OpenRaiser/Intern-Atlas huggingface dataset:
Transfer learning for process design with reinforcement learning
Venue: arXiv cs.LG 2023
Year: 2023

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process simulation in an iterative design process. However, one major challenge in the learning process is that the RL agent demands numerous process simulations in rigorous process simulators, thereby requiring long simulation times and expensive computational power. Therefore, typically short-cut simulation methods are employed to accelerate the learning process. Short-cut methods can, however, lead to inaccurate results. We thus propose to utilize transfer learning for process design with RL in combination with rigorous simulation methods. Transfer learning is an established approach from machine learning that stores knowledge gained while solving one problem and reuses this information on a different target domain. We integrate transfer learning in our RL framework for process design and apply it to an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles, our method can design economically feasible flowsheets with stable interaction with DWSIM. Our results show that transfer learning enables RL to economically design feasible flowsheets with DWSIM, resulting in a flowsheet with an 8% higher revenue. And the learning time can be reduced by a factor of 2.
AniPixel: Towards Animatable Pixel-Aligned Human Avatar
Venue: arXiv cs.CV 2023
Year: 2023

Although human reconstruction typically results in human-specific avatars, recent 3D scene reconstruction techniques utilizing pixel-aligned features show promise in generalizing to new scenes. Applying these techniques to human avatar reconstruction can result in a volumetric avatar with generalizability but limited animatability due to rendering only being possible for static representations. In this paper, we propose AniPixel, a novel animatable and generalizable human avatar reconstruction method that leverages pixel-aligned features for body geometry prediction and RGB color blending. Technically, to align the canonical space with the target space and the observation space, we propose a bidirectional neural skinning field based on skeleton-driven deformation to establish the target-to-canonical and canonical-to-observation correspondences. Then, we disentangle the canonical body geometry into a normalized neutral-sized body and a subject-specific residual for better generalizability. As the geometry and appearance are closely related, we introduce pixel-aligned features to facilitate the body geometry prediction and detailed surface normals to reinforce the RGB color blending. We also devise a pose-dependent and view direction-related shading module to represent the local illumination variance. Experiments show that AniPixel renders comparable novel views while delivering better novel pose animation results than state-of-the-art methods.
Multiple-bounce Smith Microfacet BRDFs using the Invariance Principle
Venue: arXiv cs.GR 2023
Year: 2023

Smith microfacet models are widely used in computer graphics to represent materials. Traditional microfacet models do not consider the multiple bounces on microgeometries, leading to visible energy missing, especially on rough surfaces. Later, as the equivalence between the microfacets and volume has been revealed, random walk solutions have been proposed to introduce multiple bounces, but at the cost of high variance. Recently, the position-free property has been introduced into the multiple-bounce model, resulting in much less noise, but also bias or a complex derivation. In this paper, we propose a simple way to derive the multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) using the invariance principle. At the core of our model is a shadowing-masking function for a path consisting of direction collections, rather than separated bounces. Our model ensures unbiasedness and can produce less noise compared to the previous work with equal time, thanks to the simple formulation. Furthermore, we also propose a novel probability density function (PDF) for BRDF multiple importance sampling, which has a better match with the multiple-bounce BRDFs, producing less noise than previous naive approximations.
SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Learning to segment images purely by relying on the image-text alignment from web data can lead to sub-optimal performance due to noise in the data. The noise comes from the samples where the associated text does not correlate with the image's visual content. Instead of purely relying on the alignment from the noisy data, this paper proposes a novel loss function termed SimCon, which accounts for intra-modal similarities to determine the appropriate set of positive samples to align. Further, using multiple views of the image (created synthetically) for training and combining the SimCon loss with it makes the training more robust. This version of the loss is termed MV-SimCon. The empirical results demonstrate that using the proposed loss function leads to consistent improvements on zero-shot, text supervised semantic segmentation and outperforms state-of-the-art by $+3.0\%$, $+3.3\%$ and $+6.9\%$ on PASCAL VOC, PASCAL Context and MSCOCO, respectively. With test time augmentations, we set a new record by improving these results further to $58.7\%$, $26.6\%$, and $33.3\%$ on PASCAL VOC, PASCAL Context, and MSCOCO, respectively. In addition, using the proposed loss function leads to robust training and faster convergence.
Weighted Tree Automata with Constraints
Venue: arXiv cs.FL 2023
Year: 2023

The HOM problem, which asks whether the image of a regular tree language under a given tree homomorphism is again regular, is known to be decidable [Godoy & Giménez: The HOM problem is decidable. JACM 60(4), 2013]. However, the problem remains open for regular weighted tree languages. It is demonstrated that the main notion used in the unweighted setting, the tree automaton with equality and inequality constraints, can straightforwardly be generalized to the weighted setting and can represent the image of any regular weighted tree language under any nondeleting and nonerasing tree homomorphism. Several closure properties as well as decision problems are also investigated for the weighted tree languages generated by weighted tree automata with constraints.
Natural Language Processing for Policymaking
Venue: arXiv cs.CL 2023
Year: 2023

Language is the medium for many political activities, from campaigns to news reports. Natural language processing (NLP) uses computational tools to parse text into key information that is needed for policymaking. In this chapter, we introduce common methods of NLP, including text classification, topic modeling, event extraction, and text scaling. We then overview how these methods can be used for policymaking through four major applications including data collection for evidence-based policymaking, interpretation of political decisions, policy communication, and investigation of policy effects. Finally, we highlight some potential limitations and ethical concerns when using NLP for policymaking. This text is from Chapter 7 (pages 141-162) of the Handbook of Computational Social Science for Policy (2023). Open Access on Springer: https://doi.org/10.1007/978-3-031-16624-2
Tool interoperability for model-based systems engineering
Venue: arXiv cs.SE 2023
Year: 2023

Supervisory control design of cyber-physical systems has many challenges. Model-based systems engineering can address these, with solutions originating from various disciplines. We discuss several tools, each state-of-the-art in its own discipline, offering functionality such as specification, synthesis, and verification. Integrating such mono-disciplinary tools in a multi-disciplinary workflow is a major challenge. We present Analytics as a Service, built on the Arrowhead framework, to connect these tools and make them interoperable. A seamless integration of the tools has been established through a service-oriented architecture: The engineer can easily access the functionality of the tools from a single interface, as translation steps between equivalent models for the respective tools are automated.
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
Venue: arXiv cs.CL 2023
Year: 2023

A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes. To help modelling the dialogue and enriching the context, we leverage the pre-trained knowledge adapters to infuse linguistic and factual knowledge. Experiments show that our method achieves new state-of-the-art results with 69.81% on IEMOCAP, 65.7% on MELD, and 62.51% on DailyDialog datasets. The analysis also proves that the VAD space is not only suitable for ERC but also interpretable, with VAD prototypes enhancing its performance and stabilising the training of SCCL. In addition, the pre-trained knowledge adapters benefit the performance of the utterance encoder and SCCL. Our code is available at: https://github.com/SteveKGYang/SCCL
From Utilitarian to Rawlsian Designs for Algorithmic Fairness
Venue: arXiv cs.CY 2023
Year: 2023

There is a lack of consensus within the literature as to how `fairness' of algorithmic systems can be measured, and different metrics can often be at odds. In this paper, we approach this task by drawing on the ethical frameworks of utilitarianism and John Rawls. Informally, these two theories of distributive justice measure the `good' as either a population's sum of utility, or worst-off outcomes, respectively. We present a parameterized class of objective functions that interpolates between these two (possibly) conflicting notions of the `good'. This class is shown to represent a relaxation of the Rawlsian `veil of ignorance', and its sequence of optimal solutions converges to both a utilitarian and Rawlsian optimum. Several other properties of this class are studied, including: 1) a relationship to regularized optimization, 2) feasibility of consistent estimation, and 3) algorithmic cost. In several real-world datasets, we compute optimal solutions and construct the tradeoff between utilitarian and Rawlsian notions of the `good'. Empirically, we demonstrate that increasing model complexity can manifest strict improvements to both measures of the `good'. This work suggests that the proper degree of `fairness' can be informed by a designer's preferences over the space of induced utilitarian and Rawlsian `good'.
Adaptive Aggregation for Safety-Critical Control
Venue: arXiv cs.LG 2023
Year: 2023

