OpenRaiser/Intern-Atlas huggingface dataset:
DEUS: Distributed Electronic Patient File Update System
Venue: arXiv cs.DC 2023
Year: 2023

Inadequate availability of patient information is a major cause for medical errors and affects costs in healthcare. Traditional approaches to information integration in healthcare do not solve the problem. Applying a document-oriented paradigm to systems integration enables inter-institutional information exchange in healthcare. The goal of the proposed architecture is to provide information exchange between strict autonomous healthcare institutions, bridging the gap between primary and secondary care. In a long-term healthcare data distribution scenario, the patient has to maintain sovereignty over any personal health information. Thus, the traditional publish-subscribe architecture is extended by a phase of human mediation within the data flow. DEUS essentially decouples the roles of information author and information publisher into distinct actors, resulting in a triangular data flow. The interaction scenario will be motivated. The significance of human mediation will be discussed. DEUS provides a carefully distinguished actor and role model for mediated pub-sub. The data flow between the participants is factored into distinct phases of information interchange. The artefact model is decomposed into role-dependent constituent parts. Both a domain specific (healthcare) terminology and a generic terminology is provided. From a technical perspective, the system design is presented. The sublayer for network transfer will be highlighted as well as the subsystem for human-machine interaction.
Directly Attention Loss Adjusted Prioritized Experience Replay
Venue: arXiv cs.LG 2023
Year: 2023

Prioritized Experience Replay (PER) enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimation deviation. In this article, an novel off policy reinforcement learning training framework called Directly Attention Loss Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network, so as to accurately compensate the error. In addition, a Priority-Encouragement mechanism is designed simultaneously to optimize the sample screening criterion, and further improve the training efficiency. In order to verify the effectiveness and generality of DALAP, we integrate it with the value-function based, the policy-gradient based and multi-agent reinforcement learning algorithm, respectively. The multiple groups of comparative experiments show that DALAP has the significant advantages of both improving the convergence rate and reducing the training variance.
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design
Venue: arXiv cs.LG 2023
Year: 2023

Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present "LLamol", a single novel generative transformer model based on the LLama 2 architecture, which was trained on a 13M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce "Stochastic Context Learning" as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making LLamol a potent tool for de novo molecule design, easily expandable with new properties.
Petit programming language and compiler
Venue: arXiv cs.PL 2023
Year: 2023

Petit is an educational programming language for learning compilers. Students embark on the journey of learning compilers through a series of six tutorials, progressing from topics like lexical analysis and syntactic analysis to semantic analysis and code generation. The initial tutorials in this series cover the practical applications of the lex and yacc tools, while the concluding tutorial focuses on generating LLVM intermediate representation.
Refinement Proofs in Rust Using Ghost Locks
Venue: arXiv cs.LO 2023
Year: 2023

Refinement transforms an abstract system model into a concrete, executable program, such that properties established for the abstract model carry over to the concrete implementation. Refinement has been used successfully in the development of substantial verified systems. Nevertheless, existing refinement techniques have limitations that impede their practical usefulness. Some techniques generate executable code automatically, which generally leads to implementations with sub-optimal performance. Others employ bottom-up program verification to reason about efficient implementations, but impose strict requirements on the structure of the code, the structure of the refinement proofs, as well as the employed verification logic and tools. In this paper, we present a novel refinement technique that removes these limitations. It supports a wide range of program structures, data representations, and proof structures. Our approach supports reasoning about both safety and liveness properties. We implement our approach in a state-of-the-art verifier for the Rust language, which itself offers a strong foundation for memory safety. We demonstrate the practicality of our approach on a number of substantial case studies.
Visualizing Plasma Physics Simulations in Immersive Environments
Venue: arXiv cs.GR 2023
Year: 2023

