638 research outputs found
Do passive investors influence corporate social responsibility? A risk-management perspective
This paper examines the impact of passive investors on Corporate Social Responsibility (CSR) through the lens of a risk-management view of CSR, which emphasizes its insurance-like effects in adverse corporate events. Since passive investors have diversified away most idiosyncratic risks, we predict that they demand less CSR as a strategic approach to manage risks. Using the annual Russell 1000/2000 index reconstitution as an instrument for passive investor ownership, we document evidence consistent with our prediction. The negative effect is more pronounced among better-diversified passive investors and firms that are not in CSR-sensitive industries. We further show that passive investors hold back CSR activities through the channel of “voice” by reducing the number of socially responsible investment (SRI) proposals.</p
Causal Deep Reinforcement Learning using Observational Data
Deep reinforcement learning (DRL) requires the collection of plenty of
interventional data, which is sometimes expensive and even unethical in the
real world, such as in the autonomous driving and the medical field. Offline
reinforcement learning promises to alleviate this issue by exploiting the vast
amount of observational data available in the real world. However,
observational data may mislead the learning agent to undesirable outcomes if
the behavior policy that generates the data depends on unobserved random
variables (i.e., confounders). In this paper, we propose two deconfounding
methods in DRL to address this problem. The methods first calculate the
importance degree of different samples based on the causal inference technique,
and then adjust the impact of different samples on the loss function by
reweighting or resampling the offline dataset to ensure its unbiasedness. These
deconfounding methods can be flexibly combined with the existing model-free DRL
algorithms such as soft actor-critic and deep Q-learning, provided that a weak
condition can be satisfied by the loss functions of these algorithms. We prove
the effectiveness of our deconfounding methods and validate them
experimentally
Face Alignment Assisted by Head Pose Estimation
In this paper we propose a supervised initialization scheme for cascaded face
alignment based on explicit head pose estimation. We first investigate the
failure cases of most state of the art face alignment approaches and observe
that these failures often share one common global property, i.e. the head pose
variation is usually large. Inspired by this, we propose a deep convolutional
network model for reliable and accurate head pose estimation. Instead of using
a mean face shape, or randomly selected shapes for cascaded face alignment
initialisation, we propose two schemes for generating initialisation: the first
one relies on projecting a mean 3D face shape (represented by 3D facial
landmarks) onto 2D image under the estimated head pose; the second one searches
nearest neighbour shapes from the training set according to head pose distance.
By doing so, the initialisation gets closer to the actual shape, which enhances
the possibility of convergence and in turn improves the face alignment
performance. We demonstrate the proposed method on the benchmark 300W dataset
and show very competitive performance in both head pose estimation and face
alignment.Comment: Accepted by BMVC201
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling
Many E-commerce sites now offer product-specific question answering platforms
for users to communicate with each other by posting and answering questions
during online shopping. However, the multiple answers provided by ordinary
users usually vary diversely in their qualities and thus need to be
appropriately ranked for each question to improve user satisfaction. It can be
observed that product reviews usually provide useful information for a given
question, and thus can assist the ranking process. In this paper, we
investigate the answer ranking problem for product-related questions, with the
relevant reviews treated as auxiliary information that can be exploited for
facilitating the ranking. We propose an answer ranking model named MUSE which
carefully models multiple semantic relations among the question, answers, and
relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph
with the question, each answer, and each review snippet as nodes. Then a
customized graph convolutional neural network is designed for explicitly
modeling the semantic relevance between the question and answers, the content
consistency among answers, and the textual entailment between answers and
reviews. Extensive experiments on real-world E-commerce datasets across three
product categories show that our proposed model achieves superior performance
on the concerned answer ranking task.Comment: Accepted by SIGIR 202
Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators
The recent success of Large Language Models (LLMs) signifies an impressive
stride towards artificial general intelligence. They have shown a promising
prospect in automatically completing tasks upon user instructions, functioning
as brain-like coordinators. The associated risks will be revealed as we
delegate an increasing number of tasks to machines for automated completion. A
big question emerges: how can we make machines behave responsibly when helping
humans automate tasks as personal copilots? In this paper, we explore this
question in depth from the perspectives of feasibility, completeness and
security. In specific, we present Responsible Task Automation (ResponsibleTA)
as a fundamental framework to facilitate responsible collaboration between
LLM-based coordinators and executors for task automation with three empowered
capabilities: 1) predicting the feasibility of the commands for executors; 2)
verifying the completeness of executors; 3) enhancing the security (e.g., the
protection of users' privacy). We further propose and compare two paradigms for
implementing the first two capabilities. One is to leverage the generic
knowledge of LLMs themselves via prompt engineering while the other is to adopt
domain-specific learnable models. Moreover, we introduce a local memory
mechanism for achieving the third capability. We evaluate our proposed
ResponsibleTA on UI task automation and hope it could bring more attentions to
ensuring LLMs more responsible in diverse scenarios. The research project
homepage is at
https://task-automation-research.github.io/responsible_task_automation
Oportunidades y dependencia. Evolución del cambio técnico en la minería cupríferachilena, 1900-1950
Màster Oficial d'Història Econòmica, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2020-2021, Tutor: Marc Badía MiróDurante la primera mitad del siglo XX, la minería del cobre chilena vivió transformaciones significativas en su inserción en los mercados internacionales, lo cual fue empujado por el aumento de la producción basado en la explotación a gran escala de las grandes minas. El cambio técnico jugó un papel imprescindible en estos avances tanto para hacer explotables los minerales en vetas de menor ley, como para diversificar el tipo productos metalúrgicos transformados, aunque eso fuera a cambio de genera una dependencia extranjera importante. Este trabajo se dedicará a revisar la evolución del cambio técnico y entender el doble impacto de las nuevas técnicas en la extracción del metal rojo, por medio de relacionar el uso de maquinaria con la evolución de la producción y exportación minería cuprífera a medio-largo plazo
The burden of attention:CEO publicity and tax avoidance
We use search volume index (SVI) for a CEO’s name and stock ticker from Google Trends to measure CEO publicity, and examine the competing hypotheses on its relation to tax avoidance. On the one hand, CEOs who receive more attention from retail investors may engage in tax evasion activities to meet investors’ performance expectations; on the other hand, they are more concerned with public image and avoiding being labeled as tax avoiders. Based on the CEOs of S&P 500 firms between 2004 and 2011, our finding supports the former and shows that CEOs with higher publicity manage to have a lower effective tax rate and cash effective tax rate. Such effect is moderated by board independence. Finally, firms with higher CEO publicity pay auditors higher tax fees, suggesting that these CEOs tend to use more tax planning services from auditors
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