pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
Authors: Zhijun Zeng, Zhen Hou, Ting Li, Lei Deng, Jianguo Hou, Xinran Huang, Jun Li, Meirou Sun, Yunhan Wang, Qiyu Wu, Wenhao Zheng, Hua Jiang, Qi Wang
Date: 2022-02-21 00:00:00
Tasks: Deep Learning

We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term predictions of a set of ventilator parameters for the septic patient in emergency intensive care unit (EICU). The short-term predictability of the model provides attending physicians with early warnings to make timely adjustment to the treatment of the patient in the EICU. The patient specific deep learning model can be trained on any given critically ill patient, making it an intelligent aide for physicians to use in emergent medical situations.
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning
Authors: Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, Zehui Xiong
Date: 2022-02-18 00:00:00
Tasks: Federated Learning

Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages such as decentralization and privacy protection of raw data. However, there has been few research focusing on the allocation of resources for clients in BCFL. In the BCFL framework where the FL clients and the blockchain miners are the same devices, clients broadcast the trained model updates to the blockchain network and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the model owner (MO) (i.e., the BCFL task publisher) and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.
Single-Leg Revenue Management with Advice
Authors: Santiago Balseiro, Christian Kroer, Rachitesh Kumar
Date: 2022-02-18 00:00:00
Tasks: Management

Single-leg revenue management is a foundational problem of revenue management that has been particularly impactful in the airline and hotel industry: Given $n$ units of a resource, e.g. flight seats, and a stream of sequentially-arriving customers segmented by fares, what is the optimal online policy for allocating the resource. Previous work focused on designing algorithms when forecasts are available, which are not robust to inaccuracies in the forecast, or online algorithms with worst-case performance guarantees, which can be too conservative in practice. In this work, we look at the single-leg revenue management problem through the lens of the algorithms-with-advice framework, which attempts to harness the increasing prediction accuracy of machine learning methods by optimally incorporating advice about the future into online algorithms. In particular, we characterize the Pareto frontier that captures the tradeoff between consistency (performance when advice is accurate) and competitiveness (performance when advice is inaccurate) for every advice. Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier. We also study the class of protection level policies, which is the most widely-deployed technique for single-leg revenue management: we provide an algorithm to incorporate advice into protection levels that optimally trades off consistency and competitiveness. Moreover, we empirically evaluate the performance of these algorithms on synthetic data. We find that our algorithm for protection level policies performs remarkably well on most instances, even if it is not guaranteed to be on the Pareto frontier in theory. Our results extend to other unit-cost online allocations problems such as the display advertising and the multiple secretary problem together with more general variable-cost problems such as the online knapsack problem.
Adaptive Cut Selection in Mixed-Integer Linear Programming
Authors: Mark Turner, Thorsten Koch, Felipe Serrano, Michael Winkler
Date: 2022-02-22 00:00:00

Cutting plane selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance. These solvers have millions of parameter combinations, and so are excellent candidates for parameter tuning. Cut selection scoring rules are usually weighted sums of different measurements, where the weights are parameters. We present a parametric family of mixed-integer linear programs together with infinitely many family-wide valid cuts. Some of these cuts can induce integer optimal solutions directly after being applied, while others fail to do so even if an infinite amount are applied. We show for a specific cut selection rule, that any finite grid search of the parameter space will always miss all parameter values, which select integer optimal inducing cuts in an infinite amount of our problems. We propose a variation on the design of existing graph convolutional neural networks, adapting them to learn cut selection rule parameters. We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. Our framework and design show that adaptive cut selection does substantially improve performance over a diverse set of instances, but that finding a single function describing such a rule is difficult. Code for reproducing all experiments is available at https://github.com/Opt-Mucca/Adaptive-Cutsel-MILP.
Wavebender GAN: An architecture for phonetically meaningful speech manipulation
Authors: Gustavo Teodoro Döhler Beck, Ulme Wennberg, Zofia Malisz, Gustav Eje Henter
Date: 2022-02-22 00:00:00

Deep learning has revolutionised synthetic speech quality. However, it has thus far delivered little value to the speech science community. The new methods do not meet the controllability demands that practitioners in this area require e.g.: in listening tests with manipulated speech stimuli. Instead, control of different speech properties in such stimuli is achieved by using legacy signal-processing methods. This limits the range, accuracy, and speech quality of the manipulations. Also, audible artefacts have a negative impact on the methodological validity of results in speech perception studies. This work introduces a system capable of manipulating speech properties through learning rather than design. The architecture learns to control arbitrary speech properties and leverages progress in neural vocoders to obtain realistic output. Experiments with copy synthesis and manipulation of a small set of core speech features (pitch, formants, and voice quality measures) illustrate the promise of the approach for producing speech stimuli that have accurate control and high perceptual quality.
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret
Authors: Tor Lattimore
Date: 2022-02-22 00:00:00

