pwc-archive/papers-with-abstracts huggingface dataset:
Decomposition Problem in Process of Selective Identification and Localization of Voltage Fluctuations Sources in Power Grids
Authors: Piotr Kuwałek
Date: 2022-02-10 00:00:00

Voltage fluctuations are common disturbances in power grids, therefore the effective and selective process of identification and localization of individual voltage fluctuations sources is necessary for the minimization of such disturbances. Selectivity in the process of identification and localization disturbing loads is possible by the use cascade of blocks: demodulation, decomposition and propagation assessment. The effectiveness of this approach is closely related to the used method of decomposition. The paper presents the problem of decomposition process for the selected method of selective identification and localization of voltage fluctuation sources, in which the algorithm of enhanced empirical wavelet transform (EEWT) is used as the decomposition method. The paper presents selected research results from the real power grid, for which the result of selected approach causes mistakes in the process of identification and localization of voltage fluctuations sources. The potential causes of such mistakes related to the decomposition process are discussed on the basis of obtained research results.
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions
Authors: Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yaoa
Date: 2021-09-15 00:00:00
Proceeding: Chemical Engineering Journal 2021 9
Tasks: Diversity, Retrosynthesis, Single-step retrosynthesis

Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a single-step template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two stages are accomplished with versatile Transformer models, respectively. RetroPrime achieves the Top-1 accuracy of 64.8% and 51.4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset. And the Top-1 accuracy is close to the state-of-the-art transformer-based method in the large dataset USPTO-full. It is known that outputs of the Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high chemical implausibility. These problems may limit the potential of Transformer-based methods in real practice, yet few works address both issues simultaneously. RetroPrime is designed to tackle these challenges.
Chemformer: a pre-trained transformer for computational chemistry
Authors: Ross Irwin, Spyridon Dimitriadis, Jiazhen He, Esben Jannik Bjerrum
Date: 2022-01-31 00:00:00
Proceeding: Machine Learning: Science and Technology 2022 1
Tasks: Computational chemistry, Retrosynthesis, Single-step retrosynthesis

Transformer models coupled with a simplified molecular line entry system (SMILES) have recently proven to be a powerful combination for solving challenges in cheminformatics. These models, however, are often developed specifically for a single application and can be very resource-intensive to train. In this work we present the Chemformer model—a Transformer-based model which can be quickly applied to both sequence-to-sequence and discriminative cheminformatics tasks. Additionally, we show that self-supervised pre-training can improve performance and significantly speed up convergence on downstream tasks. On direct synthesis and retrosynthesis prediction benchmark datasets we publish state-of-the-art results for top-1 accuracy. We also improve on existing approaches for a molecular optimisation task and show that Chemformer can optimise on multiple discriminative tasks simultaneously. Models, datasets and code will be made available after publication.
Learning Graph Models for Template-Free Retrosynthesis
Authors: Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay
Date: 2021-06-04 00:00:00
Proceeding: arXiv 2021 6
Tasks: Retrosynthesis, Single-step retrosynthesis

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of 53.7%, outperforming previous template-free and semi-template-based methods
Study of COVID-19 epidemiological evolution in India with a multi-wave SIR model
Authors: Kalpita Ghosh, Asim Kumar Ghosh
Date: 2022-02-10 00:00:00

The global pandemic due to the outbreak of COVID-19 ravages the whole world for more than two years in which all the countries are suffering a lot since December 2019. In order to control this ongoing waves of epidemiological infections, attempts have been made to understand the dynamics of this pandemic in deterministic approach with the help of several mathematical models. In this article characteristics of a multi-wave SIR model have been studied which successfully explains the features of this pandemic waves in India. Stability of this model has been studied by identifying the equilibrium points as well as by finding the eigen values of the corresponding Jacobian matrices. Complex eigen values are found which ultimately give rise to the oscillatory solutions for the three categories of populations, say, susceptible, infected and removed. In this model, a finite probability of the recovered people for becoming susceptible again is introduced which eventually lead to the oscillatory solution in other words. The set of differential equations has been solved numerically in order to obtain the variation for numbers of susceptible, infected and removed people with time. In this phenomenological study, finally an additional modification is made in order to explain the aperiodic oscillation which is found necessary to capture the feature of epidemiological waves particularly in India.
Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning
Authors: Pratik Kumar, Ankita Nandi, Shantanu Chakrabartty, Chetan Singh Thakur
Date: 2022-02-10 00:00:00

