OpenRaiser/Intern-Atlas huggingface dataset:
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Venue: arXiv cs.LG 2023
Year: 2023

We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels. We extend the contrastive reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL losses and conduct an extensive amount of experiments with various self-supervised losses. Our observations suggest that the existing SSL framework for RL fails to bring meaningful improvement over the baselines only taking advantage of image augmentation when the same amount of data and augmentation is used. We further perform evolutionary searches to find the optimal combination of multiple self-supervised losses for RL, but find that even such a loss combination fails to meaningfully outperform the methods that only utilize carefully designed image augmentations. After evaluating these approaches together in multiple different environments including a real-world robot environment, we confirm that no single self-supervised loss or image augmentation method can dominate all environments and that the current framework for joint optimization of SSL and RL is limited. Finally, we conduct the ablation study on multiple factors and demonstrate the properties of representations learned with different approaches.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Venue: arXiv cs.LG 2023
Year: 2023

Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which leads to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. These techniques are more and more used to explain the marginal effect of hyperparameters on the black-box cost function or to quantify the importance of hyperparameters. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r.t. predictive performance \emph{and} the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
CodeS: Towards Code Model Generalization Under Distribution Shift
Venue: arXiv cs.SE 2023
Year: 2023

Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming a driving force for large-scale source code analysis in the big code era, limited progress has been made on distribution shift analysis and benchmarking for source code tasks. To fill this gap, this paper initiates to propose CodeS, a distribution shift benchmark dataset, for source code learning. Specifically, CodeS supports two programming languages (Java and Python) and five shift types (task, programmer, time-stamp, token, and concrete syntax tree). Extensive experiments based on CodeS reveal that 1) out-of-distribution detectors from other domains (e.g., computer vision) do not generalize to source code, 2) all code classification models suffer from distribution shifts, 3) representation-based shifts have a higher impact on the model than others, and 4) pre-trained bimodal models are relatively more resistant to distribution shifts.
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization
Venue: arXiv cs.CL 2023
Year: 2023

The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing. Many state-of-the-art language models are first trained on a large text corpus and then fine-tuned on downstream tasks. Despite its recent success and wide adoption, fine-tuning a pre-trained language model often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR). Specifically, we propose to inject the standard Gaussian noise or In-manifold noise and regularize hidden representations of the fine-tuned model. We first provide theoretical analyses to support the efficacy of our method. We then demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART. While these previous works only verify the effectiveness of their methods on relatively simple text classification tasks, we also verify the effectiveness of our method on question answering tasks, where the target problem is much more difficult and more training examples are available. Furthermore, extensive experimental results indicate that the proposed algorithm can not only enhance the in-domain performance of the language models but also improve the domain generalization performance on out-of-domain data.
Learning-Based Data Storage [Vision] (Technical Report)
Venue: arXiv cs.DB 2023
Year: 2023

Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detection, and so on. In addition to many important machine learning tasks, as artificial networks emulating the way brain cells function, DNNs also show the capability of storing non-linear relationships between input and output data, which exhibits the potential of storing data via DNNs. We envision a new paradigm of data storage, "DNN-as-a-Database", where data are encoded in well-trained machine learning models. Compared with conventional data storage that directly records data in raw formats, learning-based structures (e.g., DNN) can implicitly encode data pairs of inputs and outputs and compute/materialize actual output data of different resolutions only if input data are provided. This new paradigm can greatly enhance the data security by allowing flexible data privacy settings on different levels, achieve low space consumption and fast computation with the acceleration of new hardware (e.g., Diffractive Neural Network and AI chips), and can be generalized to distributed DNN-based storage/computing. In this paper, we propose this novel concept of learning-based data storage, which utilizes a learning structure called learning-based memory unit (LMU), to store, organize, and retrieve data. As a case study, we use DNNs as the engine in the LMU, and study the data capacity and accuracy of the DNN-based data storage. Our preliminary experimental results show the feasibility of the learning-based data storage by achieving high (100%) accuracy of the DNN storage. We explore and design effective solutions to utilize the DNN-based data storage to manage and query relational tables. We discuss how to generalize our solutions to other data types (e.g., graphs) and environments such as distributed DNN storage/computing.
Latent Diffusion Energy-Based Model for Interpretable Text Modeling
Venue: arXiv cs.LG 2023
Year: 2023

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built upon it have made interesting attempts aiming at the interpretability of text modeling. However, latent space EBMs also inherit some flaws from EBMs in data space; the degenerate MCMC sampling quality in practice can lead to poor generation quality and instability in training, especially on data with complex latent structures. Inspired by the recent efforts that leverage diffusion recovery likelihood learning as a cure for the sampling issue, we introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework, coined as the latent diffusion energy-based model. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space. Experiments on several challenging tasks demonstrate the superior performance of our model on interpretable text modeling over strong counterparts.
Dynamics of opinion polarization
Venue: arXiv cs.SI 2023
Year: 2023

