior to achieve good performance (meta-exploitation) and (ii) accounting for uncertainty in the estimated prior by appropriately “widening” the estimated prior as a function of its estimation error. We introduce a novel prior alignment technique to analyze the regret of Thompson sampling with a misspecified prior, which may be of independent interest. Unlike prior-independent approaches, our algorithm’s meta regret grows sublinearly in N, demonstrating that the price of an unknown prior in Thompson sampling can be negligible in experiment-rich environments (large N). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared with prior-independent algorithms. This paper was accepted by George J. Shanthikumar for the Management Science Special Issue on Data-Driven Analytics.

Efficient hardware implementation for lightweight mCrypton algorithm using FPGA

<p>The lightweight cryptography is used for low available resources devices such as radio frequency identification (RFID) tags, internet of things (IoTs) and wireless sensor networks. In such case, the lightweight cryptographic algorithms should consider power consumption, design area, speed, and throughput. This paper presents a new architecture of mCrypton lightweight cryptographic algorithm which considers the above-mentioned conditions. Resource-shared structure is used to reduce the area of the new architecture. The proposed architecture is implemented using ISE Xilinx V14,5 and Spartan 3 FPGA platform. The simulation results introduced that the proposed design area is 375 of slices, up to 302 MHz operating frequency, a throughput of 646 Mbps, efficiency of 1.7 Mbps/slice and 0.089 Watt power consumption. Thus, the proposed architecture outperforms similar architectures in terms of area, speed, efficiency and throughput.</p>

Examining the effects of time of day and sleep on generalization

Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for time of day apart from sleep. In follow-up experiments, we were unable to replicate the time of day effect for reasons that may relate to changes in category structure and task engagement. Despite this lack of consistency, we found a morning benefit for generalization when analyzing all the data from experiments with matched protocols (n = 136). We suggest that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.

Multiview Common Subspace Clustering via Coupled Low Rank Representation

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.

Collaborative place-based health governance systems: stakeholders’ perceptions in the Portuguese Baixo Vouga sub-region

This study aimed to assess the Baixo Vouga sub-region (Portugal) governance system through 15 interviews with leaders of institutions with decision-making power and provide healthcare. The interviews were subjected to a content analysis, organized in matrices by cases, categories, subcategories, and indicators. Recording units were extracted from the interviews to produce data for each indicator. A Collaborative Place-based Governance Framework systematizing operational definitions of collaborative governance was implemented to serve as a benchmark for assessing the collaborative and place-based dimensions. The Baixo Vouga sub-Region governance system is collaborative because it is based on a shared structure of principles that translates into the services provided. It has a multilevel and multisector collaboration, and can undertake shared decisions. These dimensions could be reinforced through increased participation, autonomy, subsidiarity if more place-based information and practical knowledge were sought. The system would also benefit from an extensive adoption of bottom-up methods to formulate and implement policies.

Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs

A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.

Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2 × 2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications.