OpenRaiser/Intern-Atlas huggingface dataset:
Designing Menus of Contracts Efficiently: The Power of Randomization
Venue: arXiv cs.GT 2022
Year: 2022

We study hidden-action principal-agent problems in which a principal commits to an outcome-dependent payment scheme (called contract) so as to incentivize the agent to take a costly, unobservable action leading to favorable outcomes. In particular, we focus on Bayesian settings where the agent has private information. This is collectively encoded by the agent's type, which is unknown to the principal, but randomly drawn according to a finitely-supported, commonly-known probability distribution. In Bayesian principal-agent problems, the principal may be better off by committing to a menu of contracts specifying a contract for each agent's type, rather than committing to a single contract. This induces a two-stage process that resembles interactions studied in classical mechanism design: after the principal has committed to a menu, the agent first reports a type to the principal, and, then, the latter puts in place the contract in the menu that corresponds to the reported type. Thus, the principal's computational problem boils down to designing a menu of contracts that incentivizes the agent to report their true type and maximizes expected utility. Previous works showed that computing an optimal menu of contracts is APX-hard. Crucially, previous works focus on menus of deterministic contracts. Surprisingly, we show that, if one considers menus of randomized contracts defined as probability distributions over payment vectors, then an "almost-optimal" menu can be computed in polynomial time. Indeed, the problem of computing a principal-optimal menu of randomized contracts may not admit a maximum, but only a supremum. Nevertheless, we show how to design a polynomial-time algorithm that guarantees the principal with an expected utility arbitrarily close to the supremum. Besides this main result, we also close several gaps in the analysis of menus of deterministic contracts.
Classification of Planar Monomials Over Finite Fields of Small Order
Venue: arXiv cs.IT 2022
Year: 2022

For all finite fields of order up to $2^{30}$, we computationally prove that there are no planar monomials besides the ones already known.
Banding vs. Quality: Perceptual Impact and Objective Assessment
Venue: arXiv cs.MM 2022
Year: 2022

Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a long-standing problem in both video processing and quality assessment communities. The fact that even a relatively small change of the original pixel values can result in a strong impact on perceived quality makes banding especially difficult to be detected by objective quality metrics. In this paper, we study how banding annoyance compares to more commonly studied scaling and compression artifacts with respect to the overall perceptual quality. We further propose a simple combination of VMAF and the recently developed banding index, CAMBI, into a banding-aware video quality metric showing improved correlation with overall perceived quality.
FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access Channels
Venue: arXiv cs.IT 2022
Year: 2022

This article considers the massive MIMO unsourced random access problem on a quasi-static Rayleigh fading channel. Given a fixed message length and a prescribed number of channel uses, the objective is to construct a coding scheme that minimizes the energy-per-bit subject to a fixed probability of error. The proposed scheme differs from other state-of-the-art schemes in that it blends activity detection, single-user coding, pilot-aided and temporary decisions-aided iterative channel estimation and decoding, minimum-mean squared error (MMSE) estimation, and successive interference cancellation (SIC). We show that an appropriate combination of these ideas can substantially outperform state-of-the-art coding schemes when the number of active users is more than 100, making this the best performing scheme known for this regime.
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Venue: arXiv cs.LG 2022
Year: 2022

Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). Identifying optimal orchestration considering the cross-layer opportunities and requirements in the face of varying system dynamics is a challenging multi-dimensional problem. While Reinforcement Learning (RL) approaches have been proposed earlier, they suffer from a large number of trial-and-errors during the learning process resulting in excessive time and resource consumption. We present a Hybrid Learning orchestration framework that reduces the number of interactions with the system environment by combining model-based and model-free reinforcement learning. Our Deep Learning inference orchestration strategy employs reinforcement learning to find the optimal orchestration policy. Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings. We demonstrate efficacy of our HL strategy through experimental comparison with state-of-the-art RL-based inference orchestration, demonstrating that our HL strategy accelerates the learning process by up to 166.6x.
ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users
Venue: arXiv cs.HC 2022
Year: 2022

Recent advances have enabled automatic sound recognition systems for deaf and hard of hearing (DHH) users on mobile devices. However, these tools use pre-trained, generic sound recognition models, which do not meet the diverse needs of DHH users. We introduce ProtoSound, an interactive system for customizing sound recognition models by recording a few examples, thereby enabling personalized and fine-grained categories. ProtoSound is motivated by prior work examining sound awareness needs of DHH people and by a survey we conducted with 472 DHH participants. To evaluate ProtoSound, we characterized performance on two real-world sound datasets, showing significant improvement over state-of-the-art (e.g., +9.7% accuracy on the first dataset). We then deployed ProtoSound's end-user training and real-time recognition through a mobile application and recruited 19 hearing participants who listened to the real-world sounds and rated the accuracy across 56 locations (e.g., homes, restaurants, parks). Results show that ProtoSound personalized the model on-device in real-time and accurately learned sounds across diverse acoustic contexts. We close by discussing open challenges in personalizable sound recognition, including the need for better recording interfaces and algorithmic improvements.
Early Stage Diabetes Prediction via Extreme Learning Machine
Venue: arXiv cs.LG 2022
Year: 2022

Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe diseases that can lead to patient death. Developing and rural areas suffer the most due to the limited medical providers and financial situations. This paper proposed a novel approach based on an extreme learning machine for diabetes prediction based on a data questionnaire that can early alert the users to seek medical assistance and prevent late diagnoses and severe illness development.
Differentiable and Learnable Robot Models
Venue: arXiv cs.RO 2022
Year: 2022

Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulations with modern differentiable machine learning libraries. Here, we present a library that focuses on the ability to combine data driven methods with analytical rigid body computations. More concretely, our library \emph{Differentiable Robot Models} implements both \emph{differentiable} and \emph{learnable} models of the kinematics and dynamics of robots in Pytorch. The source-code is available at \url{https://github.com/facebookresearch/differentiable-robot-model}
Arbitrary Shape Text Detection using Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose an end-to-end trainable architecture based on Detection using Transformers (DETR), that outperforms previous state-of-the-art methods in arbitrary-shaped text detection. At its core, our proposed method leverages a bounding box loss function that accurately measures the arbitrary detected text regions' changes in scale and aspect ratio. This is possible due to a hybrid shape representation made from Bezier curves, that are further split into piece-wise polygons. The proposed loss function is then a combination of a generalized-split-intersection-over-union loss defined over the piece-wise polygons and regularized by a Smooth-$\ln$ regression over the Bezier curve's control points. We evaluate our proposed model using Total-Text and CTW-1500 datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for multi-oriented text, and show that the proposed method outperforms the previous state-of-the-art methods in arbitrary-shape text detection tasks.
Circuit and System Technologies for Energy-Efficient Edge Robotics
Venue: arXiv cs.AR 2022
Year: 2022

As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixed-signal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.
Lattices from Linear Codes: Source and Channel Networks
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we consider the information-theoretic characterization of the set of achievable rates and distortions in a broad class of multiterminal communication scenarios with general continuous-valued sources and channels. A framework is presented which involves fine discretization of the source and channel variables followed by communication over the resulting discretized network. In order to evaluate fundamental performance limits, convergence results for information measures are provided under the proposed discretization process. Using this framework, we consider point-to-point source coding and channel coding with side-information, distributed source coding with distortion constraints, the function reconstruction problems (two-help-one), computation over multiple access channel, the interference channel, and the multiple-descriptions source coding problem. We construct lattice-like codes for general sources and channels, and derive inner-bounds to set of achievable rates and distortions in these communication scenarios.
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy
Venue: arXiv cs.RO 2022
Year: 2022

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this paper, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the last 30 years as well as the current directions VAM-HRI is headed in the coming decade.
Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications
Venue: arXiv cs.IT 2022
Year: 2022

In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machine-type communications. By including the channel and the a priori activity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two message-scheduling strategies based on the residual belief propagation and the activity user detection in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.
Better Modelling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup
Venue: arXiv cs.LG 2022
Year: 2022

Generalizing the application of machine learning models to situations where the statistical distribution of training and test data are different has been a complex problem. Our contributions in this paper are threefold: (1) we introduce an anchored-based Out of Distribution (OOD) Regression Mixup algorithm, leveraging manifold hidden state mixup and observation similarities to form a novel regularization penalty, (2) we provide a first of its kind, high resolution Distributed Acoustic Sensor (DAS) dataset that is suitable for testing OOD regression modelling, allowing other researchers to benchmark progress in this area, and (3) we demonstrate with an extensive evaluation the generalization performance of the proposed method against existing approaches, then show that our method achieves state-of-the-art performance. Lastly, we also demonstrate a wider applicability of the proposed method by exhibiting improved generalization performances on other types of regression datasets, including Udacity and Rotation-MNIST datasets.
Classification of Computer Aided Engineering (CAE) Parts Using Graph Convolutional Networks
Venue: arXiv cs.LG 2022
Year: 2022

CAE engineers work with hundreds of parts spread across multiple body models. A Graph Convolutional Network (GCN) was used to develop a CAE parts classifier. As many as 866 distinct parts from a representative body model were used as training data. The parts were represented as a three-dimensional (3-D) Finite Element Analysis (FEA) mesh with values of each node in the x, y, z coordinate system. The GCN based classifier was compared to fully connected neural network and PointNet based models. Performance of the trained models was evaluated with a test set that included parts from the training data, but with additional holes, rotation, translation, mesh refinement/coarsening, variation of mesh schema, mirroring along x and y axes, variation of topographical features, and change in mesh node ordering. The trained GCN model was able to achieve 88.5% classification accuracy on the test set i.e., it was able to find the correct matching part from the dataset of 866 parts despite significant variation from the baseline part. A CAE parts classifier demonstrated in this study could be very useful for engineers to filter through CAE parts spread across several body models to find parts that meet their requirements.
Minimum time trajectory generation for surveying using UAVs
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we present a motion planning strategy for UAVs that generates a time-optimal trajectory to survey a given target area. There are several situations where completing an aerial survey is time sensitive, such as gaining situational awareness for first responders, surveying hazardous environments, etc. One of the challenges in such cases is to plan a time-optimal trajectory for the drone. To this end, we present an autonomous aerial survey framework that minimizes the time taken to completely explore a target area or volume using drones. In this work, (i) we present an approach, where for a known flight survey pattern, the planner can generate time-optimal flight paths in 3-D (ii) we frame the planning problem as a discrete non-linear program, and reduce the time taken to compute its solution by using an SOCP relaxation (iii) The given path is then executed using a simple trajectory tracking controller on a quadrotor to demonstrate its capability on hardware.
Multivariate Quantile Function Forecaster
Venue: arXiv cs.LG 2022
Year: 2022