We show that a version of the generalised information ratio of Lattimore and Gyorgy (2020) determines the asymptotic minimax regret for all finite-action partial monitoring games provided that (a) the standard definition of regret is used but the latent space where the adversary plays is potentially infinite; or (b) the regret introduced by Rustichini (1999) is used and the latent space is finite. Our results are complemented by a number of examples. For any $p \in [1/2,1]$ there exists an infinite partial monitoring game for which the minimax regret over $n$ rounds is $n^p$ up to subpolynomial factors and there exist finite games for which the minimax Rustichini regret is $n^{4/7}$ up to subpolynomial factors.
StickyLand: Breaking the Linear Presentation of Computational Notebooks
Authors: Zijie J. Wang, Katie Dai, W. Keith Edwards
Date: 2022-02-22 00:00:00

How can we better organize code in computational notebooks? Notebooks have become a popular tool among data scientists, as they seamlessly weave text and code together, supporting users to rapidly iterate and document code experiments. However, it is often challenging to organize code in notebooks, partially because there is a mismatch between the linear presentation of code and the non-linear process of exploratory data analysis. We present StickyLand, a notebook extension for empowering users to freely organize their code in non-linear ways. With sticky cells that are always shown on the screen, users can quickly access their notes, instantly observe experiment results, and easily build interactive dashboards that support complex visual analytics. Case studies highlight how our tool can enhance notebook users's productivity and identify opportunities for future notebook designs. StickyLand is available at https://github.com/xiaohk/stickyland.
Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm
Authors: Arnaud Pannatier, Ricardo Picatoste, François Fleuret
Date: 2021-12-20 00:00:00
Tasks: GPU

This paper proposes a simple yet efficient high-altitude wind nowcasting pipeline. It processes efficiently a vast amount of live data recorded by airplanes over the whole airspace and reconstructs the wind field with good accuracy. It creates a unique context for each point in the dataset and then extrapolates from it. As creating such context is computationally intensive, this paper proposes a novel algorithm that reduces the time and memory cost by efficiently fetching nearest neighbors in a data set whose elements are organized along smooth trajectories that can be approximated with piece-wise linear structures. We introduce an efficient and exact strategy implemented through algebraic tensorial operations, which is well-suited to modern GPU-based computing infrastructure. This method employs a scalable Euclidean metric and allows masking data points along one dimension. When applied, this method is more efficient than plain Euclidean k-NN and other well-known data selection methods such as KDTrees and provides a several-fold speedup. We provide an implementation in PyTorch and a novel data set to allow the replication of empirical results.
Variational encoding approach for interpretable assessment of remaining useful life estimation
Authors: Nahuel Costa, Luciano Sanchez
Date: 2022-02-23 00:00:00
Proceeding: Reliability Engineering & System Safety (RESS) 2022 2
Tasks: Management, Remaining Useful Lifetime Estimation, Variational Inference

A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. To overcome this gap we propose a novel approach based on variational encoding. The model consists of a recurrent encoder and a regression model: the encoder learns to compress the input data to a latent space that serves as a basis to build a self-explanatory map that can visually evaluate the rate of deterioration of aircraft engines. Obtaining such a latent space is regularized by a new cost function guided by variational inference and a term that penalizes prediction errors. Consequently, not only an interpretable assessment is achieved but also a remarkable prognostic accuracy, outperforming most of the state-of-the-art approaches on the popular simulation dataset C-MAPSS from NASA. In addition, we demonstrate the application of our method in a real-world scenario with data from actual Turbofan engines.
Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology
Authors: Nam Nguyen, Kwang-cheng Chen
Date: 2022-02-21 00:00:00
Tasks: Bayesian Optimization, Decision Making, Representation Learning

Quantifying the dynamics of tumor burden reveals useful information about cancer evolution concerning treatment effects and drug resistance, which play a crucial role in advancing model-informed drug developments (MIDD) towards personalized medicine and precision oncology. The emergence of Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. This paper introduces a novel hybrid quantum-classical neural architecture named $\eta-$Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects. We evaluate our proposed neural solution on two major use cases, including cohort-specific and patient-specific modeling. In silico numerical results show a high capacity and expressivity of $\eta-$Net to the quantified biological problem. Moreover, the close connection to representation learning - the foundation for successes of modern AI, enables efficient transferability of empirical knowledge from relevant cohorts to targeted patients. Finally, we leverage Bayesian optimization to quantify the epistemic uncertainty of model predictions, paving the way for $\eta-$Net towards reliable AI in decision-making for clinical usages.
A Current Supported PV Array Reconfiguration Technique to Mitigate Partial Shading
Authors: Binoy Kumar Karmakar, Gopinath Karmakar
Date: 2021-04-05 00:00:00
Proceeding: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021 4