Safety has been recognized as the central obstacle to preventing the use of reinforcement learning (RL) for real-world applications. Different methods have been developed to deal with safety concerns in RL. However, learning reliable RL-based solutions usually require a large number of interactions with the environment. Likewise, how to improve the learning efficiency, specifically, how to utilize transfer learning for safe reinforcement learning, has not been well studied. In this work, we propose an adaptive aggregation framework for safety-critical control. Our method comprises two key techniques: 1) we learn to transfer the safety knowledge by aggregating the multiple source tasks and a target task through the attention network; 2) we separate the goal of improving task performance and reducing constraint violations by utilizing a safeguard. Experiment results demonstrate that our algorithm can achieve fewer safety violations while showing better data efficiency compared with several baselines.
Does Adding Physical Realism to Virtual Reality Training Reduce Time Compression?
Venue: arXiv cs.HC 2023
Year: 2023

Virtual reality (VR) is known to cause a "time compression" effect, where the time spent in VR feels to pass faster than the effective elapsed time. Our goal with this research is to investigate if the physical realism of a VR experience reduces the time compression effect on a gas monitoring training task that requires precise time estimation. We used physical props and passive haptics in a VR task with high physical realism and compared it to an equivalent standard VR task with only virtual objects. We also used an identical real-world task as a baseline time estimation task. Each scenario includes the user picking up a device, opening a door, navigating a corridor with obstacles, performing five short time estimations, and estimating the total time from task start to end. Contrary to previous work, there was a consistent time dilation effect in all conditions, including the real world. However, no significant effects were found comparing the estimated differences between the high and low physical realism conditions. We discuss implications of the results and limitations of the study and propose future work that may better address this important question for virtual reality training.
Tight algorithms for connectivity problems parameterized by clique-width
Venue: arXiv cs.DS 2023
Year: 2023

The complexity of problems involving global constraints is usually much more difficult to understand than the complexity of problems only involving local constraints. A natural form of global constraints are connectivity constraints. We study connectivity problems from a fine-grained parameterized perspective. In a breakthrough, Cygan et al. (TALG 2022) first obtained algorithms with single-exponential running time c^{tw} n^O(1) for connectivity problems parameterized by treewidth by introducing the cut-and-count-technique. Furthermore, the obtained bases c were shown to be optimal under the Strong Exponential-Time Hypothesis (SETH). However, since only sparse graphs may admit small treewidth, we lack knowledge of the fine-grained complexity of connectivity problems with respect to dense structure. The most popular graph parameter to measure dense structure is arguably clique-width, which intuitively measures how easily a graph can be constructed by repeatedly adding bicliques. Bergougnoux and Kanté (TCS 2019) have shown, using the rank-based approach, that also parameterized by clique-width many connectivity problems admit single-exponential algorithms. Unfortunately, the obtained running times are far from optimal under SETH. We show how to obtain optimal running times parameterized by clique-width for two benchmark connectivity problems, namely Connected Vertex Cover and Connected Dominating Set. These are the first tight results for connectivity problems with respect to clique-width and these results are obtained by developing new algorithms based on the cut-and-count-technique and novel lower bound constructions. Precisely, we show that there exist one-sided error Monte-Carlo algorithms that given a k-clique-expression solve Connected Vertex Cover in time 6^k n^O(1), and Connected Dominating Set in time 5^k n^O(1). Both results are shown to be tight under SETH.
Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing
Venue: arXiv cs.CL 2023
Year: 2023

Writing is a complex process at the center of much of modern human activity. Despite it appears to be a linear process, writing conceals many highly non-linear processes. Previous research has focused on three phases of writing: planning, translation and transcription, and revision. While research has shown these are non-linear, they are often treated linearly when measured. Here, we introduce measures to detect and quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. We apply these to a novel dataset that recorded the creation of a text in all its phases, from early attempts to the finishing touches on a final version. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text. More than 60 junior researchers in science wrote a scientific essay intended for a general readership. We recorded each essay as a writing cloud, defined as a complex topological structure capturing the history of the essay itself. Through this unique dataset of writing clouds, we expose a representation of the writing process that quantifies its complexity and the writer's efforts throughout the draft and through time. Interestingly, this representation highlights the phases of "translation flow", where authors improve existing ideas, and exploration, where creative deviations appear as the writer returns to the planning phase. These turning points between translation and exploration become rarer as the writing process progresses and the author approaches the final version. Our results and the new measures introduced have the potential to foster the discussion about the non-linear nature of writing and support the development of tools that can support more creative and impactful writing processes.
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Venue: arXiv cs.LG 2023
Year: 2023

