pwc-archive/papers-with-abstracts huggingface dataset:
Enhanced Behavioral Cloning with Environmental Losses for Self-Driving Vehicles
Authors: Nelson Fernandez Pinto, Thomas Gilles
Date: 2022-02-04 00:00:00

Learned path planners have attracted research interest due to their ability to model human driving behavior and rapid inference. Recent works on behavioral cloning show that simple imitation of expert observations is not sufficient to handle complex driving scenarios. Besides, predictions that land outside drivable areas can lead to potentially dangerous situations. This paper proposes a set of loss functions, namely Social loss and Road loss, which account for modelling risky social interactions in path planning. These losses act as a repulsive scalar field that surrounds non-drivable areas. Predictions that land near these regions incur in a higher training cost, which is minimized using backpropagation. This methodology provides additional environment feedback to the traditional supervised learning set up. We validated this approach on a large-scale urban driving dataset. The results show the agent learns to imitate human driving while exhibiting better safety metrics. Furthermore, the proposed methodology has positive effects on inference without the need to artificially generate unsafe driving examples. The explanability study suggests that the benefits obtained are associated with a higher relevance of non-drivable areas in the agent's decisions compared to classical behavioral cloning.
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models
Authors: Vrunda N. Sukhadia, S. Umesh
Date: 2022-02-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Domain Adaptation, speech-recognition, Speech Recognition, Transfer Learning

In this paper, we investigate domain adaptation for low-resource Automatic Speech Recognition (ASR) of target-domain data, when a well-trained ASR model trained with a large dataset is available. We argue that in the encoder-decoder framework, the decoder of the well-trained ASR model is largely tuned towards the source-domain, hurting the performance of target-domain models in vanilla transfer-learning. On the other hand, the encoder layers of the well-trained ASR model mostly capture the acoustic characteristics. We, therefore, propose to use the embeddings tapped from these encoder layers as features for a downstream Conformer target-domain model and show that they provide significant improvements. We do ablation studies on which encoder layer is optimal to tap the embeddings, as well as the effect of freezing or updating the well-trained ASR model's encoder layers. We further show that applying Spectral Augmentation (SpecAug) on the proposed features (this is in addition to default SpecAug on input spectral features) provides a further improvement on the target-domain performance. For the LibriSpeech-100-clean data as target-domain and SPGI-5000 as a well-trained model, we get 30% relative improvement over baseline. Similarly, with WSJ data as target-domain and LibriSpeech-960 as a well-trained model, we get 50% relative improvement over baseline.
Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion
Authors: Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito, Noboru Harada
Date: 2022-02-18 00:00:00
Tasks: Event Detection, Sensor Fusion

We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microphones and their weak labels. In this task, distributed sensors are utilized complementarily to capture events that are difficult to capture with a single sensor, such as a series of actions of people moving in an intricate room, or communication between people located far apart in a room. For sensors to cooperate effectively in such a situation, the system should be able to exchange information among sensors and combines information that is useful for identifying events in a complementary manner. For such a mechanism, we propose a Transformer-based multi-sensor fusion (MultiTrans) which combines multi-sensor data on the basis of the relationships between features of different viewpoints and modalities. In the experiments using a dataset newly collected for this task, our proposed method using MultiTrans improved the event detection performance and outperformed comparatives.
Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments
Authors: Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito
Date: 2022-02-18 00:00:00
Tasks: Domain Adaptation, Sound Event Localization and Detection

Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degraded in an unknown environment due to environmental effects such as reverberation and noise not contained in the training data. Previous studies on related tasks have shown that domain adaptation methods are effective when data on the environment in which the system will be used is available even without labels. However adaptation to unknown environments remains a difficult task. In this study, we propose echo-aware feature refinement (EAR) for SELD, which suppresses environmental effects at the feature level by using additional spatial cues of the unknown environment obtained through measuring acoustic echoes. FOA-MEIR, an impulse response dataset containing over 100 environments, was recorded to validate the proposed method. Experiments on FOA-MEIR show that the EAR effectively improves SELD performance in unknown environments.
A reliability measure for smart surveillance systems
Authors: Anj Simmons
Date: 2022-02-18 00:00:00

