pwc-archive/papers-with-abstracts huggingface dataset:
An empirical study on V2X radio coverage using leaky coaxial cables in road crash barriers
Authors: Hagen Ußler, Christian Setzefand, Oliver Michler
Date: 2021-12-08 00:00:00

For current and future automated driving functions, the radio availability of broadband hybrid networking services (e.g. digital broadcasting, mobile radio, dedicated short range communication) is a prerequisite for continuous V2X information exchange. The supply focus for this is explicitly the road route with its lanes. The application of antenna-based solutions for such longitudinal radio cells with hybrid telematics services is expensive from the installation point of view and can only be adapted to new future telematics standards with great effort. A more suitable solution for such longitudinally shaped radio cells for road routes are leaky coaxial cables (LCX), which are already successfully used for tunnel solutions, for example. The paper discusses the installation and radio implementation of broadband LCX solutions (up to 6 GHz) in terms of simulation and surveying. The integration of the LCX into the crash barrier is favored due to low installation effort and easy upgradeability. An installation was realized on an automotive test fields, where preliminary empirical results for radio simulation and coverage were obtained. Based on the simulations and evaluation measurements, it can be shown that the propagated coverage approach is sustainable over all radiated services. Further solution approaches such as the direct insertion of LCX into the roadway and the derivation of vehicle location information are discussed in the outlook of the paper.
$\boldsymbolγ$-Net: Superresolving SAR Tomographic Inversion via Deep Learning
Authors: Kun Qian, Yuanyuan Wang, Yilei Shi, Xiao Xiang Zhu
Date: 2021-12-08 00:00:00
Tasks: Compressive Sensing, Computational Efficiency, Deep Learning, Super-Resolution

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, $\boldsymbol{\gamma}$-Net, to tackle this challenge. $\boldsymbol{\gamma}$-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained $\boldsymbol{\gamma}$-Net approaches the Cram\'er-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, $\boldsymbol{\gamma}$-Net reaches more than $90\%$ detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers.
Testing for Causal Influence using a Partial Coherence Statistic
Authors: Louis L. Scharf, YuAn Wang
Date: 2021-12-07 00:00:00

In this paper we explore partial coherence as a tool for evaluating causal influence of one signal sequence on another. In some cases the signal sequence is sampled from a time- or space-series. The key idea is to establish a connection between questions of causality and questions of partial coherence. Once this connection is established, then a scale-invariant partial coherence statistic is used to resolve the question of causality. This coherence statistic is shown to be a likelihood ratio, and its null distribution is shown to be a Wilks Lambda. It may be computed from a composite covariance matrix or from its inverse, the information matrix. Numerical experiments demonstrate the application of partial coherence to the resolution of causality. Importantly, the method is model-free, depending on no generative model for causality.
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors
Authors: Liran Szlak, Kristoffer Aberg, Rony Paz
Date: 2021-12-08 00:00:00
Tasks: Reinforcement Learning (RL)

During probabilistic learning organisms often apply a sub-optimal "probability-matching" strategy, where selection rates match reward probabilities, rather than engaging in the optimal "maximization" strategy, where the option with the highest reward probability is always selected. Despite decades of research, the mechanisms contributing to probability-matching are still under debate, and particularly noteworthy is that no differences between probability-matching and maximization strategies have been reported at the level of the brain. Here, we provide theoretical proof for a computational model that explains the complete range of behaviors between pure maximization and pure probability-matching. Fitting this model to behavior of 60 participants performing a probabilistic reinforcement learning task during fMRI scanning confirmed the model-derived prediction that probability-matching relates to an increased integration of negative outcomes during learning, as indicated by a stronger coupling between midbrain BOLD signal and negative prediction errors. Because the degree of probability-matching was consistent within an individual across nine different conditions, our results further suggest that the tendency to express a particular learning strategy is a trait-like feature of an individual.
Attractor and integrator networks in the brain
Authors: Mikail Khona, Ila R. Fiete
Date: 2021-12-07 00:00:00

In this review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, error-corrects, and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long time-scales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we look ahead by highlighting recent theoretical advances in understanding how the fundamental tradeoffs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible.
A hypothesis-free bridging of disease dynamics and non-pharmaceutical policies
Authors: Xiunan Wang, Hao Wang, Pouria Ramazi, Kyeongah Nah, Mark Lewis
Date: 2021-12-08 00:00:00

Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction power. Typically, a hypothesis is made on the form of the transmission rate with respect to time. Yet the real form is too complex to be mechanistically modeled due to the unknown dynamics of many influential factors. We tackle this problem by using a hypothesis-free machine-learning algorithm to estimate the transmission rate from data on non-pharmaceutical policies, and in turn forecast the confirmed cases using a mechanistic disease model. More specifically, we build a hybrid model consisting of a mechanistic ordinary differential equation (ODE) model and a generalized boosting model (GBM). To calibrate the parameters, we develop an "inverse method" that obtains the transmission rate inversely in time from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. The resulting model forecasted the number of daily confirmed cases up to 35 days in the future in the United States with an averaged mean absolute percentage error of 27%. Being partly data-driven, the method is more accurate than typical mechanistic models and meanwhile more intuitive, and possibly reliable, than purely data-based machine learning models. Moreover, it can identify the most informative predictive variables, which can be helpful in designing improved forecasters as well as informing policymakers.
Viral replication dynamics could critically modulate vaccine effectiveness and should be accounted for when assessing new SARS-CoV-2 variants
Authors: Wan Yang, Jeffrey Shaman
Date: 2021-12-07 00:00:00

