OpenRaiser/Intern-Atlas huggingface dataset:
Quick Relaxation in Collective Motion
Venue: arXiv cs.MA 2022
Year: 2022

We establish sufficient conditions for the quick relaxation to kinetic equilibrium in the classic Vicsek-Cucker-Smale model of bird flocking. The convergence time is polynomial in the number of birds as long as the number of flocks remains bounded. This new result relies on two key ingredients: exploiting the convex geometry of embedded averaging systems; and deriving new bounds on the s-energy of disconnected agreement systems. We also apply our techniques to bound the relaxation time of certain pattern-formation robotic systems investigated by Sugihara and Suzuki.
Offset equivariant networks and their applications
Venue: arXiv cs.CV 2022
Year: 2022

In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input. In a suitable color space this kind of networks achieves equivariance with respect to the photometric transformations that characterize changes in the lighting conditions. We verified the framework on three different problems: image recognition, illuminant estimation, and image inpainting. Our experiments show that the performance of offset equivariant networks are comparable to those in the state of the art on regular data. Differently from conventional networks, however, equivariant networks do behave consistently well when the color of the illuminant changes.
A Note on Process Modelling: Combining Situation Calculus and Petri Nets
Venue: arXiv cs.LO 2022
Year: 2022

The situation calculus logic model is convenient for modelling the actions that can occur in an information system application. The interplay of pre-conditions and post-conditions determines a semantically justified partial order of the defined actions and serves to enforce integrity constraints. This form of specification allows the use of plan-generation algorithms to investigate, before the system is adopted, whether the proposed specification allows all desirable use cases, and effectively disallows undesirable ones. Especially for legacy applications, implemented without a prior specification, Process Mining techniques were employed to derive an implicit Petri net model from the analysis of a large number of traces registered in an execution log. However, if the system just begins to be used, and has a still empty execution log, this sort of process mining discovery would not be feasible. This paper explains how the Petri net model can be directly derived from the situation calculus specification rules. The main gist is to provide evidence that the two models are complementary, not only because the Petri net model is derivable from the situation calculus model, but also in view of the distinct advantages of the two models. While the situation calculus model leads to planning and simulated execution prior to implementation, the Petri net model can be designed to run in a restrictive mode, allowing an intuitive visualization of the workable sequences. As proof of concept, the paper describes a prototype to demonstrate the methods and applies it to two examples: a published request processing application used to introduce process mining notions; and an analogously structured trial by combat application taken from a popular movie. The prototype includes an interactive dramatization component, which enacts the second application.
SDRTV-to-HDRTV via Hierarchical Dynamic Context Feature Mapping
Venue: arXiv cs.MM 2022
Year: 2022

In this work, we address the task of SDR videos to HDR videos(SDRTV-to-HDRTV). Previous approaches use global feature modulation for SDRTV-to-HDRTV. Feature modulation scales and shifts the features in the original feature space, which has limited mapping capability. In addition, the global image mapping cannot restore detail in HDR frames due to the luminance differences in different regions of SDR frames. To resolve the appeal, we propose a two-stage solution. The first stage is a hierarchical Dynamic Context feature mapping (HDCFM) model. HDCFM learns the SDR frame to HDR frame mapping function via hierarchical feature modulation (HME and HM ) module and a dynamic context feature transformation (DCT) module. The HME estimates the feature modulation vector, HM is capable of hierarchical feature modulation, consisting of global feature modulation in series with local feature modulation, and is capable of adaptive mapping of local image features. The DCT module constructs a feature transformation module in conjunction with the context, which is capable of adaptively generating a feature transformation matrix for feature mapping. Compared with simple feature scaling and shifting, the DCT module can map features into a new feature space and thus has a more excellent feature mapping capability. In the second stage, we introduce a patch discriminator-based context generation model PDCG to obtain subjective quality enhancement of over-exposed regions. PDCG can solve the problem that the model is challenging to train due to the proportion of overexposed regions of the image. The proposed method can achieve state-of-the-art objective and subjective quality results. Specifically, HDCFM achieves a PSNR gain of 0.81 dB at a parameter of about 100K. The number of parameters is 1/14th of the previous state-of-the-art methods. The test code will be released soon.
HyperTensioN and Total-order Forward Decomposition optimizations
Venue: arXiv cs.AI 2022
Year: 2022

Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, used in the HTN IPC 2020.
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
Venue: arXiv cs.AI 2022
Year: 2022

Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example, in wind farm control a trade-off exists between maximising power and minimising stress on the systems components. Dependencies between turbines arise due to the wake effect. We model such sparse dependencies between agents as a multi-objective coordination graph (MO-CoG). In multi-objective reinforcement learning a utility function is typically used to model a users preferences over objectives, which may be unknown a priori. In such settings a set of optimal policies must be computed. Which policies are optimal depends on which optimality criterion applies. If the utility function of a user is derived from multiple executions of a policy, the scalarised expected returns (SER) must be optimised. If the utility of a user is derived from a single execution of a policy, the expected scalarised returns (ESR) criterion must be optimised. For example, wind farms are subjected to constraints and regulations that must be adhered to at all times, therefore the ESR criterion must be optimised. For MO-CoGs, the state-of-the-art algorithms can only compute a set of optimal policies for the SER criterion, leaving the ESR criterion understudied. To compute a set of optimal polices under the ESR criterion, also known as the ESR set, distributions over the returns must be maintained. Therefore, to compute a set of optimal policies under the ESR criterion for MO-CoGs, we present a novel distributional multi-objective variable elimination (DMOVE) algorithm. We evaluate DMOVE in realistic wind farm simulations. Given the returns in real-world wind farm settings are continuous, we utilise a model known as real-NVP to learn the continuous return distributions to calculate the ESR set.
AUDITEM: Toward an Automated and Efficient Data Integrity Verification Model Using Blockchain
Venue: arXiv cs.CR 2022
Year: 2022

Data tampering is often considered a severe problem in industrial applications as it can lead to inaccurate financial reports or even a corporate security crisis. A correct representation of data is essential for companies' core business processes and is demanded by investors and customers. Traditional data audits are performed through third-party auditing services; however, these services are expensive and can be untrustworthy in some cases. Blockchain and smart contracts provide a decentralized mechanism to achieve secure and trustworthy data integrity verification; however, existing solutions present challenges in terms of scalability, privacy protection, and compliance with data regulations. In this paper, we propose the AUtomated and Decentralized InTegrity vErification Model (AUDITEM) to assist business stakeholders in verifying data integrity in a trustworthy and automated manner. To address the challenges in existing integrity verification processes, our model uses carefully designed smart contracts and a distributed file system to store integrity verification attributes and uses blockchain to enhance the authenticity of data certificates. A sub-module called Data Integrity Verification Tool (DIVT) is also developed to support easy-to-use interfaces and customizable verification operations. This paper presents a detailed implementation and designs experiments to verify the proposed model. The experimental and analytical results demonstrate that our model is feasible and efficient to meet various business requirements for data integrity verification.
Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs
Venue: arXiv cs.LG 2022
Year: 2022