For decades, researchers have been trying to understand how people form their opinions. This quest has become even more pressing with the widespread usage of online social networks and social media, which seem to amplify the already existing phenomenon of polarization. In this work, we study the problem of polarization assuming that opinions evolve according to the popular Friedkin-Johnsen (FJ) model. The FJ model is one of the few existing opinion dynamics models that has been validated on small/medium-sized social groups. First, we carry out a comprehensive survey of the FJ model in the literature (distinguishing its main variants) and of the many polarization metrics available, deriving an invariant relation among them. Secondly, we derive the conditions under which the FJ variants are able to induce opinion polarization in a social network, as a function of the social ties between the nodes and their individual susceptibility to the opinion of others. Thirdly, we discuss a methodology for finding concrete opinion vectors that are able to bring the network to a polarized state. Finally, our analytical results are applied to two real social network graphs, showing how our theoretical findings can be used to identify polarizing conditions under various configurations.
A Methodology and Software Architecture to Support Explainability-by-Design
Venue: arXiv cs.SE 2023
Year: 2023

Algorithms play a crucial role in many technological systems that control or affect various aspects of our lives. As a result, providing explanations for their decisions to address the needs of users and organisations is increasingly expected by laws, regulations, codes of conduct, and the public. However, as laws and regulations do not prescribe how to meet such expectations, organisations are often left to devise their own approaches to explainability, inevitably increasing the cost of compliance and good governance. Hence, we envision Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation. This paper describes phase (B), a technical workflow to implement explanation capability from requirements elicited by domain experts for a specific application context. Outputs of this phase are a set of configurations, allowing a reusable explanation service to exploit logs provided by the target application to create provenance traces of the application's decisions. The provenance then can be queried to extract relevant data points, which can be used in explanation plans to construct explanations personalised to their consumers. Following the workflow, organisations can design their decision-making systems to produce explanations that meet the specified requirements. To facilitate the process, we present a software architecture with reusable components to incorporate the resulting explanation capability into an application. Finally, we applied the workflow to two application scenarios and measured the associated development costs. It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Venue: arXiv cs.LG 2023
Year: 2023

Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public policy, ecology, and in medicine, decisions are often made in non-tabular settings, informed by patterns or objects detected in images (e.g., maps, satellite or tomography imagery). Using such imagery for causal inference presents an opportunity because objects in the image may be related to the treatment and outcome of interest. In these cases, we rely on the images to adjust for confounding but observed data do not directly label the existence of the important objects. Motivated by real-world applications, we formalize this challenge, how it can be handled, and what conditions are sufficient to identify and estimate causal effects. We analyze finite-sample performance using simulation experiments, estimating effects using a propensity adjustment algorithm that employs a machine learning model to estimate the image confounding. Our experiments also examine sensitivity to misspecification of the image pattern mechanism. Finally, we use our methodology to estimate the effects of policy interventions on poverty in African communities from satellite imagery.
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
Venue: arXiv cs.CV 2023
Year: 2023

We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements related to characterization and volume properties of the set of optimal segmentations are proved, and associated experiments are provided. Our main insights are: (i) the volume of the solutions to both metrics may deviate significantly from the expected volume of the target, (ii) the volume of a solution to Accuracy is always less than or equal to the volume of a solution to Dice and (iii) the optimal solutions to both of these metrics coincide when the set of feasible segmentations is constrained to the set of segmentations with the volume equal to the expected volume of the target.
WHIS: Hearing impairment simulator based on the gammachirp auditory filterbank
Venue: arXiv cs.SD 2023
Year: 2023

