OpenRaiser/Intern-Atlas huggingface dataset:
Learning Modular Robot Locomotion from Demonstrations
Venue: arXiv cs.RO 2022
Year: 2022

Modular robots can be reconfigured to create a variety of designs from a small set of components. But constructing a robot's hardware on its own is not enough -- each robot needs a controller. One could create controllers for some designs individually, but developing policies for additional designs can be time consuming. This work presents a method that uses demonstrations from one set of designs to accelerate policy learning for additional designs. We leverage a learning framework in which a graph neural network is made up of modular components, each component corresponds to a type of module (e.g., a leg, wheel, or body) and these components can be recombined to learn from multiple designs at once. In this paper we develop a combined reinforcement and imitation learning algorithm. Our method is novel because the policy is optimized to both maximize a reward for one design, and simultaneously imitate demonstrations from different designs, within one objective function. We show that when the modular policy is optimized with this combined objective, demonstrations from one set of designs influence how the policy behaves on a different design, decreasing the number of training iterations needed.
Fully Adaptive Composition for Gaussian Differential Privacy
Venue: arXiv cs.CR 2022
Year: 2022

We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst. Such an analyst selects mechanisms (to be run on a sensitive data set) and their privacy budgets adaptively, that is, based on the answers from other mechanisms run previously on the same data set. In the language of Rogers, Roth, Ullman and Vadhan, this gives a filter for GDP with the same parameters as for nonadaptive composition.
High-Level Event Mining: A Framework
Venue: arXiv cs.DB 2022
Year: 2022

Process mining methods often analyze processes in terms of the individual end-to-end process runs. Process behavior, however, may materialize as a general state of many involved process components, which can not be captured by looking at the individual process instances. A more holistic state of the process can be determined by looking at the events that occur close in time and share common process capacities. In this work, we conceptualize such behavior using high-level events and propose a new framework for detecting and logging such high-level events. The output of our method is a new high-level event log, which collects all generated high-level events together with the newly assigned event attributes: activity, case, and timestamp. Existing process mining techniques can then be applied on the produced high-level event log to obtain further insights. Experiments on both simulated and real-life event data show that our method is able to automatically discover how system-level patterns such as high traffic and workload emerge, propagate and dissolve throughout the process.
How fair were COVID-19 restriction decisions? A data-driven investigation of England using the dominance-based rough sets approach
Venue: arXiv cs.CY 2022
Year: 2022

During the COVID-19 pandemic, several countries have taken the approach of tiered restrictions which has remained a point of debate due to a lack of transparency. Using the dominance-based rough set approach, we identify patterns in the COVID-19 data pertaining to the UK government's tiered restrictions allocation system. These insights from the analysis are translated into "if-then" type rules, which can easily be interpreted by policy makers. The differences in the rules extracted from different geographical areas suggest inconsistencies in the allocations of tiers in these areas. We found that the differences delineated an overall north south divide in England, however, this divide was driven mostly by London. Based on our analysis, we demonstrate the usefulness of the dominance-based rough sets approach for investigating the fairness and explainabilty of decision making regarding COVID-19 restrictions. The proposed approach and analysis could provide a more transparent approach to localised public health restrictions, which can help ensure greater conformity to the public safety rules.
Technology and COVID-19: How Reliant is Society on Technology?
Venue: arXiv cs.CY 2022
Year: 2022

Social media and messaging platforms have become a support system for those in fear of COVID-19 while, at the same time, becoming the root cause of spreading hate, inaccurate representations, and false realities. As technology has morphed into a commodity for daily tasks and actions, this article may be useful for people of all ages and backgrounds who are interested in understanding the impact of technology on society.
Denoising neural networks for magnetic resonance spectroscopy
Venue: arXiv cs.LG 2022
Year: 2022

In many scientific applications, measured time series are corrupted by noise or distortions. Traditional denoising techniques often fail to recover the signal of interest, particularly when the signal-to-noise ratio is low or when certain assumptions on the signal and noise are violated. In this work, we demonstrate that deep learning-based denoising methods can outperform traditional techniques while exhibiting greater robustness to variation in noise and signal characteristics. Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals that are often obscured by strong interference that can be difficult to separate from the signal using traditional methods. We explore various deep learning architecture choices to capture the inherently complex-valued nature of magnetic resonance signals. On both synthetic and experimental data, we show that our deep learning-based approaches can exceed performance of traditional techniques, providing a powerful new class of methods for analysis of scientific time series data.
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Venue: arXiv cs.CL 2022
Year: 2022

Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data are publicly available on Github and Huggingface.
UmeTrack: Unified multi-view end-to-end hand tracking for VR
Venue: arXiv cs.CV 2022
Year: 2022

Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producing root-relative (versus world space) 3D pose or rely on multiple stages such as generating heatmaps and kinematic optimization to obtain 3D pose. Moreover, the typical VR scenario, which involves multi-view tracking from wide \ac{fov} cameras is seldom addressed by these methods. In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space. We demonstrate the benefits of end-to-end differentiabilty by extending our framework with downstream tasks such as jitter reduction and pinch prediction. To demonstrate the efficacy of our model, we further present a new large-scale egocentric hand pose dataset that consists of both real and synthetic data. Experiments show that our system trained on this dataset handles various challenging interactive motions, and has been successfully applied to real-time VR applications.
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)
Venue: arXiv cs.AI 2022
Year: 2022

Explainable Artificial Intelligence (XAI) has recently gained a swell of interest, as many Artificial Intelligence (AI) practitioners and developers are compelled to rationalize how such AI-based systems work. Decades back, most XAI systems were developed as knowledge-based or expert systems. These systems assumed reasoning for the technical description of an explanation, with little regard for the user's cognitive capabilities. The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback, which are essential for XAI system evaluation. To this end, we propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding. In this regard, we adopt Bloom's taxonomy, a widely accepted model for assessing the user's cognitive capability. We utilize the counterfactual explanations as an explanation-providing medium encompassed with user feedback to validate the levels of understanding about the explanation at each cognitive level and improvise the explanation generation methods accordingly.
AI Assistants: A Framework for Semi-Automated Data Wrangling
Venue: arXiv cs.DB 2022
Year: 2022

Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
Venue: arXiv cs.LG 2022
Year: 2022

Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences. We adopt a recently developed transfer learning approach for PINNs and introduce a multi-head model to efficiently obtain accurate solutions to nonlinear systems of ordinary differential equations with random potentials. In particular, we apply the method to simulate stochastic branched flows, a universal phenomenon in random wave dynamics. Finally, we compare the results achieved by feed forward and GAN-based PINNs on two physically relevant transfer learning tasks and show that our methods provide significant computational speedups in comparison to standard PINNs trained from scratch.
Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features
Venue: arXiv cs.LG 2022
Year: 2022

Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, the system does not crash suddenly, the performance will be reduced for instance voltages fluctuation and current harmonics. A fault diagnosis method based on deep feedforward network with transient synthetic features is proposed to reduce the dependence on the fault mathematical models in this paper, which mainly uses the transient phase current to train the deep feedforward network classifier. Firstly, the features of fault phase current are analyzed in this paper. Secondly, the historical fault data after feature synthesis is employed to train the deep feedforward network classifier, and the average fault diagnosis accuracy can reach 97.85% for transient synthetic fault data, the classifier trained by the transient synthetic features obtained more than 1% gain in performance compared with original transient features. Finally, the online fault diagnosis experiments show that the method can accurately locate the fault IGBTs, and the final diagnosis result is determined by multiple groups results, which has the ability to increase the accuracy and reliability of the diagnosis results. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
Clustering-Based Approaches for Symbolic Knowledge Extraction
Venue: arXiv cs.AI 2022
Year: 2022

Opaque models belonging to the machine learning world are ever more exploited in the most different application areas. These models, acting as black boxes (BB) from the human perspective, cannot be entirely trusted if the application is critical unless there exists a method to extract symbolic and human-readable knowledge out of them. In this paper we analyse a recurrent design adopted by symbolic knowledge extractors for BB regressors - that is, the creation of rules associated with hypercubic input space regions. We argue that this kind of partitioning may lead to suboptimal solutions when the data set at hand is high-dimensional or does not satisfy symmetric constraints. We then propose a (deep) clustering-based approach to be performed before symbolic knowledge extraction to achieve better performance with data sets of any kind.
Empowering Data Centers for Next Generation Trusted Computing
Venue: arXiv cs.CR 2022
Year: 2022

Modern data centers have grown beyond CPU nodes to provide domain-specific accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud customers want to protect their data. They are willing to pay additional costs for trusted execution environments such as enclaves provided by Intel SGX and AMD SEV. Unfortunately, the customers have to make a critical choice -- either use domain-specific accelerators for speed or use CPU-based confidential computing solutions. To bridge this gap, we aim to enable data-center scale confidential computing that expands across CPUs and accelerators. We argue that having wide-scale TEE-support for accelerators presents a technically easier solution, but is far away from being a reality. Instead, our hybrid design provides enclaved execution guarantees for computation distributed over multiple CPU nodes and devices with/without TEE support. Our solution scales gracefully in two dimensions -- it can handle a large number of heterogeneous nodes and it can accommodate TEE-enabled devices as and when they are available in the future. We observe marginal overheads of $0.42$--$8\%$ on real-world AI data center workloads that are independent of the number of nodes in the data center. We add custom TEE support to two accelerators (AI and storage) and integrate it into our solution, thus demonstrating that it can cater to future TEE devices.
Real-Time Character Inverse Kinematics using the Gauss-Seidel Iterative Approximation Method
Venue: arXiv cs.GR 2022
Year: 2022

