arXiv research

Organizations adapt ML models to new data types using existing resources.

Examines parallels between human subjects and texts for causal inference.

Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. …

Companies do not operate in a vacuum. As companies move towards an increasingly specialized production function and their reach is becoming truly global, their aptitude in managing and shaping their inter-organizational network is a determining factor in measuring their health. Current models of company financial healt…

Study shows market volatility affects optimal communication design for trading strategies.

BERTopic improves financial text analysis with FinTextSim's contextual embeddings.

Improving software quality through effective organizational learning.

Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challeng…

P3LS preserves privacy while integrating data across companies.

Proposes a method for evaluating multiple dimensions of organizational effectiveness using DEA.

Workplace communications became more siloed during the pandemic, reducing stability within communities.

Study examines human factors in radiographic testing to improve inspection performance.

Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnit…

Productions functions map the inputs of a firm or a productive system onto its outputs. This article expounds generalizations of the production function that include state variables, organizational structures and increasing returns to scale. These extensions are needed in order to explain the regularities of the empiri…

Study proposes new framework for Board-CEO relationship.

We examine the space of solutions to the affine quasi--Einstein equation in the context of homogeneous surfaces. As these spaces can be used to create gradient Yamabe solitions, conformally Einstein metrics, and warped product Einstein manifolds using the modified Riemannian extension, we provide very explicit descript…

Adaptive AI delegation framework for dynamic decision authority allocation.

This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.

We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space and find that learning embeddings in the Lorentz model is substantially more efficient than in the Poincaré-ball model. We show that the prop…

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…

This paper is a contribution to interweaving two lines of research that have progressed in separate ways: network analyses of international trade and the literature on African trade and development. Gathering empirical data on African countries has important limitations and so does the space occupied by African countri…

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank using only internal loss data, and takes into account in a simple and realistic way the correlations among different processe…

Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.

Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.

MineRL Competition reduced reinforcement learning sample needs.

Corporate venture capital is in the midst of a renaissance. The end of 2015 marked all-time highs both in the number of corporate firms participating in VC deals and in the amount of capital being deployed by corporate VCs. This paper explores, rather than defines, how these firms find success in the wake of this sudde…

A novel dynamical model for the study of operational risk in banks and suitable for the calculation of the Value at Risk (VaR) is proposed. The equation of motion takes into account the interactions among different bank's processes, the spontaneous generation of losses via a noise term and the efforts made by the bank …

Machine learning predicts criminal networks' missing partnerships and future behavior.

Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, char…

The paper classifies actions of a specific group on certain manifolds.

The paper solves the Nielsen realization problem for high degree del Pezzo surfaces.

We establish a gluing construction for Higgs bundles over a connected sum of Riemann surfaces in terms of solutions to the $\text{Sp(4}\text{,}\mathbb{R}\text{)}$-Hitchin equations using the linearization of a relevant elliptic operator. The construction can be used to provide model Higgs bundles in all the $2g-3$ exce…

FairLangProc simplifies fairness in NLP models for Python users.

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserv…

A new text representation model combines CNN and VAE for better semantic extraction.

We propose two algorithms that can find local minima faster than the state-of-the-art algorithms in both finite-sum and general stochastic nonconvex optimization. At the core of the proposed algorithms is $\text{One-epoch-SNVRG}^+$ using stochastic nested variance reduction (Zhou et al., 2018a), which outperforms the s…

New families of Lie groups with special foliations discovered.

Improved text summarization using belief propagation on weighted bipartite graphs.

Paper proposes distributed optimization for federated learning with theoretical guarantees.

New TC variant dTC better fits motion planning for some systems.

Let $\text{Man}_{*}$ denote the category of closed, connected, oriented and based $3$-manifolds, with basepoint preserving diffeomorphisms between them. Juhász, Thurston and Zemke showed that the Heegaard Floer invariants are natural with respect to diffeomorphisms, in the sense that there are functors $HF^{\circ}: \te…

StoryGen uses images to generate coherent text from multiple images.

Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However struc…

Paper generates diverse, readable adversarial texts from scratch.

SCROLLS benchmarks long text NLP tasks, improving existing models.

New method detects text changes under dependencies, outperforming baselines.

Contextual bandits study how reward variance affects regret bounds.

The paper studies symplectic structures on character varieties of Sasakian threefolds.