Layered Fiduciaries in the Information Age

Technology companies such as Facebook have long been criticized for abusing customers’ personal information and monetizing user data in a manner contrary to customer expectations. Some commentators suggest fiduciary law could be used to restrict how these companies use their customers’ data.1 Under this framework, a new member of the fiduciary family called the “information fiduciary” was born. The concept of an information fiduciary is that a company providing network services to “collect, analyze, use, sell, and distribute personal information” owes customers and end-users a fiduciary duty to use the collected data to promote their interests, thereby assuming fiduciary liability if it misuses or misappropriates customer data.2 Although the possibility of an information fiduciary has generated significant attention, neither questions about the scope of the information fiduciary’s duty of care nor whether corporate law’s fiduciary duties are compatible with the information fiduciary duty have been satisfactorily answered. In 2021, Facebook was renamed Meta Platforms, Inc., to expand business related to the Metaverse,3 which is expected to bring about many new digital products. The establishment and development of the information fiduciary duty will help prepare the legal framework for this new era of digitization. This Article proposes a model to implement the information fiduciary’s duty of loyalty and duty of care to end-users in today’s information age by imposing these duties on Data Protection Officers (DPOs). First, this Article sketches the contours of information fiduciary duties on DPOs, examines how these duties can be structured, and clarifies how they interact with the duties owed by directors to the company. Second, this paper addresses the use of layered fiduciaries to alleviate the potential conflict caused by the information fiduciary duty. Third, this Article discusses in detail how the fiduciary duties imposed by Delaware corporate law can be applied to the field of digital privacy and consumer data. Directors’ duties of care and loyalty in corporate law have developed over decades to form a useful system that is applicable in developing the information fiduciary duty. Implementing the information fiduciary duty can benefit from and be partially guided by existing law, like the director’s duty to inform under the duty of care and the duty to act in the best interests of the company under the duty of loyalty. Lastly, this Article explores how the information fiduciary duty can efficiently regulate multinational corporations’ international data transfers, a rarely discussed yet important aspect of world economic development

Optimization Study on the Heat Transfer Area of the Sewage Source Heat Pump System Based on Year-round Coefficient of Performance

AbstractBased on simulation model of the sewage source heat pump system (SSHPS), weigh the influence of coefficient of performance under different work conditions, the ACOP was defined and as the optimization function, with the heat exchange areas of the evaporator, condenser and the sewage-midwater heat exchanger as the optimization variables. The optimization mathematical models for the on-off control scheme were set up to calculate the ACOP to study the optimal design and areas matching between the evaporator, condenser and sewage-midwater heat exchanger. Comparison with the initial design parameters and different optimization designs with HSPF, SEER and EER, the ACOP optimization is nearer to the objective energy conservation effect

Concatenation Schemes for Topological Fault-tolerant Quantum Error Correction

We investigate a family of fault-tolerant quantum error correction schemes based on the concatenation of small error detection or error correction codes with the three-dimensional cluster state. We propose fault-tolerant state preparation and decoding schemes that effectively convert every circuit-level error into an erasure error, leveraging the cluster state's high threshold against such errors. We find a set of codes for which such a conversion is possible, and study their performance against the standard circuit-level depolarizing model. Our best performing scheme, which is based on a concatenation with a classical code, improves the threshold by $16.5\%$ and decreases the spacetime overhead by $32\%$ compared to the scheme without concatenation, with each scheme subject to a physical error rate of $10^{-3}$ and achieving a logical error rate of $10^{-6}$.Comment: 11+3 pages, 9+12 figure

The Reasons for UK Large Financial Institutions' Failure during the Recent Financial Crisis in 2007

Most UK financial institutions have failed during the recent financial crisis. Although this depression is triggered by the failure of US housing market, the business model for the UK is still different with US financial institutions. Most failed companies are not hold large amount of sub-prime loans in their asset portfolio in the UK, the most important reason is the wholesale funding strategies became the central business model for the most failed firms, therefore the retail deposits are no longer be an important funding source for most failed banks and building societies. And the Basel regulation cannot represent as a reasonable binding to constraint firm take risky project. Since the capital ratio for the most failed companies is good, but the capital still not absorb the contingent increasing liability for most failed companies. This paper will concentrates on evaluating the main reasons for the large UK financial institutions failed in both internal operation and external supervision. The empirical research will focus on the large commercial banks and building societies in the UK during the period from 2006 to 2011

Evolutionary Curriculum Training for DRL-Based Navigation Systems

In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising method for robot collision avoidance. However, such DRL models often come with limitations, such as adapting effectively to structured environments containing various pedestrians. In order to solve this difficulty, previous research has attempted a few approaches, including training an end-to-end solution by integrating a waypoint planner with DRL and developing a multimodal solution to mitigate the drawbacks of the DRL model. However, these approaches have encountered several issues, including slow training times, scalability challenges, and poor coordination among different models. To address these challenges, this paper introduces a novel approach called evolutionary curriculum training to tackle these challenges. The primary goal of evolutionary curriculum training is to evaluate the collision avoidance model's competency in various scenarios and create curricula to enhance its insufficient skills. The paper introduces an innovative evaluation technique to assess the DRL model's performance in navigating structured maps and avoiding dynamic obstacles. Additionally, an evolutionary training environment generates all the curriculum to improve the DRL model's inadequate skills tested in the previous evaluation. We benchmark the performance of our model across five structured environments to validate the hypothesis that this evolutionary training environment leads to a higher success rate and a lower average number of collisions. Further details and results at our project website.Comment: Robotics: Science and System

Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition

Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the help of Transformer-based models. While these models still have the disadvantage of requiring a large number of model parameters. To overcome the drawback of universal Transformer models for the application of ASR on edge devices, we propose a solution that can reuse the block in Transformer models for the occasion of the small footprint ASR system, which meets the objective of accommodating resource limitations without compromising recognition accuracy. Specifically, we design a novel block-reusing strategy for speech Transformer (BRST) to enhance the effectiveness of parameters and propose an adapter module (ADM) that can produce a compact and adaptable model with only a few additional trainable parameters accompanying each reusing block. We conducted an experiment with the proposed method on the public AISHELL-1 corpus, and the results show that the proposed approach achieves the character error rate (CER) of 9.3%/6.63% with only 7.6M/8.3M parameters without and with the ADM, respectively. In addition, we also make a deeper analysis to show the effect of ADM in the general block-reusing method