Plasma physics simulations create complex datasets for which researchers need state-of-the-art visualization tools to gain insights. These datasets are 3D in nature but are commonly depicted and analyzed using 2D idioms displayed on 2D screens. These offer limited understandability in a domain where spatial awareness is key. Virtual reality (VR) can be used as an alternative to conventional means for analyzing such datasets. VR has been known to improve depth and spatial relationship perception, which are fundamental for obtaining insights into 3D plasma morphology. Likewise, VR can potentially increase user engagement by offering more immersive and enjoyable experiences. Methods This study presents PlasmaVR, a proof-of-concept VR tool for visualizing datasets resulting from plasma physics simulations. It enables immersive multidimensional data visualization of particles, scalar, and vector fields and uses a more natural interface. The study includes user evaluation with domain experts where PlasmaVR was employed to assess the possible benefits of immersive environments in plasma physics visualization. The experimental group comprised five plasma physics researchers who were asked to perform tasks designed to represent their typical analysis workflow. To assess the suitability of the prototype for the different types of tasks, a set of objective metrics, such as completion time and number of errors, were measured. The prototype's usability was also evaluated using a standard System Usability Survey questionnaire.
MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors
Venue: arXiv cs.CR 2023
Year: 2023

As the complexities of processors keep increasing, the task of effectively verifying their integrity and security becomes ever more daunting. The intricate web of instructions, microarchitectural features, and interdependencies woven into modern processors pose a formidable challenge for even the most diligent verification and security engineers. To tackle this growing concern, recently, researchers have developed fuzzing techniques explicitly tailored for hardware processors. However, a prevailing issue with these hardware fuzzers is their heavy reliance on static strategies to make decisions in their algorithms. To address this problem, we develop a novel dynamic and adaptive decision-making framework, MABFuzz, that uses multi-armed bandit (MAB) algorithms to fuzz processors. MABFuzz is agnostic to, and hence, applicable to, any existing hardware fuzzer. In the process of designing MABFuzz, we encounter challenges related to the compatibility of MAB algorithms with fuzzers and maximizing their efficacy for fuzzing. We overcome these challenges by modifying the fuzzing process and tailoring MAB algorithms to accommodate special requirements for hardware fuzzing. We integrate three widely used MAB algorithms in a state-of-the-art hardware fuzzer and evaluate them on three popular RISC-V-based processors. Experimental results demonstrate the ability of MABFuzz to cover a broader spectrum of processors' intricate landscapes and doing so with remarkable efficiency. In particular, MABFuzz achieves up to 308x speedup in detecting vulnerabilities and up to 5x speedup in achieving coverage compared to a state-of-the-art technique.
Instance-Specific Asymmetric Sensitivity in Differential Privacy
Venue: arXiv cs.DS 2023
Year: 2023

We provide a new algorithmic framework for differentially private estimation of general functions that adapts to the hardness of the underlying dataset. We build upon previous work that gives a paradigm for selecting an output through the exponential mechanism based upon closeness of the inverse to the underlying dataset, termed the inverse sensitivity mechanism. Our framework will slightly modify the closeness metric and instead give a simple and efficient application of the sparse vector technique. While the inverse sensitivity mechanism was shown to be instance optimal, it was only with respect to a class of unbiased mechanisms such that the most likely outcome matches the underlying data. We break this assumption in order to more naturally navigate the bias-variance tradeoff, which will also critically allow for extending our method to unbounded data. In consideration of this tradeoff, we provide strong intuition and empirical validation that our technique will be particularly effective when the distances to the underlying dataset are asymmetric. This asymmetry is inherent to a range of important problems including fundamental statistics such as variance, as well as commonly used machine learning performance metrics for both classification and regression tasks. We efficiently instantiate our method in $O(n)$ time for these problems and empirically show that our techniques will give substantially improved differentially private estimations.
Real-time Digital Twins
Venue: arXiv cs.CY 2023
Year: 2023

We live in a world of exploding complexity driven by technical evolution as well as highly volatile socio-economic environments. Managing complexity is a key issue in everyday decision making such as providing safe, sustainable, and efficient industrial control solutions as well as solving today's global grand challenges such as the climate change. However, the level of complexity has well reached our cognitive capability to take informed decisions. Digital Twins, tightly integrating the real and the digital world, are a key enabler to support decision making for complex systems. They allow informing operational as well as strategic decisions upfront through accepted virtual predictions and optimizations of their real-world counter parts. Here we focus on real-time Digital Twins for online prediction and optimization of highly dynamic industrial assets and processes. They offer significant opportunities in the context of the industrial Internet of Things for novel and more effective control and optimization concepts. Thereby, they meet the Internet of Things needs for novel technologies to overcome today's limitations in terms of data availability in industrial contexts. Integrating today's seemingly complementary technologies of model-based and data-based, as well as edge-based and cloud-based approaches has the potential to re-imagine industrial process performance optimization solutions.
Social AI Improves Well-Being Among Female Young Adults
Venue: arXiv cs.CY 2023
Year: 2023