There has been a growing interest in the use of Deep Neural Networks (DNNs) to solve Partial Differential Equations (PDEs). Despite the promise that such approaches hold, there are various aspects where they could be improved. Two such shortcomings are (i) their computational inefficiency relative to classical numerical methods, and (ii) the non-interpretability of a trained DNN model. In this work we present ASPINN, an anisotropic extension of our earlier work called SPINN--Sparse, Physics-informed, and Interpretable Neural Networks--to solve PDEs that addresses both these issues. ASPINNs generalize radial basis function networks. We demonstrate using a variety of examples involving elliptic and hyperbolic PDEs that the special architecture we propose is more efficient than generic DNNs, while at the same time being directly interpretable. Further, they improve upon the SPINN models we proposed earlier in that fewer nodes are require to capture the solution using ASPINN than using SPINN, thanks to the anisotropy of the local zones of influence of each node. The interpretability of ASPINN translates to a ready visualization of their weights and biases, thereby yielding more insight into the nature of the trained model. This in turn provides a systematic procedure to improve the architecture based on the quality of the computed solution. ASPINNs thus serve as an effective bridge between classical numerical algorithms and modern DNN based methods to solve PDEs. In the process, we also streamline the training of ASPINNs into a form that is closer to that of supervised learning algorithms.
Swiss German Speech to Text system evaluation
Venue: arXiv cs.CL 2022
Year: 2022

We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and referred to as system a-d in this report. We compare the four systems to our STT model, referred to as FHNW from hereon after, and provide details on how we trained our model. To evaluate the models, we use two STT datasets from different domains. The Swiss Parliament Corpus (SPC) test set and a private dataset in the news domain with an even distribution across seven dialect regions. We provide a detailed error analysis to detect the three systems' strengths and weaknesses. This analysis is limited by the characteristics of the two test sets. Our model scored the highest bilingual evaluation understudy (BLEU) on both datasets. On the SPC test set, we obtain a BLEU score of 0.607, whereas the best commercial system reaches a BLEU score of 0.509. On our private test set, we obtain a BLEU score of 0.722 and the best commercial system a BLEU score of 0.568.
Review of 5G capabilities for smart manufacturing
Venue: arXiv cs.NI 2022
Year: 2022

5G has been defined to address new use cases beyond consumer-focused mobile broadband services. In particular industrial use cases, for example in smart manufacturing, have been addressed in the 5G standardization, so that 5G can support industrial IoT services and wireless industrial networking. To this end, 5G needs to integrate with the industrial network based on Ethernet and TSN. 5G can create new opportunities for smart manufacturing by enabling flexibility and increasing the automation in the production. This paper provides an overview of the use cases and requirements for smart manufacturing that can be addressed with 5G and which are validated in three industrial 5G trial systems. The capabilities of 5G are described for providing non-public networks that support industrial LAN services based on Ethernet and TSN. The 5G radio access network functionality is described and discussed for practical deployments. The paper provides an overview of the current state-of-the-art of using 5G for smart manufacturing.
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision-making problem. However, poor runtime safety hinders RL-based decision-making strategies from complex driving tasks in practice. To address this problem, human demonstrations are incorporated into the RL-based decision-making strategy in this paper. Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL. A complex lane change task in an off-ramp scenario is established to examine the performance of the developed strategy. Simulation results suggest that human demonstrations can effectively improve the safety of decisions of RL. And the proposed strategy surpasses other existing learning-based decision-making strategies with respect to multiple driving performances.
RUCA: RUntime Configurable Approximate Circuits with Self-Correcting Capability
Venue: arXiv cs.AR 2022
Year: 2022

Approximate computing is an emerging computing paradigm that offers improved power consumption by relaxing the requirement for full accuracy. Since real-world applications may have different requirements for design accuracy, one trend of approximate computing is to design runtime quality-configurable circuits, which are able to operate under different accuracy modes with different power consumption. In this paper, we present a novel framework RUCA which aims to approximate an arbitrary input circuit in a runtime configurable fashion. By factorizing and decomposing the truth table, our approach aims to approximate and separate the input circuit into multiple configuration blocks which support different accuracy levels, including a corrector circuit to restore full accuracy. By activating different blocks, the approximate circuit is able to operate at different accuracy-power configurations. To improve the scalability of our algorithm, we also provide a design space exploration scheme with circuit partitioning to navigate the search space of possible approximations of subcircuits during design time. We thoroughly evaluate our methodology on a set of benchmarks and compare against another quality-configurable approach, showcasing the benefits and flexibility of RUCA. For 3-level designs, RUCA saves power consumption by 36.57% within 1% error and by 51.32% within 2% error on average.
Lifelong Inverse Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user. However, even for a single task, LfD may require numerous demonstrations. For versatile agents that must learn many tasks via demonstration, this process would substantially burden the user if each task were learned in isolation. To address this challenge, we introduce the novel problem of lifelong learning from demonstration, which allows the agent to continually build upon knowledge learned from previously demonstrated tasks to accelerate the learning of new tasks, reducing the amount of demonstrations required. As one solution to this problem, we propose the first lifelong learning approach to inverse reinforcement learning, which learns consecutive tasks via demonstration, continually transferring knowledge between tasks to improve performance.
Automatic Integration of BFT State-Machine Replication into IoT Systems
Venue: arXiv cs.DC 2022
Year: 2022

