OpenRaiser/Intern-Atlas huggingface dataset:
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
Venue: arXiv cs.IR 2022
Year: 2022

Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because of the sparse environment in recommender systems (RS), DRL agents could spend times either exploring informative user-item interaction trajectories or using existing trajectories for policy learning. It is also known as the exploration and exploitation trade-off which affects the recommendation performance significantly when the environment is sparse. It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems. As a step to address this issue, We design a novel intrinsically ,otivated reinforcement learning method to increase the capability of exploring informative interaction trajectories in the sparse environment, which are further enriched via a counterfactual augmentation strategy for more efficient exploitation. The extensive experiments on six offline datasets and three online simulation platforms demonstrate the superiority of our model to a set of existing state-of-the-art methods.
Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. We develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the 'tolerated' image size. We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications.
PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments
Venue: arXiv cs.RO 2022
Year: 2022

LiDAR sensors are a powerful tool for robot simultaneous localization and mapping (SLAM) in unknown environments, but the raw point clouds they produce are dense, computationally expensive to store, and unsuited for direct use by downstream autonomy tasks, such as motion planning. For integration with motion planning, it is desirable for SLAM pipelines to generate lightweight geometric map representations. Such representations are also particularly well-suited for man-made environments, which can often be viewed as a so-called "Manhattan world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM algorithm for Manhattan world environments which extracts planar features from point clouds to achieve lightweight, real-time localization and mapping. Our approach generates plane-based maps which occupy significantly less memory than their point cloud equivalents, and are suited towards fast collision checking for motion planning. By leveraging the Manhattan world assumption, we target extraction of orthogonal planes to generate maps which are more structured and organized than those of existing plane-based LiDAR SLAM approaches. We demonstrate our approach in the high-fidelity AirSim simulator and in real-world experiments with a ground rover equipped with a Velodyne LiDAR. For both cases, we are able to generate high quality maps and trajectory estimates at a rate matching the sensor rate of 10 Hz.
Synthesizing Nested Relational Queries from Implicit Specifications
Venue: arXiv cs.DB 2022
Year: 2022

Derived datasets can be defined implicitly or explicitly. An implicit definition (of dataset $O$ in terms of datasets $\vec{I}$) is a logical specification involving the source data $\vec{I}$ and the interface data $O$. It is a valid definition of $O$ in terms of $\vec{I}$, if any two models of the specification agreeing on $\vec{I}$ agree on $O$. In contrast, an explicit definition is a query that produces $O$ from $\vec{I}$. Variants of Beth's theorem state that one can convert implicit definitions to explicit ones. Further, this conversion can be done effectively given a proof witnessing implicit definability in a suitable proof system. We prove the analogous effective implicit-to-explicit result for nested relations: implicit definitions, given in the natural logic for nested relations, can be effectively converted to explicit definitions in the nested relational calculus NRC. As a consequence, we can effectively extract rewritings of NRC queries in terms of NRC views, given a proof witnessing that the query is determined by the views.
Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
Venue: arXiv cs.RO 2022
Year: 2022

Edge Computing is a promising technology to provide new capabilities in technological fields that require instantaneous data processing. Researchers in areas such as machine and deep learning use extensively edge and cloud computing for their applications, mainly due to the significant computational and storage resources that they provide. Currently, Robotics is seeking to take advantage of these capabilities as well, and with the development of 5G networks, some existing limitations in the field can be overcome. In this context, it is important to know how to utilize the emerging edge architectures, what types of edge architectures and platforms exist today and which of them can and should be used based on each robotic application. In general, Edge platforms can be implemented and used differently, especially since there are several providers offering more or less the same set of services with some essential differences. Thus, this study addresses these discussions for those who work in the development of the next generation robotic systems and will help to understand the advantages and disadvantages of each edge computing architecture in order to choose wisely the right one for each application.
TeleSHift: Telexisting TUI for Physical Collaboration & Interaction
Venue: arXiv cs.HC 2022
Year: 2022