We present a reliability measure for smart surveillance systems, taking into account the adversarial nature of intrusion. Our approach is based on percolation theory and is a generalisation of Hamedmoghadam et al.'s reliability measure. Specifically, our approach incorporates a customisable cost function to allow modelling a diverse range of situations, such as access restrictions, monitoring, and failures. We demonstrate our approach by applying it to a digital twin of a smart building. However, challenges remain in estimating and modelling the key parameters needed.
Reduced-Order Modeling of Thermal Dynamics in District Energy Networks using Spectral Clustering
Authors: Johan Simonsson, Khalid Tourkey Atta, Wolfgang Birk
Date: 2022-02-18 00:00:00
Tasks: Clustering

Simulation of thermal dynamics in city-scale district energy grids often becomes computationally prohibitive for long simulation runs. Current model order reduction methods offer limited interpretability with regards to the non-reduced system, and are not in general applicable for e.g., varying flow rates, multiple producers, or changing flow directions. This article presents a novel method based on graph theory that approximates the solution of an optimization problem that minimizes the local truncation error for heat transport in the grid. It is shown that the method can be used to reduce the thermal dynamic model of a city-scale energy grid, resulting in a coarser temporal and spatial resolution. The relative root mean square error was 2.3\% for the temperature in the evaluation scenario, comparing the reduced-order system with the non-reduced system at the instances of the coarser time-step.
History Data Driven Distributed Consensus in Networks
Authors: Venkatraman Renganathan, Angela Fontan, Karthik Ganapathy
Date: 2022-02-18 00:00:00

The association of weights in a distributed consensus protocol quantify the trust that an agent has on its neighbors in a network. An important problem in such networked systems is the uncertainty in the estimation of trust between neighboring agents, coupled with the losses arising from mistakenly associating wrong amounts of trust with different neighboring agents. We introduce a probabilistic approach which uses the historical data collected in the network, to determine the level of trust between each agent. Specifically, using the finite history of the shared data between neighbors, we obtain a configuration which represents the confidence estimate of every neighboring agent's trustworthiness. Finally, we propose a History-Data-Driven (HDD) distributed consensus protocol which translates the computed configuration data into weights to be used in the consensus update. The approach using the historical data in the context of a distributed consensus setting marks the novel contribution of our paper.
Tackling A Class of Hard Subset-Sum Problems: Integration of Lattice Attacks with Disaggregation Techniques
Authors: Bojun Lu, Duan Li, Rujun Jiang
Date: 2022-02-18 00:00:00

Subset-sum problems belong to the NP class and play an important role in both complexity theory and knapsack-based cryptosystems, which have been proved in the literature to become hardest when the so-called density approaches one. Lattice attacks, which are acknowledged in the literature as the most effective methods, fail occasionally even when the number of unknown variables is of medium size. In this paper we propose a modular disaggregation technique and a simplified lattice formulation based on which two lattice attack algorithms are further designed. We introduce the new concept "jump points" in our disaggregation technique, and derive inequality conditions to identify superior jump points which can more easily cut-off non-desirable short integer solutions. Empirical tests have been conducted to show that integrating the disaggregation technique with lattice attacks can effectively raise success ratios to 100% for randomly generated problems with density one and of dimensions up to 100. Finally, statistical regressions are conducted to test significant features, thus revealing reasonable factors behind the empirical success of our algorithms and techniques proposed in this paper.
Truck Platoon Formation at Hubs: An Optimal Release Time Rule
Authors: Alexander Johansson, Valerio Turri, Ehsan Nekouei, Karl H. Johansson, Jonas Mårtensson
Date: 2022-02-18 00:00:00