Partial shading in a solar PV plant is an important concern as it reduces the output power. Dynamic array reconfiguration (DAR) helps in reducing the impact of partial shading. But, it has its own limitations. Further, DAR does not guarantee single peak in array power voltage characteristics during partial shading. It requires sophisticated and costly MPPT controller to track the global peak in order to extract maximum power from the PV array. In this paper, an improved DAR strategy is proposed using current injection (CI). This CI-based DAR strategy enhances the array power output beyond what is achievable by the available DAR techniques. Using this technique, multiple peaks in the power-voltage characteristics of the PV array are also avoided. The proposed method is evaluated for different levels of shading and its patterns. Experimental results establish the superiority of the CI-based DAR method over the available PV array reconfiguration strategies
Event-Triggered Tracking Control of Networked Multi-Agent Systems
Authors: Wei Ren, Dimos V. Dimarogonas
Date: 2022-02-22 00:00:00

This paper studies the tracking control problem of networked multi-agent systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect multiple agents and reference systems with decentralized controllers to guarantee their information transmission, whereas the event-triggered mechanisms are to reduce the information transmission via the networks. In this paper, each agent has a network to communicate with its controller and reference system, and all networks are independent and asynchronous and have local event-triggered mechanisms, which are based on local measurements and determine whether the local measurements need to be transmitted via the corresponding network. To address this scenario, we first implement the emulation-based approach to develop a novel hybrid model for the tracking control of networked multi-agent systems. Next, sufficient conditions are derived and decentralized event-triggered mechanisms are designed to guarantee the desired tracking performance. Furthermore, the proposed approach is applied to derive novel results for the event-triggered observer design problem of networked multi-agent systems. Finally, two numerical examples are presented to illustrate the validity of the developed results.
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition
Authors: Jinhan Wang, Xiaosu Tong, Jinxi Guo, Di He, Roland Maas
Date: 2022-02-22 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Form, speech-recognition, Speech Recognition

While end-to-end models have shown great success on the Automatic Speech Recognition task, performance degrades severely when target sentences are long-form. The previous proposed methods, (partial) overlapping inference are shown to be effective on long-form decoding. For both methods, word error rate (WER) decreases monotonically when overlapping percentage decreases. Setting aside computational cost, the setup with 50% overlapping during inference can achieve the best performance. However, a lower overlapping percentage has an advantage of fast inference speed. In this paper, we first conduct comprehensive experiments comparing overlapping inference and partial overlapping inference with various configurations. We then propose Voice-Activity-Detection Overlapping Inference to provide a trade-off between WER and computation cost. Results show that the proposed method can achieve a 20% relative computation cost reduction on Librispeech and Microsoft Speech Language Translation long-form corpus while maintaining the WER performance when comparing to the best performing overlapping inference algorithm. We also propose Soft-Match to compensate for similar words mis-aligned problem.
Spanish and English Phoneme Recognition by Training on Simulated Classroom Audio Recordings of Collaborative Learning Environments
Authors: Mario Esparza
Date: 2022-02-21 00:00:00
Tasks: Data Augmentation, Phoneme Recognition, speech-recognition, Speech Recognition, Speech-to-Text

Audio recordings of collaborative learning environments contain a constant presence of cross-talk and background noise. Dynamic speech recognition between Spanish and English is required in these environments. To eliminate the standard requirement of large-scale ground truth, the thesis develops a simulated dataset by transforming audio transcriptions into phonemes and using 3D speaker geometry and data augmentation to generate an acoustic simulation of Spanish and English speech. The thesis develops a low-complexity neural network for recognizing Spanish and English phonemes (available at github.com/muelitas/keywordRec). When trained on 41 English phonemes, 0.099 PER is achieved on Speech Commands. When trained on 36 Spanish phonemes and tested on real recordings of collaborative learning environments, a 0.7208 LER is achieved. Slightly better than Google's Speech-to-text 0.7272 LER, which used anywhere from 15 to 1,635 times more parameters and trained on 300 to 27,500 hours of real data as opposed to 13 hours of simulated audios.
On Local Distributions in Graph Signal Processing
Authors: T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk, Santiago Segarra
Date: 2022-02-22 00:00:00

Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborhood of each node. Moreover, a graph filter's output can be computed separately at each node by carrying out repeated exchanges with immediate neighbors. Graph filters can be compactly written as polynomials of a graph shift operator (typically, a sparse matrix description of the graph). This has led to relating the properties of the filters with the spectral properties of the corresponding matrix -- which encodes global structure of the graph. In this work, we propose a framework that relies solely on the local distribution of the neighborhoods of a graph. The crux of this approach is to describe graphs and graph signals in terms of a measurable space of rooted balls. Leveraging this, we are able to seamlessly compare graphs of different sizes and coming from different models, yielding results on the convergence of spectral densities, transferability of filters across arbitrary graphs, and continuity of graph signal properties with respect to the distribution of local substructures.
Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences
Authors: Martin Higgins, Wangkun Xu, Fei Teng, Thomas Parisini
Date: 2022-02-22 00:00:00

In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Many works consider the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection (BDD). In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector.
Behaviour-neutral Smart Charging of Plugin Electric Vehicles: Reinforcement learning approach
Authors: Vladimir Dyo
Date: 2022-02-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

High-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV's past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user's charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point.