We present a realistic, robust, and computationally fast method of solving highly non-linear inverse kinematic problems with angular limits using the Gauss-Seidel iterative method. Our method is ideally suited towards character based interactive applications such as games. To achieve interactive simulation speeds, numerous acceleration techniques are employed, including spatial coherent starting approximations and projected angular clamping. The method has been tested on a continuous range of poses for animated articulated characters and successfully performed in all cases and produced good visual outcomes.
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Moreover, most previous methods are application-specific, and establishing a unified model for anomalies across application scenarios remains unsolved. This paper proposes a novel hybrid framework termed Siamese Transition Masked Autoencoders(ST-MAE) to handle various visual anomaly detection tasks uniformly via deep feature transition. Concretely, the proposed method first extracts hierarchical semantics features from a pre-trained deep convolutional neural network and then develops a feature decoupling strategy to split the deep features into two disjoint feature patch subsets. Leveraging the decoupled features, the ST-MAE is developed with the Siamese encoders that operate on each subset of feature patches and perform the latent representations transition of two subsets, along with a lightweight decoder that reconstructs the original feature from the transitioned latent representation. Finally, the anomalous attributes can be detected using the semantic deep feature residual. Our deep feature transition scheme yields a nontrivial and semantic self-supervisory task to extract prototypical normal patterns, which allows for learning uniform models that generalize well for different visual anomaly detection tasks. The extensive experiments conducted demonstrate that the proposed ST-MAE method can advance state-of-the-art performance on multiple benchmarks across application scenarios with a superior inference efficiency, which exhibits great potential to be the uniform model for unsupervised visual anomaly detection.
sRSP: GPUlarda Asimetrik Senkronizasyon Icin Yeni Olceklenebilir Bir Cozum
Venue: arXiv cs.DC 2022
Year: 2022

Asymmetric sharing is a dynamic sharing model, where a shared data is heavily accessed by a (local) sharer, and rarely accessed by other (remote) sharers. On GPUs, without special support, asymmetric sharing requires heavily loaded synchronization on every access. With the introduction of Remote Scope Promotion (RSP), access to the local sharer is allowed with lightweight synchronization, while heavyweight synchronization is only used for remote accesses where it is rarely needed. RSP ensures data consistency by promoting local synchronizations on remote accesses. Unfortunately, the first implementation of RSP is not a scalable solution. We offer a more efficient and scalable RSP implementation. This new design, which we call sRSP, is based on the monitoring of the local sharer and the selective execution of heavyweight synchronization operations. We evaluated the sRSP with the time-detailed Gem5-APU simulator and the results show that the sRSP improves performance by an average of 29 percent on a 64 Compute Unit GPU.
Oracle-guided Contrastive Clustering
Venue: arXiv cs.CV 2022
Year: 2022

Deep clustering aims to learn a clustering representation through deep architectures. Most of the existing methods usually conduct clustering with the unique goal of maximizing clustering performance, that ignores the personalized demand of clustering tasks.% and results in unguided clustering solutions. However, in real scenarios, oracles may tend to cluster unlabeled data by exploiting distinct criteria, such as distinct semantics (background, color, object, etc.), and then put forward personalized clustering tasks. To achieve task-aware clustering results, in this study, Oracle-guided Contrastive Clustering(OCC) is then proposed to cluster by interactively making pairwise ``same-cluster" queries to oracles with distinctive demands. Specifically, inspired by active learning, some informative instance pairs are queried, and evaluated by oracles whether the pairs are in the same cluster according to their desired orientation. And then these queried same-cluster pairs extend the set of positive instance pairs for contrastive learning, guiding OCC to extract orientation-aware feature representation. Accordingly, the query results, guided by oracles with distinctive demands, may drive the OCC's clustering results in a desired orientation. Theoretically, the clustering risk in an active learning manner is given with a tighter upper bound, that guarantees active queries to oracles do mitigate the clustering risk. Experimentally, extensive results verify that OCC can cluster accurately along the specific orientation and it substantially outperforms the SOTA clustering methods as well. To the best of our knowledge, it is the first deep framework to perform personalized clustering.
Learning in colloids: Synapse-like ZnO + DMSO colloid
Venue: arXiv cs.ET 2022
Year: 2022

Colloids submitted to electrical stimuli exhibit a reconfiguration that could be used to store information and, potentially, compute. We investigated learnign, memorization, and time and stimulation's voltage dependence of conductive network formation in a colloidal suspension of ZnO nanoparticles in DMSO. Relations between critical resistance and stimulation time were reconstructed. The critical voltage, i.e. the stimulation voltage necessary for dropping the resistance, was shown to decrease in response to an increase in stimulation time.
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
Venue: arXiv cs.CL 2022
Year: 2022

Pre-trained language models have achieved promising performance on general benchmarks, but underperform when migrated to a specific domain. Recent works perform pre-training from scratch or continual pre-training on domain corpora. However, in many specific domains, the limited corpus can hardly support obtaining precise representations. To address this issue, we propose a novel Transformer-based language model named VarMAE for domain-adaptive language understanding. Under the masked autoencoding objective, we design a context uncertainty learning module to encode the token's context into a smooth latent distribution. The module can produce diverse and well-formed contextual representations. Experiments on science- and finance-domain NLU tasks demonstrate that VarMAE can be efficiently adapted to new domains with limited resources.