We consider a hub-based platoon coordination problem in which vehicles arrive at a hub according to an independent and identically distributed stochastic arrival process. The vehicles wait at the hub, and a platoon coordinator, at each time-step, decides whether to release the vehicles from the hub in the form of a platoon or wait for more vehicles to arrive. The platoon release time problem is modeled as a stopping rule problem wherein the objective is to maximize the average platooning benefit of the vehicles located at the hub and there is a cost of having vehicles waiting at the hub. We show that the stopping rule problem is monotone and the optimal platoon release time policy will therefore be in the form of a one time-step look-ahead rule. The performance of the optimal release rule is numerically compared with (i) a periodic release time rule and (ii) a non-causal release time rule where the coordinator knows all the future realizations of the arrival process. Our numerical results show that the optimal release time rule achieves a close performance to that of the non-causal rule and outperforms the periodic rule, especially when the arrival rate is low.
Event-Triggered Distributed Model Predictive Control for Platoon Coordination at Hubs in a Transport System
Authors: Ting Bai, Alexander Johansson, Karl Henrik Johansson, Jonas Mårtensson
Date: 2022-02-18 00:00:00
Tasks: Model Predictive Control, Scheduling

This paper considers the problem of hub-based platoon coordination for a large-scale transport system, where trucks have individual utility functions to optimize. An event-triggered distributed model predictive control method is proposed to solve the optimal scheduling of waiting times at hubs for individual trucks. In this distributed framework, trucks are allowed to decide their waiting times independently and only limited information is shared between trucks. Both the predicted reward gained from platooning and the predicted cost for waiting at hubs are included in each truck's utility function. The performance of the coordination method is demonstrated in a simulation with one hundred trucks over the Swedish road network.
"Back to the future" projections for COVID-19 surges
Authors: J. Sunil Rao, Tianhao Liu, Daniel Andrés Díaz-Pachón
Date: 2022-02-17 00:00:00
Tasks: Time Series, Time Series Analysis

We argue that information from countries who had earlier COVID-19 surges can be used to inform another country's current model, then generating what we call back-to-the-future (BTF) projections. We show that these projections can be used to accurately predict future COVID-19 surges prior to an inflection point of the daily infection curve. We show, across 12 different countries from all populated continents around the world, that our method can often predict future surges in scenarios where the traditional approaches would always predict no future surges. However, as expected, BTF projections cannot accurately predict a surge due to the emergence of a new variant. To generate BTF projections, we make use of a matching scheme for asynchronous time series combined with a response coaching SIR model.
Market-Based Price Autocorrelation
Authors: Victor Olkhov
Date: 2022-02-15 00:00:00
Tasks: Time Series, Time Series Analysis

This paper assumes that the randomness of market trade values and volumes determines the properties of stochastic market prices. We derive the direct dependence of the first two price statistical moments and price volatility on statistical moments, volatilities, and correlations of market trade values and volumes. That helps describe the dependence of market-based price autocorrelation between times t and t-{\tau} on statistical moments and correlations between trade values and volumes. That highlights the impact of the randomness of the size of market deals on price statistical moments and autocorrelation. Statistical moments and correlations of market trade values and volumes are assessed by conventional frequency-based probabilities. The distinctions between market-based price autocorrelation and autocorrelation that are assessed by the frequency-based probability analysis of price time series reveal the different approaches to the definitions of price probabilities. To forecast market-based price autocorrelation, one should predict the statistical moments and correlations of trade values and volumes.
OKVIS2: Realtime Scalable Visual-Inertial SLAM with Loop Closure
Authors: Stefan Leutenegger
Date: 2022-02-18 00:00:00
Tasks: State Estimation

Robust and accurate state estimation remains a challenge in robotics, Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial Simultaneous Localisation and Mapping (VI-SLAM) getting commoditised. Here, a full VI-SLAM system is introduced that particularly addresses challenges around long as well as repeated loop-closures. A series of experiments reveals that it achieves and in part outperforms what state-of-the-art open-source systems achieve. At the core of the algorithm sits the creation of pose-graph edges through marginalisation of common observations, which can fluidly be turned back into landmarks and observations upon loop-closure. The scheme contains a realtime estimator optimising a bounded-size factor graph consisting of observations, IMU pre-integral error terms, and pose-graph edges -- and it allows for optimisation of larger loops re-using the same factor-graph asynchronously when needed.
An Analog Signal Processing EIC-PIC Solution for Coherent Data Center Interconnects
Authors: Shivangi Chugh, Rakesh Ashok, Punit Jain, Sana Naaz, Aboobackkar Sidhique, Shalabh Gupta
Date: 2022-02-18 00:00:00