Bias-scalable analog computing is attractive for implementing machine learning (ML) processors with distinct power-performance specifications. For instance, ML implementations for server workloads are focused on higher computational throughput for faster training, whereas ML implementations for edge devices are focused on energy-efficient inference. In this paper, we demonstrate the implementation of bias-scalable approximate analog computing circuits using the generalization of the margin-propagation principle called shape-based analog computing (S-AC). The resulting S-AC core integrates several near-memory compute elements, which include: (a) non-linear activation functions; (b) inner-product compute circuits; and (c) a mixed-signal compressive memory, all of which can be scaled for performance or power while preserving its functionality. Using measured results from prototypes fabricated in a 180nm CMOS process, we demonstrate that the performance of computing modules remains robust to transistor biasing and variations in temperature. In this paper, we also demonstrate the effect of bias-scalability and computational accuracy on a simple ML regression task.
On-the-fly 3D metrology of volumetric additive manufacturing
Authors: Antony Orth, Kathleen L. Sampson, Yujie Zhang, Kayley Ting, Derek Aranguren van Egmond, Kurtis Laqua, Thomas Lacelle, Daniel Webber, Dorothy Fathi, Jonathan Boisvert, Chantal Paquet
Date: 2022-02-07 00:00:00
Tasks: Defect Detection

Additive manufacturing techniques are revolutionizing product development by enabling fast turnaround from design to fabrication. However, the throughput of the rapid prototyping pipeline remains constrained by print optimization, requiring multiple iterations of fabrication and ex-situ metrology. Despite the need for a suitable technology, robust in-situ shape measurement of an entire print is not currently available with any additive manufacturing modality. Here, we address this shortcoming by demonstrating fully simultaneous 3D metrology and printing. We exploit the dramatic increase in light scattering by a photoresin during gelation for real-time 3D imaging of prints during tomographic volumetric additive manufacturing. Tomographic imaging of the light scattering density in the build volume yields quantitative, artifact-free 3D + time models of cured objects that are accurate to below 1% of the size of the print. By integrating shape measurement into the printing process, our work paves the way for next-generation rapid prototyping with real-time defect detection and correction.
Matching with Transfers under Distributional Constraints
Authors: Devansh Jalota, Michael Ostrovsky, Marco Pavone
Date: 2022-02-10 00:00:00

We study two-sided many-to-one matching markets with transferable utilities, e.g., labor and rental housing markets, in which money can exchange hands between agents, subject to distributional constraints on the set of feasible allocations. In such markets, we establish the efficiency of equilibrium arrangements, specified by an assignment and transfers between agents on the two sides of the market, and study the conditions on the distributional constraints and agent preferences under which equilibria exist and can be computed efficiently. To this end, we first consider the setting when the number of institutions (e.g., firms in a labor market) is one and show that equilibrium arrangements exist irrespective of the nature of the constraint structure or the agents' preferences. However, equilibrium arrangements may not exist in markets with multiple institutions even when agents on each side have linear (or additively separable) preferences over agents on the other side. Thus, for markets with linear preferences, we study sufficient conditions on the constraint structure that guarantee the existence of equilibria using linear programming duality. Our linear programming approach not only generalizes that of Shapley and Shubik (1971) in the one-to-one matching setting to the many-to-one matching setting under distributional constraints but also provides a method to compute market equilibria efficiently.
IEC Flickermeter Measurement Results for Distorted Modulating Signal while Supplied with Distorted Voltage
Authors: Piotr Kuwałek
Date: 2022-02-10 00:00:00