A new version of a hearing impairment simulator (WHIS) was implemented based on a revised version of the gammachirp filterbank (GCFB), which incorporates fast frame-based processing, absolute threshold (AT), an audiogram of a hearing-impaired (HI) listener, and a parameter to control the cochlear input-output (IO) function. The parameter referred to as the compression health $α$ controlled the slope of the IO function to range from normal hearing (NH) listeners to HI listeners, without largely changing the total hearing loss (HL). The new WHIS was designed provide an NH listener the same EPs as those of a target HI listener.The analysis part of WHIS was almost the same as that of the revised GCFB, except that the IO function was used instead of the gain function. We proposed two synthesis methods: a direct time-varying filter for perceptually small distortion and a filterbank analysis-synthesis for further HI simulations including temporal smearing. We evaluated the WHIS family and a Cambridge version of the HL simulator (CamHLS) in terms of differences in the IO function and spectral distance. The IO functions were simulated fairly well at $α$ less than 0.5 but not at $α$ equal to 1. Thus, it is difficult to simulate the HL when the IO function is sufficiently healthy. This is a fundamental limit of any existing HL simulator as well as WHIS. The new WHIS yielded a smaller spectral distortion than CamHLS and was fairly compatible with the previous version.
Performance Analysis of SWIPT Relay Networks over Arbitrary Dependent Fading Channels
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we investigate the impact of fading channel correlation on the performance of dual-hop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) relay networks. More specifically, by considering the power splitting-based relaying (PSR) protocol for the energy harvesting (EH) process, we quantify the effect of positive and negative dependency between the source-to-relay (SR) and relay-to-destination (RD) links on key performance metrics such as ergodic capacity and outage probability. To this end, we first represent general formulations for the cumulative distribution function (CDF) of the product of two arbitrary random variables, exploiting copula theory. This is used to derive the closed-form expressions of the ergodic capacity and outage probability in a SWIPT relay network under correlated Nakagami-m fading channels. Monte-Carlo (MC) simulation results are provided throughout to validate the correctness of the developed analytical results, showing that the system performance significantly improves under positive dependence in the SR-RD links, compared to the case of negative dependence and independent links. Results further demonstrate that the efficiency of the ergodic capacity and outage probability ameliorates as the fading severity reduces for the PSR protocol.
Data security as a top priority in the digital world: preserve data value by being proactive and thinking security first
Venue: arXiv cs.DB 2023
Year: 2023

Today, large amounts of data are being continuously produced, collected, and exchanged between systems. As the number of devices, systems and data produced grows up, the risk of security breaches increases. This is all the more relevant in times of COVID-19, which has affected not only the health and lives of human beings' but also the lifestyle of society, i.e., the digital environment has replaced the physical. This has led to an increase in cyber security threats of various nature. While security breaches and different security protection mechanisms have been widely covered in the literature, the concept of a primitive artifact such as data management system seems to have been more neglected by researchers and practitioners. But are data management systems always protected by default? Previous research and regular updates on data leakages suggest that the number and nature of these vulnerabilities are high. It also refers to little or no DBMS protection, especially in case of NoSQL, which are thus vulnerable to attacks. The aim of this paper is to examine whether traditional vulnerability registries provide a sufficiently comprehensive view of DBMS security, or they should be intensively and dynamically inspected by DBMS owners by referring to Internet of Things Search Engines moving towards a sustainable and resilient digitized environment. The paper brings attention to this problem and makes the reader think about data security before looking for and introducing more advanced security and protection mechanisms, which, in the absence of the above, may bring no value.
On the Message Passing Efficiency of Polar and Low-Density Parity-Check Decoders
Venue: arXiv cs.IT 2023
Year: 2023

This study focuses on the efficiency of message-passing-based decoding algorithms for polar and low-density parity-check (LDPC) codes. Both successive cancellation (SC) and belief propagation (BP) decoding algorithms are studied {in} the message-passing framework. Counter-intuitively, SC decoding demonstrates the highest decoding efficiency, although it was considered a weak decoder {in terms of} error-correction performance. We analyze the complexity-performance tradeoff to dynamically track the decoding efficiency, where the complexity is measured by the number of messages passed (NMP), and the performance is measured by the statistical distance to the maximum a posteriori (MAP) estimate. This study offers a new insight into the contribution of each message passed in decoding, and compares various decoding algorithms on a message-by-message level. The analysis corroborates recent results on terabits-per-second polar SC decoders, and might shed light on better scheduling strategies.
Plurality Veto: A Simple Voting Rule Achieving Optimal Metric Distortion
Venue: arXiv cs.GT 2023
Year: 2023

The metric distortion framework posits that n voters and m candidates are jointly embedded in a metric space such that voters rank candidates that are closer to them higher. A voting rule's purpose is to pick a candidate with minimum total distance to the voters, given only the rankings, but not the actual distances. As a result, in the worst case, each deterministic rule picks a candidate whose total distance is at least three times larger than that of an optimal one, i.e., has distortion at least 3. A recent breakthrough result showed that achieving this bound of 3 is possible; however, the proof is non-constructive, and the voting rule itself is a complicated exhaustive search. Our main result is an extremely simple voting rule, called Plurality Veto, which achieves the same optimal distortion of 3. Each candidate starts with a score equal to his number of first-place votes. These scores are then gradually decreased via an n-round veto process in which a candidate drops out when his score reaches zero. One after the other, voters decrement the score of their bottom choice among the standing candidates, and the last standing candidate wins. We give a one-paragraph proof that this voting rule achieves distortion 3. This rule is also immensely practical, and it only makes two queries to each voter, so it has low communication overhead. We also generalize Plurality Veto into a class of randomized voting rules in the following way: Plurality veto is run only for k < n rounds; then, a candidate is chosen with probability proportional to his residual score. This general rule interpolates between Random Dictatorship (for k=0) and Plurality Veto (for k=n-1), and k controls the variance of the output. We show that for all k, this rule has distortion at most 3.