OpenRaiser/Intern-Atlas huggingface dataset:
Human-AI Coordination via Human-Regularized Search and Learning
Venue: arXiv cs.AI 2022
Year: 2022

We consider the problem of making AI agents that collaborate well with humans in partially observable fully cooperative environments given datasets of human behavior. Inspired by piKL, a human-data-regularized search method that improves upon a behavioral cloning policy without diverging far away from it, we develop a three-step algorithm that achieve strong performance in coordinating with real humans in the Hanabi benchmark. We first use a regularized search algorithm and behavioral cloning to produce a better human model that captures diverse skill levels. Then, we integrate the policy regularization idea into reinforcement learning to train a human-like best response to the human model. Finally, we apply regularized search on top of the best response policy at test time to handle out-of-distribution challenges when playing with humans. We evaluate our method in two large scale experiments with humans. First, we show that our method outperforms experts when playing with a group of diverse human players in ad-hoc teams. Second, we show that our method beats a vanilla best response to behavioral cloning baseline by having experts play repeatedly with the two agents.
ACRNet: Attention Cube Regression Network for Multi-view Real-time 3D Human Pose Estimation in Telemedicine
Venue: arXiv cs.CV 2022
Year: 2022

Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine has long received attention. Although the development of deep learning has made HPE methods in telemedicine simpler and easier to use, addressing low accuracy and high latency remains a big challenge. In this paper, we propose a novel multi-view Attention Cube Regression Network (ACRNet), which regresses the 3D position of joints in real time by aggregating informative attention points on each cube surface. More specially, a cube whose each surface contains uniformly distributed attention points with specific coordinate values is first created to wrap the target from the main view. Then, our network regresses the 3D position of each joint by summing and averaging the coordinates of attention points on each surface after being weighted. To verify our method, we first tested ACRNet on the open-source ITOP dataset; meanwhile, we collected a new multi-view upper body movement dataset (UBM) on the trunk support trainer (TruST) to validate the capability of our model in real rehabilitation scenarios. Experimental results demonstrate the superiority of ACRNet compared with other state-of-the-art methods. We also validate the efficacy of each module in ACRNet. Furthermore, Our work analyzes the performance of ACRNet under the medical monitoring indicator. Because of the high accuracy and running speed, our model is suitable for real-time telemedicine settings. The source code is available at https://github.com/BoceHu/ACRNet
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
Venue: arXiv cs.LG 2022
Year: 2022

Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accuracy while maintaining low inference latency. To this end, we deploy a lightweight model on the edge and a heavyweight model on the cloud. A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model. Moreover, a supervised learning (SL)-based offloading strategy is applied to determine whether the sample should be processed on the edge or on the cloud. Our method is implemented on real hardware and tested on multiple datasets. The experimental results show that (1) The sizes of the lightweight models obtained by RL-based DNN compression are up to 87.6% smaller than those obtained by the baseline method; (2) SL-based offloading strategy makes correct offloading decisions in most cases; (3) Our method reduces up to 78.8% inference latency and achieves higher accuracy compared with the cloud-only strategy.
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval
Venue: arXiv cs.CL 2022
Year: 2022

Legal case retrieval, which aims to retrieve relevant cases given a query case, plays an essential role in the legal system. While recent research efforts improve the performance of traditional ad-hoc retrieval models, legal case retrieval is still challenging since queries are legal cases, which contain hundreds of tokens. Legal cases are much longer and more complicated than keywords queries. Apart from that, the definition of legal relevance is beyond the general definition. In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case. In this paper, we propose an interaction-focused network for legal case retrieval with a multi-view contrastive learning objective. The contrastive learning views, including case-view and element-view, aim to overcome the above challenges. The case-view contrastive learning minimizes the hidden space distance between relevant legal case representations produced by a pre-trained language model (PLM) encoder. The element-view builds positive and negative instances by changing legal elements of cases to help the network better compute legal relevance. To achieve this, we employ a legal element knowledge-aware indicator to detect legal elements of cases. We conduct extensive experiments on the benchmark of relevant case retrieval. Evaluation results indicate our proposed method obtains significant improvement over the existing methods.
GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models
Venue: arXiv cs.SD 2022
Year: 2022

We propose AudioStyleGAN (ASGAN), a new generative adversarial network (GAN) for unconditional speech synthesis. As in the StyleGAN family of image synthesis models, ASGAN maps sampled noise to a disentangled latent vector which is then mapped to a sequence of audio features so that signal aliasing is suppressed at every layer. To successfully train ASGAN, we introduce a number of new techniques, including a modification to adaptive discriminator augmentation to probabilistically skip discriminator updates. ASGAN achieves state-of-the-art results in unconditional speech synthesis on the Google Speech Commands dataset. It is also substantially faster than the top-performing diffusion models. Through a design that encourages disentanglement, ASGAN is able to perform voice conversion and speech editing without being explicitly trained to do so. ASGAN demonstrates that GANs are still highly competitive with diffusion models. Code, models, samples: https://github.com/RF5/simple-asgan/.
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Venue: arXiv cs.CL 2022
Year: 2022