Current workflow on co-editing and simultaneous presentation of 3-D shapes is confined to on-screen manipulation, which causes loss of perceived information when presenting perceptual concepts or complex shapes between members. Thus, we create TeleSHift, a 3-D tangible user interface (TUI) with a telexisting communication framework for group-based collaboration and demonstration. In this work, we present a larger-scaled proof-of-concept prototype providing hands-on operation for shape-based interactions including multi-sided collaboration and one-to-many presentation. In contrast to previous works, we further extend the use of TUIs to support cooperative tasks with telexistence while enabling the linkage of manipulable bits to provide a better user experience and interactivity.
2x2 MIMO Prototype for BER and EVM Measurements in Metal Enclosure
Venue: arXiv cs.IT 2022
Year: 2022

In this work, we present a 2x2 near-field multi-input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-field MIMO prototype is developed using software-defined-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We check the near-field MIMO BER and EVM measurements in three different scenarios in a highly reflecting metal enclosure environment. In the first scenario, the line-of-sight (LOS) communication link is investigated when the mode-stirrer is stationary. In stationary channel conditions near-field MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements are performed in dynamic channel conditions when the mode-stirrer is set to move continuously. In the third scenario, LOS communication near-field MIMO BER and EVM measurements are performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-field MIMO BER and EVM measurements are investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference.
Survival of dominated strategies under imitation dynamics
Venue: arXiv cs.GT 2022
Year: 2022

The literature on evolutionary game theory suggests that pure strategies that are strictly dominated by other pure strategies always become extinct under imitative game dynamics, but they can survive under innovative dynamics. As we explain, this is because innovative dynamics favour rare strategies while standard imitative dynamics do not. However, as we also show, there are reasonable imitation protocols that favour rare or frequent strategies, thus allowing strictly dominated strategies to survive in large classes of imitation dynamics. Dominated strategies can persist at nontrivial frequencies even when the level of domination is not small.
Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video
Venue: arXiv cs.CV 2022
Year: 2022

Spatial-temporal Map (STMap)-based methods have shown great potential to process high-angle videos for vehicle trajectory reconstruction, which can meet the needs of various data-driven modeling and imitation learning applications. In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that imposes parity constraints at both pixel and instance levels to generate instance-aware embeddings for vehicle stripe segmentation on STMap. At pixel level, each pixel was encoded with its 8-neighbor pixels at different ranges, and this encoding is subsequently used to guide a neural network to learn the embedding mechanism. At the instance level, a discriminative loss function is designed to pull pixels belonging to the same instance closer and separate the mean value of different instances far apart in the embedding space. The output of the spatial-temporal affinity is then optimized by the mutex-watershed algorithm to obtain final clustering results. Based on segmentation metrics, our model outperformed five other baselines that have been used for STMap processing and shows robustness under the influence of shadows, static noises, and overlapping. The designed model is applied to process all public NGSIM US-101 videos to generate complete vehicle trajectories, indicating a good scalability and adaptability. Last but not least, the strengths of the scanline method with STDE and future directions were discussed. Code, STMap dataset and video trajectory are made publicly available in the online repository. GitHub Link: shorturl.at/jklT0.
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treating the disease. In particular, despite an increased number of curative resections, many postoperative patients still develop recurrent lesions. Consequently, there is a significant need for prognostic tools that can more accurately predict a patient's risk for recurrence. In this paper, we explore the use of convolutional neural networks (CNNs) for the segmentation and recurrence risk prediction of lung tumors that are present in preoperative computed tomography (CT) images. First, expanding upon recent progress in medical image segmentation, a residual U-Net is used to localize and characterize each nodule. Then, the identified tumors are passed to a second CNN for recurrence risk prediction. The system's final results are produced with a random forest classifier that synthesizes the predictions of the second network with clinical attributes. The segmentation stage uses the LIDC-IDRI dataset and achieves a dice score of 70.3%. The recurrence risk stage uses the NLST dataset from the National Cancer institute and achieves an AUC of 73.0%. Our proposed framework demonstrates that first, automated nodule segmentation methods can generalize to enable pipelines for a wide range of multitask systems and second, that deep learning and image processing have the potential to improve current prognostic tools. To the best of our knowledge, it is the first fully automated segmentation and recurrence risk prediction system.
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
Venue: arXiv cs.IR 2022
Year: 2022