Byzantine fault tolerance (BFT) can preserve the availability and integrity of IoT systems where single components may suffer from random data corruption or attacks that can expose them to malicious behavior. While state-of-the-art BFT state-machine replication (SMR) libraries are often tailored to fit a standard request-response interaction model with dedicated client-server roles, in our design, we employ an IoT-fit interaction model that assumes a loosly-coupled, event-driven interaction between arbitrarily wired IoT components. In this paper, we explore the possibility of automating and streamlining the complete process of integrating BFT SMR into a component-based IoT execution environment. Our main goal is providing simplicity for the developer: We strive to decouple the specification of a logical application architecture from the difficulty of incorporating BFT replication mechanisms into it. Thus, our contributions address the automated configuration, re-wiring and deployment of IoT components, and their replicas, within a component-based, event-driven IoT platform.
Behavioral Player Rating in Competitive Online Shooter Games
Venue: arXiv cs.LG 2022
Year: 2022

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several features from in-game statistics to model players and create ratings that accurately represent their behavior and true performance level. We then compare the estimating power of our behavioral ratings against ratings created with three mainstream rating systems by predicting rank of players in four popular game modes from the competitive shooter genre. Our results show that the behavioral ratings present more accurate performance estimations while maintaining the interpretability of the created representations. Considering different aspects of the playing behavior of players and using behavioral ratings for matchmaking can lead to match-ups that are more aligned with players' goals and interests, consequently resulting in a more enjoyable gaming experience.
The "AI+R"-tree: An Instance-optimized R-tree
Venue: arXiv cs.DB 2022
Year: 2022

The emerging class of instance-optimized systems has shown potential to achieve high performance by specializing to a specific data and query workloads. Particularly, Machine Learning (ML) techniques have been applied successfully to build various instance-optimized components (e.g., learned indexes). This paper investigates to leverage ML techniques to enhance the performance of spatial indexes, particularly the R-tree, for a given data and query workloads. As the areas covered by the R-tree index nodes overlap in space, upon searching for a specific point in space, multiple paths from root to leaf may potentially be explored. In the worst case, the entire R-tree could be searched. In this paper, we define and use the overlap ratio to quantify the degree of extraneous leaf node accesses required by a range query. The goal is to enhance the query performance of a traditional R-tree for high-overlap range queries as they tend to incur long running-times. We introduce a new AI-tree that transforms the search operation of an R-tree into a multi-label classification task to exclude the extraneous leaf node accesses. Then, we augment a traditional R-tree to the AI-tree to form a hybrid "AI+R"-tree. The "AI+R"-tree can automatically differentiate between the high- and low-overlap queries using a learned model. Thus, the "AI+R"-tree processes high-overlap queries using the AI-tree, and the low-overlap queries using the R-tree. Experiments on real datasets demonstrate that the "AI+R"-tree can enhance the query performance over a traditional R-tree by up to 500%.
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Venue: arXiv cs.LG 2022
Year: 2022

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers -- long short-term memory and gated recurrent unit -- within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
Venue: arXiv cs.AI 2022
Year: 2022

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.