In this article, we propose a theory to explain the reduction in vaccine effectiveness (VE) against the Delta SARS-CoV-2 variant and decreasing VE over time reported in recent studies. Using a model illustration, we show that in-host viral replication dynamics and delays in immune response could play a key role in VE. Given this, current laboratory approaches solely measuring reductions in neutralizing ability cannot fully represent the potential impact of new SARS-CoV-2 variants. We instead propose an alternative approach that incorporates viral replication dynamics into evaluations of SARS-CoV-2 variant impact on immunity and VE. This more robust assessment may better inform public health response to new variants like the newly detected Omicron variant.
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations
Authors: Carlos P. Roca1, Oliver T. Burton, Julika Neumann, Samar Tareen, Carly E. Whyte, Stéphanie Humblet-Baron, Adrian Liston
Date: 2021-12-08 00:00:00
Tasks: Dimensionality Reduction, valid

The advent of high dimensional single cell data in the biomedical sciences has necessitated the development of dimensionality-reduction tools. t-SNE and UMAP are the two most frequently used approaches, allowing clear visualisation of highly complex single cell datasets. Despite the ubiquity of these approaches and the clear need for quantitative comparison of single cell datasets, t-SNE and UMAP have largely remained data visualisation tools due to the lack of robust statistical approaches available. Here, we have derived a statistical test for evaluating the difference between dimensionality-reduced datasets, using the Kolmogorov-Smirnov test on the distributions of cross entropy of single cells within each dataset. As the approach uses the interrelationship of single cells for comparison, the resulting statistic is robust and capable of distinguishing between true biological variation and rotational symmetry generation during dimensionality reduction. Further, the test provides a valid distance between single cell datasets, allowing the organisation of multiple samples into a dendrogram for quantitative comparison of complex datasets. These results demonstrate the largely untapped potential of dimensionality-reduction tools for biomedical data analysis beyond visualisation.
Do fundamentals shape the price response? A critical assessment of linear impact models
Authors: Michele Vodret, Iacopo Mastromatteo, Bence Tóth, Michael Benzaquen
Date: 2021-12-08 00:00:00

We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high frequency, due to the emergence of universality at small time scales. On the other hand, we find those models to disagree on the overall strength of the impact function by a quantity that we are able to relate to the amount of excess-volatility in the market. We reveal a crossover between a high-frequency regime in which the market reacts sub-linearly to the signed order flow, to a low-frequency regime in which prices respond linearly to order flow imbalances. Overall, we reconcile results from the literature on market microstructure (sub-linearity in the price response to traded volumes) with those relating to macroeconomically relevant timescales (in which a linear relation is typically assumed).
Classic Meets Modern: a Pragmatic Learning-Based Congestion Control for the Internet
Authors: Soheil Abbasloo, Chen-Yu Yen, H. Jonathan Chao
Date: 2020-07-30 00:00:00
Proceeding: SIGCOMM 2020 7
Tasks: Deep Reinforcement Learning

These days, taking the revolutionary approach of using clean-slate learning-based designs to completely replace the classic congestion control schemes for the Internet is gaining popularity. However, we argue that current clean-slate learning-based techniques bring practical issues and concerns such as overhead, convergence issues, and low performance over unseen network conditions to the table. To address these issues, we take a pragmatic and evolutionary approach combining classic congestion control strategies and advanced modern deep reinforcement learning (DRL) techniques and introduce a novel hybrid congestion control for the Internet named Orca1. Through extensive experiments done over global testbeds on the Internet and various locally emulated network conditions, we demonstrate that Orca is adaptive and achieves consistent high performance in different network conditions, while it can significantly alleviate the issues and problems of its clean-slate learning-based counterparts.
Adaptive Methods for Aggregated Domain Generalization
Authors: Xavier Thomas, Dhruv Mahajan, Alex Pentland, Abhimanyu Dubey
Date: 2021-12-09 00:00:00
Tasks: Domain Generalization

Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains, with applications in large-scale learning and personalized inference. In many settings, privacy concerns prohibit obtaining domain labels for the training data samples, and instead only have an aggregated collection of training points. Existing approaches that utilize domain labels to create domain-invariant feature representations are inapplicable in this setting, requiring alternative approaches to learn generalizable classifiers. In this paper, we propose a domain-adaptive approach to this problem, which operates in two steps: (a) we cluster training data within a carefully chosen feature space to create pseudo-domains, and (b) using these pseudo-domains we learn a domain-adaptive classifier that makes predictions using information about both the input and the pseudo-domain it belongs to. Our approach achieves state-of-the-art performance on a variety of domain generalization benchmarks without using domain labels whatsoever. Furthermore, we provide novel theoretical guarantees on domain generalization using cluster information. Our approach is amenable to ensemble-based methods and provides substantial gains even on large-scale benchmark datasets. The code can be found at: https://github.com/xavierohan/AdaClust_DomainBed
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Authors: Weijia Wu, Yuanqiang Cai, Debing Zhang, Sibo Wang, Zhuang Li, Jiahong Li, Yejun Tang, Hong Zhou
Date: 2021-12-09 00:00:00
Tasks: text annotation, Text Spotting

Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data. In this work, we introduce a large-scale, Bilingual, Open World Video text benchmark dataset(BOVText). There are four features for BOVText. Firstly, we provide 2,000+ videos with more than 1,750,000+ frames, 25 times larger than the existing largest dataset with incidental text in videos. Secondly, our dataset covers 30+ open categories with a wide selection of various scenarios, e.g., Life Vlog, Driving, Movie, etc. Thirdly, abundant text types annotation (i.e., title, caption or scene text) are provided for the different representational meanings in video. Fourthly, the BOVText provides bilingual text annotation to promote multiple cultures live and communication. Besides, we propose an end-to-end video text spotting framework with Transformer, termed TransVTSpotter, which solves the multi-orient text spotting in video with a simple, but efficient attention-based query-key mechanism. It applies object features from the previous frame as a tracking query for the current frame and introduces a rotation angle prediction to fit the multiorient text instance. On ICDAR2015(video), TransVTSpotter achieves the state-of-the-art performance with 44.1% MOTA, 9 fps. The dataset and code of TransVTSpotter can be found at github:com=weijiawu=BOVText and github:com=weijiawu=TransVTSpotter, respectively.
Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm
Authors: Fengyun Zhang, Li Yang, Yuhuan Liu, Yulong Ding, Shuang-Hua Yang, Hao Li
Date: 2021-12-09 00:00:00
Tasks: Indoor Localization

Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We first introduce the architecture of the real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and analyze the relationship between anchor deployment and positioning accuracy. The Extended Kalman Filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy in moving targets.
HBReID: Harder Batch for Re-identification
Authors: Wen Li, Furong Xu, Jianan Zhao, Ruobing Zheng, Cheng Zou, Meng Wang, Yuan Cheng
Date: 2021-12-09 00:00:00
Tasks: Person Re-Identification, Triplet

Triplet loss is a widely adopted loss function in ReID task which pulls the hardest positive pairs close and pushes the hardest negative pairs far away. However, the selected samples are not the hardest globally, but the hardest only in a mini-batch, which will affect the performance. In this report, a hard batch mining method is proposed to mine the hardest samples globally to make triplet harder. More specifically, the most similar classes are selected into a same mini-batch so that the similar classes could be pushed further away. Besides, an adversarial scene removal module composed of a scene classifier and an adversarial loss is used to learn scene invariant feature representations. Experiments are conducted on dataset MSMT17 to prove the effectiveness, and our method surpasses all of the previous methods and sets state-of-the-art result.
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
Authors: Yining Hong, Li Yi, Joshua B. Tenenbaum, Antonio Torralba, Chuang Gan
Date: 2021-12-09 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Diagnostic, Instance Segmentation, Object, Semantic Segmentation, Visual Reasoning

A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies. Such composite structures could induce a rich set of semantic concepts and relations, thus playing an important role in the interpretation and organization of visual signals as well as for the generalization of visual perception and reasoning. However, existing visual reasoning benchmarks mostly focus on objects rather than parts. Visual reasoning based on the full part-whole hierarchy is much more challenging than object-centric reasoning due to finer-grained concepts, richer geometry relations, and more complex physics. Therefore, to better serve for part-based conceptual, relational and physical reasoning, we introduce a new large-scale diagnostic visual reasoning dataset named PTR. PTR contains around 70k RGBD synthetic images with ground truth object and part level annotations regarding semantic instance segmentation, color attributes, spatial and geometric relationships, and certain physical properties such as stability. These images are paired with 700k machine-generated questions covering various types of reasoning types, making them a good testbed for visual reasoning models. We examine several state-of-the-art visual reasoning models on this dataset and observe that they still make many surprising mistakes in situations where humans can easily infer the correct answer. We believe this dataset will open up new opportunities for part-based reasoning.
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Authors: Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann
Date: 2021-12-09 00:00:00
Tasks: Object

We present Neural Descriptor Fields (NDFs), an object representation that encodes both points and relative poses between an object and a target (such as a robot gripper or a rack used for hanging) via category-level descriptors. We employ this representation for object manipulation, where given a task demonstration, we want to repeat the same task on a new object instance from the same category. We propose to achieve this objective by searching (via optimization) for the pose whose descriptor matches that observed in the demonstration. NDFs are conveniently trained in a self-supervised fashion via a 3D auto-encoding task that does not rely on expert-labeled keypoints. Further, NDFs are SE(3)-equivariant, guaranteeing performance that generalizes across all possible 3D object translations and rotations. We demonstrate learning of manipulation tasks from few (5-10) demonstrations both in simulation and on a real robot. Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors. Project website: https://yilundu.github.io/ndf/.