In this work, we present our journey to revolutionize the personalized recommendation engine through end-to-end learning from raw user actions. We encode user's long-term interest in Pinner- Former, a user embedding optimized for long-term future actions via a new dense all-action loss, and capture user's short-term intention by directly learning from the real-time action sequences. We conducted both offline and online experiments to validate the performance of the new model architecture, and also address the challenge of serving such a complex model using mixed CPU/GPU setup in production. The proposed system has been deployed in production at Pinterest and has delivered significant online gains across organic and Ads applications.
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
Venue: arXiv cs.CV 2022
Year: 2022

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.
VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification
Venue: arXiv cs.CV 2022
Year: 2022

Human texture perception is a weighted average of multi-sensory inputs: visual and tactile. While the visual sensing mechanism extracts global features, the tactile mechanism complements it by extracting local features. The lack of coupled visuotactile datasets in the literature is a challenge for studying multimodal fusion strategies analogous to human texture perception. This paper presents a visual dataset that augments an existing tactile dataset. We propose a novel deep fusion architecture that fuses visual and tactile data using four types of fusion strategies: summation, concatenation, max-pooling, and attention. Our model shows significant performance improvements (97.22%) in surface roughness classification accuracy over tactile only (SVM - 92.60%) and visual only (FENet-50 - 85.01%) architectures. Among the several fusion techniques, attention-guided architecture results in better classification accuracy. Our study shows that analogous to human texture perception, the proposed model chooses a weighted combination of the two modalities (visual and tactile), thus resulting in higher surface roughness classification accuracy; and it chooses to maximize the weightage of the tactile modality where the visual modality fails and vice-versa.
A Benchmark for Understanding and Generating Dialogue between Characters in Stories
Venue: arXiv cs.CL 2022
Year: 2022

Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which requires capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines.
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
Venue: arXiv cs.CV 2022
Year: 2022

We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms. SF2SE3 then iteratively (1) samples pixel sets to compute SE(3)-motion proposals, and (2) selects the best SE(3)-motion proposal with respect to a maximum coverage formulation. Finally, objects are formed by assigning pixels uniquely to the selected SE(3)-motions based on consistency with the input scene flow and spatial proximity. The main novelties are a more informed strategy for the sampling of motion proposals and a maximum coverage formulation for the proposal selection. We conduct evaluations on multiple datasets regarding application of SF2SE3 for scene flow estimation, object segmentation and visual odometry. SF2SE3 performs on par with the state of the art for scene flow estimation and is more accurate for segmentation and odometry.
RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System
Venue: arXiv cs.RO 2022
Year: 2022

Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.
Distribution inference risks: Identifying and mitigating sources of leakage
Venue: arXiv cs.CR 2022
Year: 2022

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the goal of an adversary is to infer distributional information about the training data. So far, research on distribution inference has focused on demonstrating successful attacks, with little attention given to identifying the potential causes of the leakage and to proposing mitigations. To bridge this gap, as our main contribution, we theoretically and empirically analyze the sources of information leakage that allows an adversary to perpetrate distribution inference attacks. We identify three sources of leakage: (1) memorizing specific information about the $\mathbb{E}[Y|X]$ (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data. Next, based on our analysis, we propose principled mitigation techniques against distribution inference attacks. Specifically, we demonstrate that causal learning techniques are more resilient to a particular type of distribution inference risk termed distributional membership inference than associative learning methods. And lastly, we present a formalization of distribution inference that allows for reasoning about more general adversaries than was previously possible.