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis. Unlike English, there is a lack of high-performing open-source Chinese KEPLMs in the natural language processing (NLP) community to support various language understanding applications. In this paper, we revisit and advance the development of Chinese natural language understanding with a series of novel Chinese KEPLMs released in various parameter sizes, namely CKBERT (Chinese knowledge-enhanced BERT).Specifically, both relational and linguistic knowledge is effectively injected into CKBERT based on two novel pre-training tasks, i.e., linguistic-aware masked language modeling and contrastive multi-hop relation modeling. Based on the above two pre-training paradigms and our in-house implemented TorchAccelerator, we have pre-trained base (110M), large (345M) and huge (1.3B) versions of CKBERT efficiently on GPU clusters. Experiments demonstrate that CKBERT outperforms strong baselines for Chinese over various benchmark NLP tasks and in terms of different model sizes.
Formalizing May's Theorem
Venue: arXiv cs.LO 2022
Year: 2022

This report presents a formalization of May's theorem in the proof assistant Coq. It describes how the theorem statement is first translated into Coq definitions, and how it is subsequently proved. Various aspects of the proof and related work are discussed. To the best of the author's knowledge, this project is the first documented attempt in mechanizing May's Theorem.
A new perspective on Digital Twins: Imparting intelligence and agency to entities
Venue: arXiv cs.HC 2022
Year: 2022

Despite the Digital Twin (DT) concept being in the industry for a long time, it remains ambiguous, unable to differentiate itself from information models, general computing, and simulation technologies. Part of this confusion stems from previous studies overlooking the DT's bidirectional nature, that enables the shift of agency (delegating control) from humans to physical elements, something that was not possible with earlier technologies. Thus, we present DTs in a new light by viewing them as a means of imparting intelligence and agency to entities, emphasizing that DTs are not just expert-centric tools but are active systems that extend the capabilities of the entities being twinned. This new perspective on DTs can help reduce confusion and humanize the concept by starting discussions about how intelligent a DT should be, and its roles and responsibilities, as well as setting a long-term direction for DTs.
Mind's Eye: Grounded Language Model Reasoning through Simulation
Venue: arXiv cs.CL 2022
Year: 2022

Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world -- their failure to relate language to the physical world causes knowledge to be misrepresented and obvious mistakes in their reasoning. We present Mind's Eye, a paradigm to ground language model reasoning in the physical world. Given a physical reasoning question, we use a computational physics engine (DeepMind's MuJoCo) to simulate the possible outcomes, and then use the simulation results as part of the input, which enables language models to perform reasoning. Experiments on 39 tasks in a physics alignment benchmark demonstrate that Mind's Eye can improve reasoning ability by a large margin (27.9% zero-shot, and 46.0% few-shot absolute accuracy improvement on average). Smaller language models armed with Mind's Eye can obtain similar performance to models that are 100x larger. Finally, we confirm the robustness of Mind's Eye through ablation studies.
Hybrid MBlur: Using Ray Tracing to Solve the Partial Occlusion Artifacts in Real-Time Rendering of Motion Blur Effect
Venue: arXiv cs.GR 2022
Year: 2022

For a foreground object in motion, details of its background which would otherwise be hidden are uncovered through its inner blur. This paper presents a novel hybrid motion blur rendering technique combining post-process image filtering and hardware-accelerated ray tracing. In each frame, we advance rays recursively into the scene to retrieve background information for inner blur regions and apply a post-process filtering pass on the ray-traced background and rasterized colour before compositing them together. Our approach achieves more accurate partial occlusion semi-transparencies for moving objects while maintaining interactive frame rates.
Cloud-Assisted Hybrid Rendering for Thin-Client Games and VR Applications
Venue: arXiv cs.GR 2022
Year: 2022

We introduce a novel distributed rendering approach to generate high-quality graphics in thin-client games and VR applications. Many mobile devices have limited computational power to achieve ray tracing in real-time. Hence, hardware-accelerated cloud servers can perform ray tracing instead and have their output streamed to clients in remote rendering. Applying the approach of distributed hybrid rendering, we leverage the computational capabilities of both the thin client and powerful server by performing rasterization locally while offloading ray tracing to the server. With advancements in 5G technology, the server and client can communicate effectively over the network and work together to produce a high-quality output while maintaining interactive frame rates. Our approach can achieve better visuals as compared to local rendering but faster performance as compared to remote rendering.
MIMO Capacity Characterization for Movable Antenna Systems
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose a new multiple-input multiple-output (MIMO) communication system with movable antennas (MAs) to exploit the antenna position optimization for enhancing the capacity. Different from conventional MIMO systems with fixed-position antennas (FPAs), the proposed system can flexibly change the positions of transmit/receive MAs, such that the MIMO channel between them is reconfigured to achieve higher capacity. We aim to characterize the capacity of MA-enabled point-to-point MIMO communication systems, by jointly optimizing the positions of transmit and receive MAs as well as the covariance of transmit signals. First, we develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix and the position of each transmit/receive MA with the other variables being fixed. Next, we propose alternative algorithms of lower complexity for capacity maximization in the low-SNR regime and for the multiple-input single-output (MISO) and single-input multiple-output (SIMO) cases. Numerical results show that our proposed MA systems significantly improve the MIMO channel capacity compared to traditional FPA systems as well as various benchmark schemes, and useful insights are drawn into the capacity gains of MA systems.
From Earth to Space: A First Deployment of 5G Core Network on Satellite
Venue: arXiv cs.NI 2022
Year: 2022

Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites. The new trend enables the paradigm shift of satellite-terrestrial integrated networks with global coverage. In particular, the integration of 5G communication systems and satellites has the potential to restructure next-generation mobile networks. By leveraging the network function virtualization and network slicing, the orbital 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks. We are the first to deploy a lightweight 5G core network on a real-world satellite to investigate its feasibility. We conducted experiments to validate the onboard 5G core network functions. The validated procedures include registration and session setup procedures. The results show that the 5G core network can function normally and generate correct signaling.
COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Venue: arXiv cs.CL 2022
Year: 2022

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets into various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, they still need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models' relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Venue: arXiv cs.GT 2022
Year: 2022

No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achieving optimal performance in domains involving cooperation with humans. We address this shortcoming by first introducing a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We prove that this is a no-regret learning algorithm under a modified utility function. We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model. We used RL-DiL-piKL to train an agent we name Diplodocus. In a 200-game no-press Diplomacy tournament involving 62 human participants spanning skill levels from beginner to expert, two Diplodocus agents both achieved a higher average score than all other participants who played more than two games, and ranked first and third according to an Elo ratings model.
Style-Guided Inference of Transformer for High-resolution Image Synthesis
Venue: arXiv cs.CV 2022
Year: 2022

Transformer is eminently suitable for auto-regressive image synthesis which predicts discrete value from the past values recursively to make up full image. Especially, combined with vector quantised latent representation, the state-of-the-art auto-regressive transformer displays realistic high-resolution images. However, sampling the latent code from discrete probability distribution makes the output unpredictable. Therefore, it requires to generate lots of diverse samples to acquire desired outputs. To alleviate the process of generating lots of samples repetitively, in this article, we propose to take a desired output, a style image, as an additional condition without re-training the transformer. To this end, our method transfers the style to a probability constraint to re-balance the prior, thereby specifying the target distribution instead of the original prior. Thus, generated samples from the re-balanced prior have similar styles to reference style. In practice, we can choose either an image or a category of images as an additional condition. In our qualitative assessment, we show that styles of majority of outputs are similar to the input style.
Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
Venue: arXiv cs.CV 2022
Year: 2022

Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper provides an alternative, Gaussian formulation of the latent space of various diffusion models, as well as an invertible DPM-Encoder that maps images into the latent space. While our formulation is purely based on the definition of diffusion models, we demonstrate several intriguing consequences. (1) Empirically, we observe that a common latent space emerges from two diffusion models trained independently on related domains. In light of this finding, we propose CycleDiffusion, which uses DPM-Encoder for unpaired image-to-image translation. Furthermore, applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors. (2) One can guide pre-trained diffusion models and GANs by controlling the latent codes in a unified, plug-and-play formulation based on energy-based models. Using the CLIP model and a face recognition model as guidance, we demonstrate that diffusion models have better coverage of low-density sub-populations and individuals than GANs. The code is publicly available at https://github.com/ChenWu98/cycle-diffusion.
MTet: Multi-domain Translation for English and Vietnamese
Venue: arXiv cs.CL 2022
Year: 2022

We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation. MTet consists of 4.2M high-quality training sentence pairs and a multi-domain test set refined by the Vietnamese research community. Combining with previous works on English-Vietnamese translation, we grow the existing parallel dataset to 6.2M sentence pairs. We also release the first pretrained model EnViT5 for English and Vietnamese languages. Combining both resources, our model significantly outperforms previous state-of-the-art results by up to 2 points in translation BLEU score, while being 1.6 times smaller.
A Formal Assisted Approach for Modeling and Testing Security Attacks in IoT Edge Devices
Venue: arXiv cs.CR 2022
Year: 2022

With the rapid growth in the number of IoT devices being added to the network, a major concern that arises is the security of these systems. As these devices are resource constrained, safety measures are difficult to implement on the edge. We propose a novel approach for the detection of IoT device attacks based on the use of formal modeling and mutation testing. Namely, we model the behavior of small IoT devices such as motion sensors and RFID reader as state machines with timeouts. We also model basic IoT attacks; namely, battery draining, sleep deprivation, data falsification, replay, and man in the middle attacks, as special mutants of these specifications. We also consider tests for detecting actual physical device manipulation. Mutation testing is then used to derive tests that distinguish these attacks from the original specifications. The behavior of these mutants is tested in real environment by running the tests on them. Our experiments show that derived the number of attack mutants and tests is small and thus these tests can be executed many times with limited overhead on the physical device. Consequently, our approach is not deterred by related high costs of traditional mutation testing. In addition, we also show that tests derived by our method which cover all IoT attacks do not provide good coverage of mutants derived using traditional mutation code-based operators and this indicates the need of using our method. A framework that implements our approach is presented along with some other relevant case studies.