pwc-archive/papers-with-abstracts huggingface dataset:
Online False Discovery Rate Control for LORD & SAFFRON Under Positive, Local Dependence
Authors: Aaron Fisher
Date: 2021-10-15 00:00:00

Online testing procedures assume that hypotheses are observed in sequence, and allow the significance thresholds for upcoming tests to depend on the test statistics observed so far. Some of the most popular online methods include alpha investing, LORD++ (hereafter, LORD), and SAFFRON. These three methods have been shown to provide online control of the "modified" false discovery rate (mFDR) under a condition known as conditional superuniformity. However, to our knowledge, LORD & SAFFRON have only been shown to control the traditional false discovery rate (FDR) under an independence condition on the test statistics. Our work bolsters these results by showing that SAFFRON and LORD additionally ensure online control of the FDR under a "local" form of nonnegative dependence. Further, FDR control is maintained under certain types of adaptive stopping rules, such as stopping after a certain number of rejections have been observed. Because alpha investing can be recovered as a special case of the SAFFRON framework, our results immediately apply to alpha investing as well. In the process of deriving these results, we also formally characterize how the conditional superuniformity assumption implicitly limits the allowed p-value dependencies. This implicit limitation is important not only to our proposed FDR result, but also to many existing mFDR results.
Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions
Authors: Quoc Tran-Dinh, Yang Luo
Date: 2021-10-15 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we develop a new type of accelerated algorithms to solve some classes of maximally monotone equations as well as monotone inclusions. Instead of using Nesterov's accelerating approach, our methods rely on a so-called Halpern-type fixed-point iteration in [32], and recently exploited by a number of researchers, including [24, 70]. Firstly, we derive a new variant of the anchored extra-gradient scheme in [70] based on Popov's past extra-gradient method to solve a maximally monotone equation $G(x) = 0$. We show that our method achieves the same $\mathcal{O}(1/k)$ convergence rate (up to a constant factor) as in the anchored extra-gradient algorithm on the operator norm $\Vert G(x_k)\Vert$, , but requires only one evaluation of $G$ at each iteration, where $k$ is the iteration counter. Next, we develop two splitting algorithms to approximate a zero point of the sum of two maximally monotone operators. The first algorithm originates from the anchored extra-gradient method combining with a splitting technique, while the second one is its Popov's variant which can reduce the per-iteration complexity. Both algorithms appear to be new and can be viewed as accelerated variants of the Douglas-Rachford (DR) splitting method. They both achieve $\mathcal{O}(1/k)$ rates on the norm $\Vert G_{\gamma}(x_k)\Vert$ of the forward-backward residual operator $G_{\gamma}(\cdot)$ associated with the problem. We also propose a new accelerated Douglas-Rachford splitting scheme for solving this problem which achieves $\mathcal{O}(1/k)$ convergence rate on $\Vert G_{\gamma}(x_k)\Vert$ under only maximally monotone assumptions. Finally, we specify our first algorithm to solve convex-concave minimax problems and apply our accelerated DR scheme to derive a new variant of the alternating direction method of multipliers (ADMM).
Convergence of Laplacian Eigenmaps and its Rate for Submanifolds with Singularities
Authors: Masayuki Aino
Date: 2021-10-15 00:00:00

In this paper, we give a spectral approximation result for the Laplacian on submanifolds of Euclidean spaces with singularities by the $\epsilon$-neighborhood graph constructed from random points on the submanifold. Our convergence rate for the eigenvalue of the Laplacian is $O\left(\left(\log n/n\right)^{1/(m+2)}\right)$, where $m$ and $n$ denote the dimension of the manifold and the sample size, respectively.
Optimal Distribution Design for Irregular Repetition Slotted ALOHA with Multi-Packet Reception
Authors: Zhengchuan Chen, Yifan Feng, Chundie Feng, Liang Liang, Yunjian Jia, Tony Q. S. Quek
Date: 2021-10-15 00:00:00

Associated with multi-packet reception at the access point, irregular repetition slotted ALOHA (IRSA) holds a great potential in improving the access capacity of massive machine type communication systems. Considering the time-frequency resource efficiency, K = 2 (multi-packet reception capability) may be the most suitable scheme for scenarios that allow smaller resource efficiency in exchange for greater throughput. In this paper, we analytically derive an optimal transmission probability distribution for IRSA with K = 2, which achieves a significant higher load threshold than the existing benchmark distributions. In addition, the energy efficiency optimization in terms of the maximum repetition rate is also presented.
Multifocal Stereoscopic Projection Mapping
Authors: Sorashi Kimura, Daisuke Iwai, Parinya Punpongsanon, Kosuke Sato
Date: 2021-10-08 00:00:00
Tasks: Object

