The Application of the Right to be Forgotten in the Machine Learning Context: From the Perspective of European Laws

The right to be forgotten has been evolving for decades along with the progress of different statutes and cases and, finally, independently enacted by the General Data Protection Regulation, making it widely applied across Europe. However, the related provisions in the regulation fail to enable machine learning systems to realistically forget the personal information which is stored and processed therein. This failure is not only because existing European rules do not stipulate standard codes of conduct and corresponding responsibilities for the parties involved, but they also cannot accommodate themselves to the new environment of machine learning, where specific information can hardly be removed from the entire cyberspace. There is also evidence in the technical, legal, and social spheres to elaborate on the mismatch between the rules of the right to be forgotten and the novel machinery background based on the above reasons. To mitigate these issues, this article will draw lessons from the cyberspace regulation theories and expound on their insights into realizing the right and the strategies they offered to reframe a new legal scheme of the right. This innovative framework entails a combination of technological, legal, and possibly social measures taken by online intermediaries which make critical decisions on the personal data given the so-called stewardship responsibilities. Therefore, the application of the right to be forgotten in the machinery landscape will plausibly be more effective

Efficient seismic imaging with the double plane wave data

Seismic imaging is critical in providing the image of the Earth’s subsurface, and it plays an important role in hydrocarbon explorations. Obtaining high resolution images with accurate reflectivities and accurate positions of subsurface structures is the goal for exploration geophysicists. Reverse time migration (RTM), which solves the two-way wave equation, can resolve all wavefield propagation phenomena. In geologically complex regions, RTM has been proven to outperform other imaging methods in correctly revealing the subsurface structures. However, implementing the traditional pre-stack shot profile RTM is computationally expensive. Time consuming wavefield propagation processes need to be performed for each shot gather to obtain high resolution images. The traditional RTM can become extremely expensive with increasing shot numbers. In this dissertation, I focus on improving the migration efficiency of the RTM using the double plane wave (DPW) data, which are the fully decomposed plane wave data. Three RTM methods are developed to migrate the DPW data, all of which can improve the migration efficiency comparing to the traditional shot profile RTM. Two of the methods utilize the adjoint state method, and they are known as the time domain DPW-based RTM and the frequency domain DPW-based RTM. A third migration method using the DPW data is derived under the Born approximation. This method employs the frequency domain plane wave Green’s functions for imaging, and it is named as frequency domain DPW RTM. Among the three proposed RTM methods, the frequency domain DPW RTM is the most efficient. Comparing to the traditional shot profile pre-stack RTM, the frequency domain DPW RTM can increase migration efficiency of RTM by an order of magnitude, making the frequency domain DPW RTM a preferable option for migrating large seismic datasets. All of the three proposed migration methods can image subsurface structures with given dips, which makes them target-oriented imaging methods. The proposed methods are beneficial to migration velocity analysis. To improve the resolution of migration results, a least squares RTM method using the DPW data is proposed. A Born modeling operator that predict the DPW data at the surface and its adjoint operator, which is a migration operator, are derived to implement the least squares RTM. Both of the operators require only a limited number of plane wave Green’s functions for the modeling and the migration processes. The proposed least squares RTM substantially increases the efficiency of the least squares migration. In the DPW domain, the applicability of the reciprocity principle is also investigated. The reciprocity principle can be applied to the seismic data that are processed with proper seismic processing flow. Utilizing the reciprocity principle, a DPW dataset transformed from one-sided shot gathers can approximate a DPW dataset transformed from split-spread shot gathers. Therefore, I suggest that one-sided acquisition geometries should be extended to the largest possible offsets, and the reciprocity principle should be invoked to improve subsurface illumination. Migration efficiency can be further improved with the help of the reciprocity principle.Geological Science

Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

Unsupervised learning methods for feature extraction are becoming more and more popular. We combine the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. Experiments have proved that our two proposed autoencoder networks have good feature learning capabilities by themselves, and the contrastive learning network we designed can better combine the features of the two to learn more representative features. As a result, our method surpasses other comparison methods in the hyperspectral classification experiments, including some supervised methods. Moreover, our method maintains a fast feature extraction speed than baseline methods. In addition, our method reduces the requirements for huge computing resources, separates feature extraction and contrastive learning, and allows more researchers to conduct research and experiments on unsupervised contrastive learning

What Makes Hiring Difficult? Evidence from Linked Survey-Administrative Data

We designed an innovative survey of firms and linked it to Danish administrative data to yield new insights about the factors that can influence firms’ hiring decisions. Several important findings stand out: (1) search and training frictions and economic uncertainty are as important as labor costs in hiring decisions ; (2) search and training frictions are more likely to affect younger and smaller firms; (3) uncertainty is more likely to affect hiring decisions in low-productivity firms; (4) thirty percent of firms prefer to hire already employed persons over the unemployed, because they believe that unemployed workers have lower abilities due to negative selection or skill depreciation during unemployment; and (5) these firms are more likely to report that labor market frictions and labor costs considerations discourage them from hiring

Dissecting causal asymmetries in inductive generalization

Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry

Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation

Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning. Previous approaches either introduce additional procedures for learning to reward, thereby increasing the complexity of optimization, or assume that user-agent interactions provide perfect demonstrations, which is not feasible in practice. Ideally, we aim to employ a unified approach that optimizes both the reward and policy using compositional demonstrations. However, this requirement presents a challenge since rewards inherently quantify user feedback on-policy, while recommender agents approximate off-policy future cumulative valuation. To tackle this challenge, we propose a novel batch inverse reinforcement learning paradigm that achieves the desired properties. Our method utilizes discounted stationary distribution correction to combine LTR and recommender agent evaluation. To fulfill the compositional requirement, we incorporate the concept of pessimism through conservation. Specifically, we modify the vanilla correction using Bellman transformation and enforce KL regularization to constrain consecutive policy updates. We use two real-world datasets which represent two compositional coverage to conduct empirical studies, the results also show that the proposed method relatively improves both effectiveness (2.3\%) and efficiency (11.53\%

ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition

In Speech Emotion Recognition (SER), textual data is often used alongside audio signals to address their inherent variability. However, the reliance on human annotated text in most research hinders the development of practical SER systems. To overcome this challenge, we investigate how Automatic Speech Recognition (ASR) performs on emotional speech by analyzing the ASR performance on emotion corpora and examining the distribution of word errors and confidence scores in ASR transcripts to gain insight into how emotion affects ASR. We utilize four ASR systems, namely Kaldi ASR, wav2vec2, Conformer, and Whisper, and three corpora: IEMOCAP, MOSI, and MELD to ensure generalizability. Additionally, we conduct text-based SER on ASR transcripts with increasing word error rates to investigate how ASR affects SER. The objective of this study is to uncover the relationship and mutual impact of ASR and SER, in order to facilitate ASR adaptation to emotional speech and the use of SER in real world.Comment: Accepted to INTERSPEECH 202