OpenRaiser/Intern-Atlas huggingface dataset:
TMSTC*: A Turn-minimizing Algorithm For Multi-robot Coverage Path Planning
Venue: arXiv cs.RO 2022
Year: 2022

Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, coverage path planning benefits greatly from multiple robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree Coverage Star(TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on the MSTC*. Our algorithm partitions the map into minimum bricks as tree's branches and thereby transforms the problem into finding the maximum independent set of bipartite graph. We then connect bricks with greedy strategy to form a tree, aiming to reduce the number of turns of corresponding circumnavigating coverage path. Our experimental results show that our approach enables multiple robots to make fewer turns and thus complete terrain coverage tasks faster than other popular algorithms.
A Deep Learning Architecture for Passive Microwave Precipitation Retrievals using CloudSat and GPM Data
Venue: arXiv cs.LG 2022
Year: 2022

This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation. The neural networks learn from coincidences of brightness temperatures from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with the active precipitating retrievals from the Dual-frequency Precipitation Radar (DPR) onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The algorithm first detects the precipitation occurrence and phase and then estimates its rate, while conditioning the results to some key ancillary information including parameters related to cloud microphysical properties. The results indicate that we can reconstruct the DPR rainfall and CPR snowfall with a detection probability of more than 0.95 while the probability of a false alarm remains below 0.08 and 0.03, respectively. Conditioned to the occurrence of precipitation, the unbiased root mean squared error in estimation of rainfall (snowfall) rate using DPR (CPR) data is less than 0.8 (0.1) mm/hr over oceans and land. Beyond methodological developments, comparing the results with ERA5 reanalysis and official GPM products demonstrates that the uncertainty in global satellite snowfall retrievals continues to be large while there is a good agreement among rainfall products. Moreover, the results indicate that CPR active snowfall data can improve passive microwave estimates of global snowfall while the current CPR rainfall retrievals should only be used for detection and not estimation of rates.
Crowdsourcing Controller -- Utilizing Reliable Agents in a Multiplayer Game
Venue: arXiv cs.HC 2022
Year: 2022

This paper presents a new use case for continuous crowdsourcing, where multiple players collectively control a single character in a video game. Similar approaches have already been proposed, but they suffer from certain limitations: (1) they simply consider static time frames to group real-time inputs from multiple players; (2) then they aggregate inputs with simple majority vote, i.e., each player is uniformly weighted. We present a continuous crowdsourcing multiplayer game equipped with our Crowdsourcing Controller. The Crowdsourcing Controller addresses the above-mentioned limitations: (1) our Dynamic Input Frame approach groups incoming players' input in real-time by dynamically adjusting the frame length; (2) our Continuous Reliability System estimates players' skills by assigning them a reliability score, which is later used in a weighted majority vote to aggregate the final output command. We evaluated our Crowdsourcing Controller offline with simulated players and online with real players. Offline and online experiments show that both components of our Crowdsourcing Controller lead to higher game scores, i.e., longer playing time. Moreover, the Crowdsourcing Controller is able to correctly estimate and update players' reliability scores.
BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization
Venue: arXiv cs.CV 2022
Year: 2022

How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an effective BiSTNet to explore colors of reference exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the reference exemplars in deep feature space to explore color information from reference exemplars. Then, to better propagate the colors of reference exemplars into each frame and avoid the inaccurate matches colors from exemplars we develop a simple yet effective bidirectional temporal feature fusion module to better colorize each frame. We note that there usually exist color-bleeding artifacts around the boundaries of the important objects in videos. To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance. In addition, we develop a multi-scale recurrent block to progressively colorize frames in a coarse-to-fine manner. Extensive experimental results demonstrate that the proposed BiSTNet performs favorably against state-of-the-art methods on the benchmark datasets. Our code will be made available at \url{https://yyang181.github.io/BiSTNet/}
Entity Set Co-Expansion in StackOverflow
Venue: arXiv cs.CL 2022
Year: 2022

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Venue: arXiv cs.LG 2022
Year: 2022

To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance. However, how much and when an ML model should be compressed, and {\em where} its training should be executed, are hard decisions to make, as they depend on the model itself, the resources of the available nodes, and the data such nodes own. Existing studies focus on each of those aspects individually, however, they do not account for how such decisions can be made jointly and adapted to one another. In this work, we model the network system focusing on the training of DNNs, formalize the above multi-dimensional problem, and, given its NP-hardness, formulate an approximate dynamic programming problem that we solve through the PACT algorithmic framework. Importantly, PACT leverages a time-expanded graph representing the learning process, and a data-driven and theoretical approach for the prediction of the loss evolution to be expected as a consequence of training decisions. We prove that PACT's solutions can get as close to the optimum as desired, at the cost of an increased time complexity, and that, in any case, such complexity is polynomial. Numerical results also show that, even under the most disadvantageous settings, PACT outperforms state-of-the-art alternatives and closely matches the optimal energy cost.
Indoor room Occupancy Counting based on LSTM and Environmental Sensor
Venue: arXiv cs.LG 2022
Year: 2022