The rise of language models like ChatGPT has introduced Social AI as a new form of entertainment, particularly among young adults who engage with AI-powered agents. This paper investigates the effects of these interactions on users' social and mental well-being, a subject that has incited extensive debate among both the public and scholars. Our study involved a survey of 5,260 users of Chai, a Social AI Platform. The findings indicate significant benefits, with notable variations across demographics. Female users, in particular, reported the most substantial improvements: 43.4% strongly agreed that Social AI positively impacted their mental health, exceeding male users by 10.5%. In managing social anxieties, 38.9% of females strongly agreed on a positive impact, compared to 30.0% for males and 27.1% for other genders. Historically, new media and technology have often been met with groundless moral panic, with societal figures raising concerns without substantial evidence of harm. Our research indicates the importance of approaching such claims with caution and emphasizes the necessity of an evidence-based perspective in discussions about the behavioral effects of emerging technologies.
Multiagent Simulators for Social Networks
Venue: arXiv cs.SI 2023
Year: 2023

Multiagent social network simulations are an avenue that can bridge the communication gap between the public and private platforms in order to develop solutions to a complex array of issues relating to online safety. While there are significant challenges relating to the scale of multiagent simulations, efficient learning from observational and interventional data to accurately model micro and macro-level emergent effects, there are equally promising opportunities not least with the advent of large language models that provide an expressive approximation of user behavior. In this position paper, we review prior art relating to social network simulation, highlighting challenges and opportunities for future work exploring multiagent security using agent-based models of social networks
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance
Venue: arXiv cs.IR 2023
Year: 2023

Relevance module plays a fundamental role in e-commerce search as they are responsible for selecting relevant products from thousands of items based on user queries, thereby enhancing users experience and efficiency. The traditional approach models the relevance based product titles and queries, but the information in titles alone maybe insufficient to describe the products completely. A more general optimization approach is to further leverage product image information. In recent years, vision-language pre-training models have achieved impressive results in many scenarios, which leverage contrastive learning to map both textual and visual features into a joint embedding space. In e-commerce, a common practice is to fine-tune on the pre-trained model based on e-commerce data. However, the performance is sub-optimal because the vision-language pre-training models lack of alignment specifically designed for queries. In this paper, we propose a method called Query-LIFE (Query-aware Language Image Fusion Embedding) to address these challenges. Query-LIFE utilizes a query-based multimodal fusion to effectively incorporate the image and title based on the product types. Additionally, it employs query-aware modal alignment to enhance the accuracy of the comprehensive representation of products. Furthermore, we design GenFilt, which utilizes the generation capability of large models to filter out false negative samples and further improve the overall performance of the contrastive learning task in the model. Experiments have demonstrated that Query-LIFE outperforms existing baselines. We have conducted ablation studies and human evaluations to validate the effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been deployed on Miravia Search, resulting in improved both relevance and conversion efficiency.
Application of Reconfigurable All-Optical Activation Unit based on Optical Injection into Bistable Fabry-Pérot Laser in Multilayer Perceptron Neural Networks
Venue: arXiv cs.ET 2023
Year: 2023

In this paper we theoretically investigate application of a bistable Fabry-Pérot semiconductor laser under optical-injection as all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.
Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning
Venue: arXiv cs.CV 2023
Year: 2023

Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the completeness of extracted visual features from both seen and unseen classes. However, as a common practice in GZSL, the pre-trained feature extractor may easily exhibit difficulty in capturing domain-specific traits of the downstream tasks/datasets to provide fine-grained discriminative features, i.e., domain bias, which hinders the overall recognition performance, especially for unseen classes. Recent studies partially address this issue by fine-tuning feature extractors, while may inevitably incur catastrophic forgetting and overfitting issues. In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features. Specifically, our method consists of two key components, i.e., Unseen-Aware Distillation (UAD) and Attribute-Guided Learning (AGL). During training, UAD exploits the prior knowledge of attribute texts that are shared by both seen/unseen classes with attention mechanisms to detect and maintain unseen class-sensitive visual features in a targeted manner, and meanwhile, AGL aims to steer the model to focus on valuable features and suppress them to fit noisy elements in the seen classes by attribute-guided representation learning. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method.
Task-Distributionally Robust Data-Free Meta-Learning
Venue: arXiv cs.LG 2023
Year: 2023

Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. Existing inversion-based DFML methods construct pseudo tasks from a learnable dataset, which is inversely generated from the pre-trained model pool. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift (TDS) and Task-Distribution Corruption (TDC). TDS leads to a biased meta-learner because of the skewed task distribution towards newly generated tasks. TDC occurs when untrusted models characterized by misleading labels or poor quality pollute the task distribution. To tackle these issues, we introduce a robust DFML framework that ensures task distributional robustness. We propose to meta-learn from a pseudo task distribution, diversified through task interpolation within a compact task-memory buffer. This approach reduces the meta-learner's overreliance on newly generated tasks by maintaining consistent performance across a broader range of interpolated memory tasks, thus ensuring its generalization for unseen tasks. Additionally, our framework seamlessly incorporates an automated model selection mechanism into the meta-training phase, parameterizing each model's reliability as a learnable weight. This is optimized with a policy gradient algorithm inspired by reinforcement learning, effectively addressing the non-differentiable challenge posed by model selection. Comprehensive experiments across various datasets demonstrate the framework's effectiveness in mitigating TDS and TDC, underscoring its potential to improve DFML in real-world scenarios.
Tamper-Evident Pairing
Venue: arXiv cs.CR 2023
Year: 2023

Establishing a secure connection between wireless devices has become significantly important with the increasing number of Wi-Fi products coming to the market. In order to provide an easy and secure pairing standard, the Wi-Fi Alliance has designed the Wi-Fi Protected Setup. Push-Button Configuration (PBC) is part of this standard and is especially useful for pairing devices with physical limitations. However, PBC is proven to be vulnerable to man-in-the-middle (MITM) attacks. Tamper-Evident Pairing (TEP) is an improvement of the PBC standard, which aims to fix the MITM vulnerability without interfering the useful properties of PBC. It relies on the Tamper-Evident Announcement (TEA), which guarantees that an adversary can neither tamper a transmitted message without being detected, nor hide the fact that the message has been sent. The security properties of TEP were proven manually by its authors and tested with the Uppaal and Spin model checkers. During the Uppaal model checking, no vulnerabilities were found. However, the Spin model revealed a case, in which the TEP's security is not guaranteed. In this paper, we first provide a comprehensive overview of the TEP protocol, including all information needed to understand how it works. Furthermore, we summarize the security checks performed on it, give the circumstances, under which it is no longer resistant to MITM attacks and explain the reasons why they could not be revealed with the first model. Nevertheless, future work is required to gain full certainty of the TEP's security before applying it in the industry.
Weak Alignment Supervision from Hybrid Model Improves End-to-end ASR
Venue: arXiv cs.SD 2023
Year: 2023

In this paper, we aim to create weak alignment supervision from an existing hybrid system to aid the end-to-end modeling of automatic speech recognition. Towards this end, we use the existing hybrid ASR system to produce triphone alignments of the training audios. We then create a cross-entropy loss at a certain layer of the encoder using the derived alignments. In contrast to the general one-hot cross-entropy losses, here we use a cross-entropy loss with a label smoothing parameter to regularize the supervision. As a comparison, we also conduct the experiments with one-hot cross-entropy losses and CTC losses with loss weighting. The results show that placing the weak alignment supervision with the label smoothing parameter of 0.5 at the third encoder layer outperforms the other two approaches and leads to about 5\% relative WER reduction on the TED-LIUM 2 dataset over the baseline. We see similar improvements when applying the method out-of-the-box on a Tagalog end-to-end ASR system.