The paper presents IEC flickermeter measurement results for voltage fluctuations modelled by amplitude modulation of distorted supply voltage. The supply voltage distortion caused by electronic and power electronic devices in the "clipped cosine" form is assumed. This type of supply voltage distortion is a common disturbance in low voltage networks. Several arbitrary distorted waveforms of the modulating signal with different modulation depth and modulating frequency up to approx. 1 kHz are selected to determine the dependence of severity of voltage fluctuation on their shape. The paper mainly presents the dependence of voltage fluctuation severity with a frequency greater than 3fc, where fc is the power frequency. The voltage fluctuation severity and the dependencies associated with it have been determined on the basis of numerical simulation studies and experimental laboratory tests.
A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification
Authors: You Zhang, Ge Zhu, Zhiyao Duan
Date: 2022-02-10 00:00:00
Tasks: Speaker Verification

The performance of automatic speaker verification (ASV) systems could be degraded by voice spoofing attacks. Most existing works aimed to develop standalone spoofing countermeasure (CM) systems. Relatively little work targeted at developing an integrated spoofing aware speaker verification (SASV) system. In the recent SASV challenge, the organizers encourage the development of such integration by releasing official protocols and baselines. In this paper, we build a probabilistic framework for fusing the ASV and CM subsystem scores. We further propose fusion strategies for direct inference and fine-tuning to predict the SASV score based on the framework. Surprisingly, these strategies significantly improve the SASV equal error rate (EER) from 19.31% of the baseline to 1.53% on the official evaluation trials of the SASV challenge. We verify the effectiveness of our proposed components through ablation studies and provide insights with score distribution analysis.
Spatial active noise control based on individual kernel interpolation of primary and secondary sound fields
Authors: Kazuyuki Arikawa, Shoichi Koyama, Hiroshi Saruwatari
Date: 2022-02-10 00:00:00

A spatial active noise control (ANC) method based on the individual kernel interpolation of primary and secondary sound fields is proposed. Spatial ANC is aimed at cancelling unwanted primary noise within a continuous region by using multiple secondary sources and microphones. A method based on the kernel interpolation of a sound field makes it possible to attenuate noise over the target region with flexible array geometry. Furthermore, by using the kernel function with directional weighting, prior information on primary noise source directions can be taken into consideration. However, whereas the sound field to be interpolated is a superposition of primary and secondary sound fields, the directional weight for the primary noise source was applied to the total sound field in previous work; therefore, the performance improvement was limited. We propose a method of individually interpolating the primary and secondary sound fields and formulate a normalized least-mean-square algorithm based on this interpolation method. Experimental results indicate that the proposed method outperforms the method based on total kernel interpolation.
SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid
Authors: Ashwin Shirsat, Valliappan Muthukaruppan, Rongxing Hu, Victor Paduani, Bei Xu, Lidong Song, Yiyan Li, Ning Lu, Mesut Baran, David Lubkeman, Wenyuan Tang
Date: 2022-02-10 00:00:00
Tasks: Scheduling

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMG is challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using newly obtained forecasts, followed by the RT dispatch stage. To make the dispatch decisions more secure and robust against forecast errors, a novel concept called delayed recourse is proposed. The methodology is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS/hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure CMG operation under numerous operating scenarios.
A Novel Four-DOF Lagrangian Approach to Attitude Tracking for Rigid Spacecraft
Authors: Eduardo Espíndola, Yu Tang
Date: 2022-02-10 00:00:00