This paper realizes the estimation of classroom occupancy by using the CO2 sensor and deep learning technique named Long-Short-Term Memory. As a case of connection with IoT and machine learning, I achieve the model to estimate the people number in the classroom based on the environmental data exported from the CO2 sensor, I also evaluate the performance of the model to show the feasibility to apply our module to the real environment.
Two 6-approximation Algorithms for the Stochastic Score Classification Problem
Venue: arXiv cs.DS 2022
Year: 2022

We study the arbitrary cost case of the unweighted Stochastic Score Classification (SSClass) problem. We show two constant approximation algorithms and both algorithms are 6-approximation non-adaptive algorithms with respect to the optimal adaptive algorithm. The first algorithm uses a modified round-robin approach among three sequences, which is inspired by a recent result on the unit cost case of the SSClass problem. The second algorithm is originally from the work of Gkenosis et al. In our work, we successfully improve its approximation factor from 2(B-1) to 6. Our analysis heavily uses the relation between computation and verification of functions, which was studied in the information theory literature.
Cooperative control of environmental extremes by artificial intelligent agents
Venue: arXiv cs.MA 2022
Year: 2022

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential A.I. management of complex ecosystems are discussed.
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
Venue: arXiv cs.CV 2022
Year: 2022

In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictable camera view change, which we identify as another major factor leading to a poor generalization of ReID methods. To tackle the viewpoint change, this work proposes to use a 3D dense pose estimation model and a texture mapping module to map the pedestrian images to canonical view images. Due to the imperfection of the texture mapping module, the canonical view images may lose the discriminative detail clues from the original images, and thus directly using them for ReID will inevitably result in poor performance. To handle this issue, we propose to fuse the original image and canonical view image via a transformer-based module. The key insight of this design is that the cross-attention mechanism in the transformer could be an ideal solution to align the discriminative texture clues from the original image with the canonical view image, which could compensate for the low-quality texture information of the canonical view image. Through extensive experiments, we show that our method can lead to superior performance over the existing approaches in various evaluation settings.
Whale Casting: Remote mobile streaming humpback whale vocalizations to the world
Venue: arXiv cs.HC 2022
Year: 2022

Over several days in early August 2021, while at sea in Chatham Strait, Southeast Alaska, aboard M/Y Blue Pearl, an online twitch.tv stream broadcast in real-time humpback whale vocalizations monitored via hydrophone. Dozens on mainland North American and around the planet listened in and chatted via the stream. The webcasts demonstrated a proof-of-concept: only relatively inexpensive commercial-off-the-shelf equipment is required for remote mobile streaming at sea. These notes document what was required and make recommendations for higher-quality and larger-scale deployments. One conclusion is that real-time, automated audio documenting whale acoustic behavior is readily accessible and, using the cloud, it can be directly integrated into behavioral databases -- information sources that now often focus exclusively on nonreal-time visual-sighting narrative reports and photography.
PEANUT: Predicting and Navigating to Unseen Targets
Venue: arXiv cs.CV 2022
Year: 2022

Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our method differs from previous prediction-based navigation methods, such as frontier potential prediction or egocentric map completion, by directly predicting unseen targets while leveraging the global context from all previously explored areas. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data. Once trained, the model can be incorporated into a modular pipeline for ObjectNav without the need for any reinforcement learning. We validate the effectiveness of our method on the HM3D and MP3D ObjectNav datasets. We find that it achieves the state-of-the-art on both datasets, despite not using any additional data for training.
INCLUSIFY: A benchmark and a model for gender-inclusive German
Venue: arXiv cs.CL 2022
Year: 2022

Gender-inclusive language is important for achieving gender equality in languages with gender inflections, such as German. While stirring some controversy, it is increasingly adopted by companies and political institutions. A handful of tools have been developed to help people use gender-inclusive language by identifying instances of the generic masculine and providing suggestions for more inclusive reformulations. In this report, we define the underlying tasks in terms of natural language processing, and present a dataset and measures for benchmarking them. We also present a model that implements these tasks, by combining an inclusive language database with an elaborate sequence of processing steps via standard pre-trained models. Our model achieves a recall of 0.89 and a precision of 0.82 in our benchmark for identifying exclusive language; and one of its top five suggestions is chosen in real-world texts in 44% of cases. We sketch how the area could be further advanced by training end-to-end models and using large language models; and we urge the community to include more gender-inclusive texts in their training data in order to not present an obstacle to the adoption of gender-inclusive language. Through these efforts, we hope to contribute to restoring justice in language and, to a small extent, in reality.
Auxiliary Quantile Forecasting with Linear Networks
Venue: arXiv cs.LG 2022
Year: 2022