We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive, implicitly capturing the dependency structure across time but exhibiting error accumulation with increasing forecast horizons, or multi-horizon sequence-to-sequence models, which do not exhibit error accumulation, but also do typically not model the dependency structure across time steps. MQF$^2$ combines the benefits of both approaches, by directly making predictions in the form of a multivariate quantile function, defined as the gradient of a convex function which we parametrize using input-convex neural networks. By design, the quantile function is monotone with respect to the input quantile levels and hence avoids quantile crossing. We provide two options to train MQF$^2$: with energy score or with maximum likelihood. Experimental results on real-world and synthetic datasets show that our model has comparable performance with state-of-the-art methods in terms of single time step metrics while capturing the time dependency structure.
Distributed and Mobile Message Level Relaying/Replaying of GNSS Signals
Venue: arXiv cs.CR 2022
Year: 2022

With the introduction of Navigation Message Authentication (NMA), future Global Navigation Satellite Systems (GNSSs) prevent spoofing by simulation, i.e., the generation of forged satellite signals based on public information. However, authentication does not prevent record-and-replay attacks, commonly termed as meaconing. These attacks are less powerful in terms of adversarial control over the victim receiver location and time, but by acting at the signal level, they are not thwarted by NMA. This makes replaying/relaying attacks a significant threat for GNSS. While there are numerous investigations on meaconing, the majority does not rely on actual implementation and experimental evaluation in real-world settings. In this work, we contribute to the improvement of the experimental understanding of meaconing attacks. We design and implement a system capable of real-time, distributed, and mobile meaconing, built with off-the-shelf hardware. We extend from basic distributed attacks, with signals from different locations relayed over the Internet and replayed within range of the victim receiver(s): this has high bandwidth requirements and thus depends on the quality of service of the available network to work. To overcome this limitation, we propose to replay on message level, including the authentication part of the payload. The resultant reduced bandwidth enables the attacker to operate in mobile scenarios, as well as to replay signals from multiple GNSS constellations and/or bands simultaneously. Additionally, the attacker can delay individually selected satellite signals to potentially influence the victim position and time solution in a more fine-grained manner. Our versatile test-bench, enabling different types of replaying/relaying attacks, facilitates testing realistic scenarios towards new and improved replaying/relaying-focused countermeasures in GNSS receivers.
The DoF Region of Two-User MIMO Broadcast Channel with Delayed Imperfect-Quality CSIT
Venue: arXiv cs.IT 2022
Year: 2022

The channel state information at the transmitter (CSIT) play an important role in the performance of wireless networks. The CSIT model can be delayed and imperfect-quality, since the feedback link has a delay and the channel state information (CSI) feedback has distortion. In this paper, we thus characterize the degrees-of-freedom (DoF) region of the two-user multiple-input multiple-output (MIMO) broadcast channel with delayed imperfect-quality CSIT, where the antenna configurations can be arbitrary. The converse proof of DoF region is based on the enhancement of physically degraded channel. The achievability proof of DoF region is through a novel transmission scheme design, where the duration of each phase and the amount of transmitted symbols are configured based on the imperfect-quality of delayed CSIT. As a result, we show that the DoF region with delayed imperfect-quality CSIT is located between the DoF region with no CSIT and the DoF region with delayed CSIT.
Open5x: Accessible 5-axis 3D printing and conformal slicing
Venue: arXiv cs.HC 2022
Year: 2022

The common layer-by-layer deposition of regular, 3-axis 3D printing simplifies both the fabrication process and the 3D printer's mechanical design. However, the resulting 3D printed objects have some unfavourable characteristics including visible layers, uneven structural strength and support material. To overcome these, researchers have employed robotic arms and multi-axis CNCs to deposit materials in conformal layers. Conformal deposition improves the quality of the 3D printed parts through support-less printing and curved layer deposition. However, such multi-axis 3D printing is inaccessible to many individuals due to high costs and technical complexities. Furthermore, the limited GUI support for conformal slicers creates an additional barrier for users. To open multi-axis 3D printing up to more makers and researchers, we present a cheap and accessible way to upgrade a regular 3D printer to 5 axes. We have also developed a GUI-based conformal slicer, integrated within a popular CAD package. Together, these deliver an accessible workflow for designing, simulating and creating conformally-printed 3D models.