Data center interconnects (DCIs) will have to support throughputs of 400 Gbps or more per wavelength in the near future. To achieve such high data rates, coherent modulation and detection is used, which conventionally requires high-speed data conversion and signal processing in the digital domain. Alternatively, high-speed signal conditioning and processing could be carried out in co-designed photonic and electronic integrated circuits, in the optical and electrical analog domains, respectively, to achieve reduced power consumption, latency, form factor, and cost. A few demonstrations of analog domain processing electronic integrated circuits (EICs), including those of equalizer and carrier phase recovery (CPR) modules showcase progress in this direction in the literature. In this brief, for the first time, we present integration of a silicon photonic integrated coherent receiver (ICR) module with a CPR module, as a part of a complete coherent receiver solution. A phase shifter in the ICR (fabricated in a 220 nm silicon-on-insulator technology) receives feedback from a CPR EIC, and the combination compensates for the time varying phase offset between the modulated signal and the unmodulated carrier in the closed loop configuration. In this proof-of-concept demonstration, we present experimental results obtained from the stand-alone silicon photonic ICR along with its system level integration with CPR chip, for QPSK signals. The technique can be extended to a higher-order modulation format, such as 16-QAM, for data rate scaling. The proposed scheme is suitable for homodyne systems, such as polarization multiplexed carrier based self-homodyne links.
Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen
Authors: Sahar Bahrami, Jérémy Moriot, Patrice Masson, François Grondin
Date: 2022-02-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification

Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to train a model. The model is then validated with data from experiments conducted with human fingers. The localization root mean square errors (RMSE) in time and frequency domains are presented. The proposed method provides satisfactory localization results for most human-machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm and a computing time of 0.44 ms. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97% with a computing time of 0.28 ms. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.
A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time
Authors: Tony Lindeberg
Date: 2022-02-18 00:00:00

This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signal in real-time scenarios when the regular Gaussian kernel cannot be used because of its non-causal access to information from the future and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. This theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time.
Stock Embeddings: Learning Distributed Representations for Financial Assets
Authors: Rian Dolphin, Barry Smyth, Ruihai Dong
Date: 2022-02-14 00:00:00

Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine learning and deep learning techniques, focused mostly on price forecasting, the literature investigating the modelling of asset correlations has lagged somewhat. To address this, inspired by recent successes in natural language processing, we propose a neural model for training stock embeddings, which harnesses the dynamics of historical returns data in order to learn the nuanced relationships that exist between financial assets. We describe our approach in detail and discuss a number of ways that it can be used in the financial domain. Furthermore, we present the evaluation results to demonstrate the utility of this approach, compared to several important benchmarks, in two real-world financial analytics tasks.
Volatility forecasting with machine learning and intraday commonality
Authors: Chao Zhang, Yihuang Zhang, Mihai Cucuringu, Zhongmin Qian
Date: 2022-02-08 00:00:00
Tasks: BIG-bench Machine Learning

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.
Constructing a NFT Price Index and Applications
Authors: Hugo Schnoering, Hugo Inzirillo
Date: 2022-02-10 00:00:00

We are witnessing the emergence of a new digital art market, the art market 3.0. Blockchain technology has taken on a new sector which is still not well known, Non-Fungible tokens (NFT). In this paper we propose a new methodology to build a NFT Price Index that represents this new market on the whole. In addition, this index will allow us to have a look on the dynamics and performances of NFT markets, and to diagnose them.
Caplet pricing in affine models for alternative risk-free rates
Authors: Claudio Fontana
Date: 2022-02-18 00:00:00

Alternative risk-free rates (RFRs) play a central role in the reform of interest rate benchmarks. We study a model for RFRs driven by a general affine process. Under minimal assumptions, we derive explicit valuation formulas for forward-looking and backward-looking caplets/floorlets, term-basis caplets as well as 1-month and 3-month RFR futures contracts.