Stereoscopic projection mapping (PM) allows a user to see a three-dimensional (3D) computer-generated (CG) object floating over physical surfaces of arbitrary shapes around us using projected imagery. However, the current stereoscopic PM technology only satisfies binocular cues and is not capable of providing correct focus cues, which causes a vergence--accommodation conflict (VAC). Therefore, we propose a multifocal approach to mitigate VAC in stereoscopic PM. Our primary technical contribution is to attach electrically focus-tunable lenses (ETLs) to active shutter glasses to control both vergence and accommodation. Specifically, we apply fast and periodical focal sweeps to the ETLs, which causes the "virtual image'" (as an optical term) of a scene observed through the ETLs to move back and forth during each sweep period. A 3D CG object is projected from a synchronized high-speed projector only when the virtual image of the projected imagery is located at a desired distance. This provides an observer with the correct focus cues required. In this study, we solve three technical issues that are unique to stereoscopic PM: (1) The 3D CG object is displayed on non-planar and even moving surfaces; (2) the physical surfaces need to be shown without the focus modulation; (3) the shutter glasses additionally need to be synchronized with the ETLs and the projector. We also develop a novel compensation technique to deal with the "lens breathing" artifact that varies the retinal size of the virtual image through focal length modulation. Further, using a proof-of-concept prototype, we demonstrate that our technique can present the virtual image of a target 3D CG object at the correct depth. Finally, we validate the advantage provided by our technique by comparing it with conventional stereoscopic PM using a user study on a depth-matching task.
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Authors: Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, Dinesh Manocha
Date: 2021-10-08 00:00:00
Tasks: Active Learning

We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our collision detector is represented as a bilevel deep autoencoder with an attention mechanism that identifies colliding mesh sub-parts. We use a numerical optimization algorithm to resolve penetrations guided by the network. Our learned collision handler can resolve collisions for unseen, high-dimensional meshes with thousands of vertices. To obtain stable network performance in such large and unseen spaces, we progressively insert new collision data based on the errors in network inferences. We automatically label these data using an analytical collision detector and progressively fine-tune our detection networks. We evaluate our method for collision handling of complex, 3D meshes coming from several datasets with different shapes and topologies, including datasets corresponding to dressed and undressed human poses, cloth simulations, and human hand poses acquired using multiview capture systems. Our approach outperforms supervised learning methods and achieves $93.8-98.1\%$ accuracy compared to the groundtruth by analytic methods. Compared to prior learning methods, our approach results in a $5.16\%-25.50\%$ lower false negative rate in terms of collision checking and a $9.65\%-58.91\%$ higher success rate in collision handling.
Toward Annotator Group Bias in Crowdsourcing
Authors: Haochen Liu, Joseph Thekinen, Sinem Mollaoglu, Da Tang, Ji Yang, Youlong Cheng, Hui Liu, Jiliang Tang
Date: 2021-10-08 00:00:00
Proceeding: ACL 2022 5

Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations. Though there are a few works investigating individual annotator bias, the group effects in annotators are largely overlooked. In this work, we reveal that annotators within the same demographic group tend to show consistent group bias in annotation tasks and thus we conduct an initial study on annotator group bias. We first empirically verify the existence of annotator group bias in various real-world crowdsourcing datasets. Then, we develop a novel probabilistic graphical framework GroupAnno to capture annotator group bias with a new extended Expectation Maximization (EM) training algorithm. We conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the effectiveness of our model in modeling annotator group bias in label aggregation and model learning over competitive baselines.
Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning
Authors: Yi Hou, Peter Graf
Date: 2021-10-05 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Frequent lane changes during congestion at freeway bottlenecks such as merge and weaving areas further reduce roadway capacity. The emergence of deep reinforcement learning (RL) and connected and automated vehicle technology provides a possible solution to improve mobility and energy efficiency at freeway bottlenecks through cooperative lane changing. Deep RL is a collection of machine-learning methods that enables an agent to improve its performance by learning from the environment. In this study, a decentralized cooperative lane-changing controller was developed using proximal policy optimization by adopting a multi-agent deep RL paradigm. In the decentralized control strategy, policy learning and action reward are evaluated locally, with each agent (vehicle) getting access to global state information. Multi-agent deep RL requires lower computational resources and is more scalable than single-agent deep RL, making it a powerful tool for time-sensitive applications such as cooperative lane changing. The results of this study show that cooperative lane changing enabled by multi-agent deep RL yields superior performance to human drivers in term of traffic throughput, vehicle speed, number of stops per vehicle, vehicle fuel efficiency, and emissions. The trained RL policy is transferable and can be generalized to uncongested, moderately congested, and extremely congested traffic conditions.
Influencing Towards Stable Multi-Agent Interactions
Authors: Woodrow Z. Wang, Andy Shih, Annie Xie, Dorsa Sadigh
Date: 2021-10-05 00:00:00
Tasks: Autonomous Driving

Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent. We learn a low-dimensional latent representation of the other agent's strategy and the dynamics of how the latent strategy evolves with respect to our robot's behavior. With this learned dynamics model, we can define an unsupervised stability reward to train our robot to deliberately influence the other agent to stabilize towards a single strategy. We demonstrate the effectiveness of stabilizing in improving efficiency of maximizing the task reward in a variety of simulated environments, including autonomous driving, emergent communication, and robotic manipulation. We show qualitative results on our website: https://sites.google.com/view/stable-marl/.
Towards a Multi-Agent System Architecture for Supply Chain Management
Authors: Carlos R. Jaimez-González, Wulfrano A. Luna-Ramírez
Date: 2021-09-24 00:00:00
Tasks: Management

Individual business processes have been changing since the Internet was created, and they are now oriented towards a more distributed and collaborative business model, in an e-commerce environment that adapts itself to the competitive and changing market conditions. This paper presents a multi-agent system architecture for supply chain management, which explores different strategies and offers solutions in a distributed e-commerce environment. The system is designed to support different types of interfaces, which allow interoperating with other business models already developed. In order to show how the entire multi-agent system is being developed, the implementation of a collaborative agent is presented and explained.
Multi-layer Space Information Networks: Access Design and Softwarization
Authors: Hayder Al-Hraishawi, Mario Minardi, Houcine Chougrani, Oltjon Kodheli, Jesus Fabian Mendoza Montoya, Symeon Chatzinotas
Date: 2021-10-15 00:00:00
Tasks: Earth Observation

In this paper, we propose an approach for constructing a multi-layer multi-orbit space information network (SIN) to provide high-speed continuous broadband connectivity for space missions (nanosatellite terminals) from the emerging space-based Internet providers. This notion has been motivated by the rapid developments in satellite technologies in terms of satellite miniaturization and reusable rocket launch, as well as the increased number of nanosatellite constellations in lower orbits for space downstream applications, such as earth observation, remote sensing, and Internet of Things (IoT) data collection. Specifically, space-based Internet providers, such as Starlink, OneWeb, and SES O3b, can be utilized for broadband connectivity directly to/from the nanosatellites, which allows a larger degree of connectivity in space network topologies. Besides, this kind of establishment is more economically efficient and eliminates the need for an excessive number of ground stations while achieving real-time and reliable space communications. This objective necessitates developing suitable radio access schemes and efficient scalable space backhauling using inter-satellite links (ISLs) and inter-orbit links (IOLs). Particularly, service-oriented radio access methods in addition to software-defined networking (SDN)-based architecture employing optimal routing mechanisms over multiple ISLs and IOLs are the most essential enablers for this novel concept. Thus, developing this symbiotic interaction between versatile satellite nodes across different orbits will lead to a breakthrough in the way that future downstream space missions and satellite networks are designed and operated.
Formation control of a leader-follower structure in three dimensional space using bearing measurements
Authors: Zhiqi Tang, Rita Cunha, Tarek Hamel, Carlos Silvestre
Date: 2021-10-15 00:00:00

This paper addresses the problem of bearing leader-follower formation control in three-dimensional space by exploring the persistence of excitation (PE) of the desired formation. Using only bearing and relative velocity measurements, distributed control laws are derived for a group of agents with double-integrator dynamics. The key contribution is that the exponential stabilization of the actual formation to the desired one in terms of both shape and scale is guaranteed as long as the PE conditions on the desired formation are satisfied. The approach generalizes stability results provided in prior work for leader-first follower (LFF) structures which are based on bearing rigidity and constraint consistency to ensure the exponential stabilization of the actual formation to a desired static geometric pattern up to a scale factor. Simulations results are provided to illustrate the performance of the proposed control method.
Reachability-based Control Synthesis under Signal Temporal Logic Specifications
Authors: Wei Ren, Raphael Jungers
Date: 2021-10-15 00:00:00

In this paper, we investigate the controller design problem for linear disturbed systems under signal temporal logic (STL) specifications imposing both spatial and temporal constraints on system behavior. We first implement zonotope-based techniques to partition the state space into finite cells, then propose an evaluation mechanism to rearrange the time constraints of the STL specification, and finally decompose the global STL formula into finite local STL formulas. In this way, each cell has a local control design problem, which is further formulated into a local optimization problem. To deal with each local optimization problem, we take advantage of the properties of zonotopes and reachability analysis to design local controller consisting of feedforward and feedback parts. By solving all local optimization problems, all local controllers are combined to guarantee the global STL specification. Finally, a numerical example is presented to illustrate the derived results.
Multi-Stage Sparse Resource Allocation for Control of Spreading Processes over Networks
Authors: Vera L. J. Somers, Ian R. Manchester
Date: 2021-10-14 00:00:00