This paper presents a novel Lagrangian approach to attitude tracking for rigid spacecraft using unit quaternions, where the motion equations of a spacecraft are described by a four degrees of freedom Lagrangian dynamics subject to a holonomic constraint imposed by the norm of a unit quaternion. The basic energy-conservation property as well as some additional useful properties of the Lagrangian dynamics are explored, enabling to develop quaternion-based attitude tracking controllers by taking full advantage of a broad class of tracking control designs for mechanical systems based on energy-shaping methodology. Global tracking of a desired attitude on the unit sphere is achieved by designing control laws that render the tracking error on the four-dimensional Euclidean space to converge to the origin. The topological constraints for globally exponentially tracking by a quaternion-based continuous controller and singularities in controller designs based on any three-parameter representation of the attitude are then avoided. Using this approach, a full-state feedback controller is first developed, and then several important issues, such as robustness to noise in quaternion measurements, unknown on-orbit torque disturbances, uncertainty in the inertial matrix, and lack of angular-velocity measurements are addressed progressively, by designing a hybrid state-feedback controller, an adaptive hybrid state-feedback controller, and an adaptive hybrid attitude-feedback controller. Global asymptotic stability is established for each controller. Simulations are included to illustrate the theoretical results.
Behavior-Semantic Scenery Description (BSSD) of Road Networks for Automated Driving
Authors: Moritz Lippert, Felix Glatzki, Hermann Winner
Date: 2022-02-10 00:00:00

The safety approval of Highly Automated Vehicles (HAV) is economically infeasible with current approaches. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD.
Optimised operation of low-emission offshore oil and gas platform integrated energy systems
Authors: Harald G Svendsen
Date: 2022-02-10 00:00:00

This paper considers the operation of offshore oil and gas platform energy systems with energy supply from wind turbines to reduce local CO2 emissions. A new integrated energy system model for operational planning and simulation has been developed and implemented in an open-source software tool (Oogeso). This model and tool is first presented, and then applied on a relevant North Sea case with different energy supply alternatives to quantify and compare CO2 emission reductions and other key indicators.
Should Storage-Centric Tariffs be Extended to Commercial Flexible Demand?
Authors: Lane D. Smith, Daniel S. Kirschen
Date: 2022-02-10 00:00:00

Further electrification of the economy is expected to sharpen ramp rates and increase peak loads. Flexibility from the demand side, which new technologies might facilitate, can help these operational challenges. Electric utilities have begun implementing new tariffs and other mechanisms to encourage the deployment of energy storage. This paper examines whether making these new tariffs technology agnostic and extending them to flexible demand would significantly improve the procurement of operational flexibility. In particular, we consider how a commercial consumer might adjust its flexible demand when subject to Pacific Gas and Electric Company's storage-centric electric tariff. We show that extending this tariff to consumers with flexible demand would reduce the utility's net demand ramp rates during peak hours. If consumers have a high level of demand flexibility, this tariff also reduces the net demand during peak hours and decreases total electric bills when compared to the base tariff.
Discovering Quantum Phase Transitions with Fermionic Neural Networks
Authors: G. Cassella, H. Sutterud, S. Azadi, N. D. Drummond, D. Pfau, J. S. Spencer, W. M. C. Foulkes
Date: 2022-02-10 00:00:00
Tasks: Variational Monte Carlo

Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network is given no \emph{a priori} knowledge that a phase transition exists, but converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density.
Diffusion-induced anisotropic cancer invasion: a novel experimental method based on tumour spheroids
Authors: Rosalia Ferraro, Flora Ascione, Prashant Dogra, Vittorio Cristini, Stefano Guido, Sergio Caserta
Date: 2022-02-10 00:00:00

Tumour invasion is strongly influenced by microenvironment and, among other parameters, chemical stimuli play an important role. An innovative methodology for the quantitative investigation of chemotaxis in vitro by live imaging of morphology of cell spheroids, in 3D collagen gel, is presented here. The assay was performed by using a chemotactic chamber to impose a controlled gradients of nutrients (glucose) on spheroids, mimicking the chemotactic stimuli naturally occurring in the proximity of blood vessels. Different tumoral cell lines (PANC-1 and HT-1080) are compared to non-tumoral ones (NIH/3T3). Morphology response is observed by means a Time-lapse workstation equipped with an incubating system and quantified by image analysis techniques. Description of invasion phenomena was based on an engineering approach, based on transport phenomena concepts. As expected, NIH/3T3 spheroids are characterized by a limited tendency of cells to invade the surrounding tissue, unlike PANC-1 and HT-1080 that show relatively stronger response to gradients.