The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the modern payment network systems, which can generate millions of transactions per day across a large number of global institutions. Training a detection model of fraudulent transactions requires not only secured transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .
Revealing the Impact of SIC in NOMA-ISAC
Venue: arXiv cs.IT 2023
Year: 2023

The impact of successive interference cancellation (SIC) in non-orthogonal multiple access integrated sensing and communications (NOMA-ISAC) is analyzed. A two-stage SIC-based framework is proposed to deal with the inter-communication user and inter-functionality interferences. The performance of sensing and communications (S\&C) is analyzed for two SIC orders, i.e., the communications-centric SIC and the sensing-centric SIC. For each design, diversity orders, high signal-to-noise ratio (SNR) slopes, and high-SNR power offsets of the sensing rate (SR) and communication rate (CR) are derived as insights. Analytical results indicate that i) the main influence of SIC order on the SR and CR lies in the high-SNR power offsets; ii) ISAC provides more degrees of freedom than frequency-division S\&C (FDSAC). Numerical results show that the SR-CR region of ISAC entirely covers that of FDSAC.
Federated Learning with Regularized Client Participation
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the issue of partial participation, which occurs when a large number of clients are involved in the training process. The traditional method to address this problem is randomly selecting a subset of clients at each communication round. In our research, we propose a new technique and design a novel regularized client participation scheme. Under this scheme, each client joins the learning process every $R$ communication rounds, which we refer to as a meta epoch. We have found that this participation scheme leads to a reduction in the variance caused by client sampling. Combined with the popular FedAvg algorithm (McMahan et al., 2017), it results in superior rates under standard assumptions. For instance, the optimization term in our main convergence bound decreases linearly with the product of the number of communication rounds and the size of the local dataset of each client, and the statistical term scales with step size quadratically instead of linearly (the case for client sampling with replacement), leading to better convergence rate $\mathcal{O}\left(\frac{1}{T^2}\right)$ compared to $\mathcal{O}\left(\frac{1}{T}\right)$, where $T$ is the total number of communication rounds. Furthermore, our results permit arbitrary client availability as long as each client is available for training once per each meta epoch.
Mining Effective Features Using Quantum Entropy for Humor Recognition
Venue: arXiv cs.CL 2023
Year: 2023

Humor recognition has been extensively studied with different methods in the past years. However, existing studies on humor recognition do not understand the mechanisms that generate humor. In this paper, inspired by the incongruity theory, any joke can be divided into two components (the setup and the punchline). Both components have multiple possible semantics, and there is an incongruous relationship between them. We use density matrices to represent the semantic uncertainty of the setup and the punchline, respectively, and design QE-Uncertainty and QE-Incongruity with the help of quantum entropy as features for humor recognition. The experimental results on the SemEval2021 Task 7 dataset show that the proposed features are more effective than the baselines for recognizing humorous and non-humorous texts.
Adding Explicit Load-Acquire and Store-Release Instructions to the RISC-V ISA
Venue: arXiv cs.AR 2023
Year: 2023

Weak memory models allow for simplified hardware and increased performance in the memory hierarchy at the cost of increased software complexity. In weak memory models, explicit synchronization is needed to enforce ordering between different processors. Acquire and release semantics provide a powerful primitive for expressing only the ordering required for correctness. In this project, we explore adding load-acquire and store-release instructions to the RISC-V ISA. We add support to the herd formal memory model, the gem5 cycle-approximate simulator, and the LLVM/Clang toolchain. Because these instructions do not exist in the RISC-V standard, there is an inherent urgency to ratify explicit load-acquire/store-release instructions in order to prevent multiple ABI implementations and ecosystem fragmentation. We found that for workloads with a high degree of sharing and heavy contention, the impact of less memory ordering is muted, but our changes successfully encode the semantics we desire.