In this paper we propose a method for sparse dynamic allocation of resources to bound the risk of spreading processes, such as epidemics and wildfires, using convex optimization and dynamic programming techniques. Here, risk is defined as the risk of an outbreak, i.e. the product of the probability of an outbreak occurring over a time interval and the future impact of that outbreak, and we can allocate budgeted resources each time step to bound or minimize the risk. Our method in particular provides sparsity of resources, which is important due to the large network structures involved with spreading processes and has advantages when resources can not be distributed widely.
Locational Factors in the Competition between Credit Unions and Banks after the Great Recession
Authors: Reka Sundaram-Stukel, Steven C Deller
Date: 2021-10-14 00:00:00

In the aftermath of the Great Recession, the regulatory framework for credit union operations has become a subject of controversy. Competing financial enterprises such as thrifts and other banks have argued that credit unions have received preferential treatment under existing tax and regulatory codes, whereas credit unions complained of undue restrictions on their ability to scale up and increase their scope of operations. Building on previous location models, this analysis focuses on credit union headquarter locations immediately following the 2008 financial crisis. We offer a new perspective for understanding credit union behavior based on central place theory, new industrial organization literature, and credit union location analysis. Our findings indicate that credit unions avoid locating near other lending institutions, instead operating in areas with a low concentration of other banks. This finding provides evidence that credit unions serve niche markets, product differential, and are not a significant source of direct competition for thrifts and other banks.
Anti-jamming beam alignment in millimeter-wave MIMO systems
Authors: Donatella Darsena, Francesco Verde
Date: 2021-10-15 00:00:00

In millimeter-wave (MMW) multiple-input multiple-output (MIMO) communications, users and their corresponding base station (BS) have to align their beam during both initial access and data transmissions to compensate for the high propagation loss. The beam alignment (BA) procedure specified for 5th Generation (5G) New Radio (NR) has been designed to be fast and precise in the presence of non-malicious interference and noise. A smart jammer might exploit this weakness and may launch an attack during the BA phase in order to degrade the accuracy of beam selection and, thus, adversely impacting the end-to-end performance and quality-of-service experienced by the users. In this paper, we study the effects of a jamming attack at MMW frequencies during the BA procedure used to perform initial access for idle users and adaptation/recovery for connected users. We show that the BA procedure adopted in 5G NR is extremely vulnerable to a smart jamming attack and, consequently, we propose a countermeasure based on the idea of randomized probing, which consists of randomly corrupting the probing sequence transmitted by the BS in order to reject the jamming signal at the UE via a subspace-based technique based on orthogonal projections and jamming cancellation. Numerical results corroborate our theoretical findings and show the very satisfactory accuracy of the proposed anti-jamming approach.
Grant-Free Random Access in Massive MIMO for Static Low-Power IoT Nodes
Authors: Gilles Callebaut, Liesbet Van der Perre, François Rottenberg
Date: 2021-10-15 00:00:00

Massive MIMO is a promising technology to enable a massive number of Internet of Things nodes to transmit short and sporadic data bursts at low power. In conventional cellular networks, devices use a grant-based random access scheme to initiate communications. This scheme relies on a limited set of orthogonal preambles, which simplify signal processing operations at network access points. However, it is not well suited for Internet of Things (IoT) devices due to: (i) the large protocol overhead, and (ii) the high probability of collision. In contrast to the grant-based scheme, a grant-free approach uses user-specific preambles and has a small overhead, at the expense of more complexity at access points. In this work, a grant-free method is proposed, applicable for both co-located and cell-free deployments. The method has a closed form solution, which results in a significantly low complexity with respect to the state-of-the art. The algorithm exploits the static nature of IoT devices through the use of prior channel state information. With a power budget of 1 mW, 64 antennas are sufficient to support 1000 nodes with 200 simultaneous access requests with a probability of false alarm and miss detection below 10-6 and 10-4, respectively.
Variance reduction in stochastic methods for large-scale regularised least-squares problems
Authors: Yusuf Pilavci, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay
Date: 2021-10-15 00:00:00
Tasks: Point Processes

Large dimensional least-squares and regularised least-squares problems are expensive to solve. There exist many approximate techniques, some deterministic (like conjugate gradient), some stochastic (like stochastic gradient descent). Among the latter, a new class of techniques uses Determinantal Point Processes (DPPs) to produce unbiased estimators of the solution. In particular, they can be used to perform Tikhonov regularization on graphs using random spanning forests, a specific DPP. While the unbiasedness of these algorithms is attractive, their variance can be high. We show here that variance can be reduced by combining the stochastic estimator with a deterministic gradient-descent step, while keeping the property of unbiasedness. We apply this technique to Tikhonov regularization on graphs, where the reduction in variance is found to be substantial at very small extra cost.