We propose a novel multi-task method for quantile forecasting with shared Linear layers. Our method is based on the Implicit quantile learning approach, where samples from the Uniform distribution $\mathcal{U}(0, 1)$ are reparameterized to quantile values of the target distribution. We combine the implicit quantile and input time series representations to directly forecast multiple quantile estimations for multiple horizons jointly. Prior works have adopted a Linear layer for the direct estimation of all forecasting horizons in a multi-task learning setup. We show that following similar intuition from multi-task learning to exploit correlations among forecast horizons, we can model multiple quantile estimates as auxiliary tasks for each of the forecast horizon to improve forecast accuracy across the quantile estimates compared to modeling only a single quantile estimate. We show learning auxiliary quantile tasks leads to state-of-the-art performance on deterministic forecasting benchmarks concerning the main-task of forecasting the 50$^{th}$ percentile estimate.
Learning Sampling Distributions for Model Predictive Control
Venue: arXiv cs.RO 2022
Year: 2022

Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics or cost function and are straightforward to parallelize. However, their efficacy is highly dependent on the quality of the sampling distribution itself, which is often assumed to be simple, like a Gaussian. This restriction can result in samples which are far from optimal, leading to poor performance. Recent work has explored improving the performance of MPC by sampling in a learned latent space of controls. However, these methods ultimately perform all MPC parameter updates and warm-starting between time steps in the control space. This requires us to rely on a number of heuristics for generating samples and updating the distribution and may lead to sub-optimal performance. Instead, we propose to carry out all operations in the latent space, allowing us to take full advantage of the learned distribution. Specifically, we frame the learning problem as bi-level optimization and show how to train the controller with backpropagation-through-time. By using a normalizing flow parameterization of the distribution, we can leverage its tractable density to avoid requiring differentiability of the dynamics and cost function. Finally, we evaluate the proposed approach on simulated robotics tasks and demonstrate its ability to surpass the performance of prior methods and scale better with a reduced number of samples.
Visual Area of Interests based Multimodal Trajectory Prediction for Probabilistic Risk Assessment
Venue: arXiv cs.HC 2022
Year: 2022

Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the driving intention and is about 0.6-2.1 s ahead of the operation. Therefore, we designed a trajectory prediction model that integrates the driving intention (DI) and the multimodal trajectory (MT) predictions. The DI model was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs.
Deterministic $K$-identification For Slow Fading Channel
Venue: arXiv cs.IT 2022
Year: 2022

Deterministic $K$-identification (DKI) is addressed for Gaussian channels with slow fading (GSF), where the transmitter is restricted to an average power constraint and channel side information is available at the decoder. We derive lower and upper bounds on the DKI capacity when the number of identifiable messages $K$ may grow sub-linearly with the codeword length $n$. As a key finding, we establish that for deterministic encoding, assuming that the number of identifiable messages $K = 2^{κ\log n}$ with $κ\in [0,1)$ being the identification target rate, the codebook size scales as $2^{(n\log n)R}$, where $R$ is the coding rate.
Pixel2ISDF: Implicit Signed Distance Fields based Human Body Model from Multi-view and Multi-pose Images
Venue: arXiv cs.CV 2022
Year: 2022

In this report, we focus on reconstructing clothed humans in the canonical space given multiple views and poses of a human as the input. To achieve this, we utilize the geometric prior of the SMPLX model in the canonical space to learn the implicit representation for geometry reconstruction. Based on the observation that the topology between the posed mesh and the mesh in the canonical space are consistent, we propose to learn latent codes on the posed mesh by leveraging multiple input images and then assign the latent codes to the mesh in the canonical space. Specifically, we first leverage normal and geometry networks to extract the feature vector for each vertex on the SMPLX mesh. Normal maps are adopted for better generalization to unseen images compared to 2D images. Then, features for each vertex on the posed mesh from multiple images are integrated by MLPs. The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space. Finally, latent code for each 3D point is extracted and utilized to calculate the SDF. Our work for reconstructing the human shape on canonical pose achieves 3rd performance on WCPA MVP-Human Body Challenge.
Emerging Technology and Policy Co-Design Considerations for the Safe and Transparent Use of Small Unmanned Aerial Systems
Venue: arXiv cs.CY 2022
Year: 2022

The rapid technological growth observed in the sUAS sector over the past decade has been unprecedented and has left gaps in policies and regulations to adequately provide for a safe and trusted environment in which to operate these devices. The Center for Security in Politics at UC Berkeley, via a two-day workshop, analyzed these gaps by addressing the entire sUAS vertical. From human factors to autonomy, we recommend a series of steps that can be taken by partners in the academic, commercial, and government sectors to reduce policy gaps introduced in the wake of the growth of the sUAS industry.