pwc-archive/papers-with-abstracts huggingface dataset:
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection
Authors: Hyeonuk Nam, Seong-Hu Kim, Byeong-Yun Ko, Yong-Hwa Park
Date: 2022-03-29 00:00:00
Tasks: Event Detection, Sound Event Detection, Translation

2D convolution is widely used in sound event detection (SED) to recognize two dimensional time-frequency patterns of sound events. However, 2D convolution enforces translation equivariance on sound events along both time and frequency axis while frequency is not shift-invariant dimension. In order to improve physical consistency of 2D convolution on SED, we propose frequency dynamic convolution which applies kernel that adapts to frequency components of input. Frequency dynamic convolution outperforms the baseline by 6.3% in DESED validation dataset in terms of polyphonic sound detection score (PSDS). It also significantly outperforms other pre-existing content-adaptive methods on SED. In addition, by comparing class-wise F1 scores of baseline and frequency dynamic convolution, we showed that frequency dynamic convolution is especially more effective for detection of non-stationary sound events with intricate time-frequency patterns. From this result, we verified that frequency dynamic convolution is superior in recognizing frequency-dependent patterns.
RGB-D Neural Radiance Fields: Local Sampling for Faster Training
Authors: Arnab Dey, Andrew I. Comport
Date: 2022-03-26 00:00:00
Tasks: NeRF

Learning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advances in implicit neural representation from images using neural radiance fields(NeRF) have shown promising results. Some of the limitations of previous NeRF based methods include longer training time, and inaccurate underlying geometry. The proposed method takes advantage of RGB-D data to reduce training time by leveraging depth sensing to improve local sampling. This paper proposes a depth-guided local sampling strategy and a smaller neural network architecture to achieve faster training time without compromising quality.
BASiNETEntropy: an alignment-free method for classification of biological sequences through complex networks and entropy maximization
Authors: Murilo Montanini Breve, Matheus Henrique Pimenta-Zanon, Fabrício Martins Lopes
Date: 2022-03-24 00:00:00

The discovery of nucleic acids and the structure of DNA have brought considerable advances in the understanding of life. The development of next-generation sequencing technologies has led to a large-scale generation of data, for which computational methods have become essential for analysis and knowledge discovery. In particular, RNAs have received much attention because of the diversity of their functionalities in the organism and the discoveries of different classes with different functions in many biological processes. Therefore, the correct identification of RNA sequences is increasingly important to provide relevant information to understand the functioning of organisms. This work addresses this context by presenting a new method for the classification of biological sequences through complex networks and entropy maximization. The maximum entropy principle is proposed to identify the most informative edges about the RNA class, generating a filtered complex network. The proposed method was evaluated in the classification of different RNA classes from 13 species. The proposed method was compared to PLEK, CPC2 and BASiNET methods, outperforming all compared methods. BASiNETEntropy classified all RNA sequences with high accuracy and low standard deviation in results, showing assertiveness and robustness. The proposed method is implemented in an open source in R language and is freely available at https://cran.r-project.org/web/packages/BASiNETEntropy.
Temporal Robustness of Temporal Logic Specifications: Analysis and Control Design
Authors: Alëna Rodionova, Lars Lindemann, Manfred Morari, George J. Pappas
Date: 2022-03-29 00:00:00

We study the temporal robustness of temporal logic specifications and show how to design temporally robust control laws for time-critical control systems. This topic is of particular interest in connected systems and interleaving processes such as multi-robot and human-robot systems where uncertainty in the behavior of individual agents and humans can induce timing uncertainty. Despite the importance of time-critical systems, temporal robustness of temporal logic specifications has not been studied, especially from a control design point of view. We define synchronous and asynchronous temporal robustness and show that these notions quantify the robustness with respect to synchronous and asynchronous time shifts in the predicates of the temporal logic specification. It is further shown that the synchronous temporal robustness upper bounds the asynchronous temporal robustness. We then study the control design problem in which we aim to design a control law that maximizes the temporal robustness of a dynamical system. Our solution consists of a Mixed-Integer Linear Programming (MILP) encoding that can be used to obtain a sequence of optimal control inputs. While asynchronous temporal robustness is arguably more nuanced than synchronous temporal robustness, we show that control design using synchronous temporal robustness is computationally more efficient. This trade-off can be exploited by the designer depending on the particular application at hand. We conclude the paper with a variety of case studies.
Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control
Authors: Mahdi Razzaghpour, Sahand Mosharafian, Arash Raftari, Javad Mohammadpour Velni, Yaser P. Fallah
Date: 2022-03-29 00:00:00
Tasks: GPR, Model Predictive Control

Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving application, has been the subject of many studies in recent years. The primary motivation behind using CACC is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. Since the information flow between cooperative vehicles can significantly affect the dynamics of a platoon, the design and performance of control components are tightly dependent on the communication component performance. In addition, the choice of Information Flow Topology (IFT) can affect certain platoons properties such as stability and scalability. Although cooperative vehicles perception can be expanded to multiple predecessors information by using V2X communication, the communication technologies still suffer from scalability issues. Therefore, cooperative vehicles are required to predict each other's behavior to compensate for the effects of non-ideal communication. The notion of Model-Based Communication (MBC) was proposed to enhance cooperative vehicles perception under non-ideal communication by introducing a new flexible content structure for broadcasting joint vehicles dynamic/drivers behavior models. By utilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian Process Regression (GPR), and the MBC concept, this paper develops a discrete hybrid stochastic model predictive control approach and examines the impact of communication losses and different information flow topologies on the performance and safety of the platoon. The results demonstrate an improvement in response time and safety using more vehicles information, validating the potential of cooperation to attenuate disturbances and improve traffic flow and safety.
CycleGAN-Based Unpaired Speech Dereverberation
Authors: Hannah Muckenhirn, Aleksandr Safin, Hakan Erdogan, Felix de Chaumont Quitry, Marco Tagliasacchi, Scott Wisdom, John R. Hershey
Date: 2022-03-29 00:00:00
Tasks: Speech Dereverberation

Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance. The main limitation of this approach is that such models can only be trained on large amounts of data and a variety of room impulse responses when the data is synthetically reverberated, since acquiring real paired data is costly. In this paper we propose a CycleGAN-based approach that enables dereverberation models to be trained on unpaired data. We quantify the impact of using unpaired data by comparing the proposed unpaired model to a paired model with the same architecture and trained on the paired version of the same dataset. We show that the performance of the unpaired model is comparable to the performance of the paired model on two different datasets, according to objective evaluation metrics. Furthermore, we run two subjective evaluations and show that both models achieve comparable subjective quality on the AMI dataset, which was not seen during training.
On Metric Learning for Audio-Text Cross-Modal Retrieval
Authors: Xinhao Mei, Xubo Liu, Jianyuan Sun, Mark D. Plumbley, Wenwu Wang
Date: 2022-03-29 00:00:00
Tasks: AudioCaps, Cross-Modal Retrieval, Metric Learning, Retrieval, Self-Supervised Learning, Text Retrieval, Triplet

Audio-text retrieval aims at retrieving a target audio clip or caption from a pool of candidates given a query in another modality. Solving such cross-modal retrieval task is challenging because it not only requires learning robust feature representations for both modalities, but also requires capturing the fine-grained alignment between these two modalities. Existing cross-modal retrieval models are mostly optimized by metric learning objectives as both of them attempt to map data to an embedding space, where similar data are close together and dissimilar data are far apart. Unlike other cross-modal retrieval tasks such as image-text and video-text retrievals, audio-text retrieval is still an unexplored task. In this work, we aim to study the impact of different metric learning objectives on the audio-text retrieval task. We present an extensive evaluation of popular metric learning objectives on the AudioCaps and Clotho datasets. We demonstrate that NT-Xent loss adapted from self-supervised learning shows stable performance across different datasets and training settings, and outperforms the popular triplet-based losses. Our code is available at https://github.com/XinhaoMei/audio-text_retrieval.
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations
Authors: Si-Ioi Ng, Cymie Wing-Yee Ng, Jiarui Wang, Tan Lee
Date: 2022-03-29 00:00:00
Tasks: Speaker Verification

This paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech. Typically, SSD is manifested by persistent articulation and phonological errors on specific phonemes in the language. The disorder can be detected by focally analyzing the phonemes or the words elicited by the child subject. In the present study, instead of attempting to detect individual phone- and word-level errors, we propose to extract a subject-level representation from a long utterance that is constructed by concatenating multiple test words. The speaker verification approach, and posterior features generated by deep neural network models, are applied to derive various types of holistic representations. A linear classifier is trained to differentiate disordered speech in normal one. On the task of detecting SSD in Cantonese-speaking children, experimental results show that the proposed approach achieves improved detection performance over previous method that requires fusing phone-level detection results. Using articulatory posterior features to derive i-vectors from multiple-word utterances achieves an unweighted average recall of 78.2% and a macro F1 score of 78.0%.
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map
Authors: Seong-Hu Kim, Hyeonuk Nam, Yong-Hwa Park
Date: 2022-03-29 00:00:00
Tasks: Data Augmentation, Speaker Verification, Text-Independent Speaker Verification

To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed. However, model size of TDY-CNN is too large and the adaptive kernel's degree of freedom is limited. To address these limitations, we propose decomposed temporal dynamic CNNs (DTDY-CNNs) which forms time-adaptive kernel by combining static kernel with dynamic residual based on matrix decomposition. Proposed DTDY-ResNet-34(x0.50) using attentive statistical pooling without data augmentation shows EER of 0.96%, which is better than other state-of-the-art methods. DTDY-CNNs are successful upgrade of TDY-CNNs, reducing the model size by 64% and enhancing the performance. We showed that DTDY-CNNs extract more accurate frame-level speaker embeddings as well compared to TDY-CNNs. Detailed behaviors of DTDY-ResNet-34(x0.50) on extraction of speaker information were analyzed using speaker activation map (SAM) produced by modified gradient-weighted class activation mapping (Grad-CAM) for speaker verification. DTDY-ResNet-34(x0.50) effectively extracts speaker information from not only formant frequencies but also high frequency information of unvoiced phonemes, thus explaining its outstanding performance on text-independent speaker verification.
Commande hybride d' un drone convertible pour des déplacements sous optimaux
Authors: Florian Sansou
Date: 2022-03-29 00:00:00

Whether for their maneuverability, autonomy or ergonomics, convertible UAVs, meaning those with the ability to take off or land vertically and fly like an airplane, present many interests. However, their dynamics are complicated by various phenomena such as non-linearities, aerodynamic couplings or the large number of degrees of freedom, and it is necessary to use new control tools. Thus, hybrid control becomes crucial, useful when making binary choices. Since the drone is able to move in two ways, in pseudo-stationary flight and in horizontal flight, the question of the relevance of a command with respect to the other, according to the operational context, arises. This problem is answered by the design of two nonlinear control laws, based on Lyapunov functions, which allow to validate the requirements. Therefore, depending on the configuration, the hybrid mechanism will have to select the appropriate flight mode. Finally, with the objective of a flight in real conditions, an implementation has been initiated.
Time-optimal control of cranes subject to container height constraints
Authors: Filipe Marques Barbosa, Johan Löfberg
Date: 2022-03-29 00:00:00

The productivity and efficiency of port operations strongly depend on how fast a ship can be unloaded and loaded again. With this in mind, ship-to-shore cranes perform the critical task of transporting containers into and onto a ship and must do so as fast as possible. Though the problem of minimizing the time spent in moving the payload has been addressed in previous studies, the different heights of the container stacks have not been the focus. In this paper, we perform a change of variable and reformulate the optimization problem to deal with the constraints on the stack heights. As consequence, these constraints become trivial and easy to represent since they turn into bound constraints when the problem is discretized for the numerical solver. To validate the idea, we simulate a small-scale scenario where different stack heights are used. The results confirm our idea and the representation of the stack constraints become indeed trivial. This approach is promising to be applied in real crane operations and has the potential to enhance their automation.
On Affine Policies for Wasserstein Distributionally Robust Unit Commitment
Authors: Youngchae Cho, Insoon Yang
Date: 2022-03-29 00:00:00
Tasks: Computational Efficiency

This paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable generation as well as its tractable exact reformulation. The proposed model is formulated as a WDRO problem relying on an affine policy, which nests an infinite-dimensional worst-case expectation problem and satisfies the non-anticipativity constraint. To reduce conservativeness, we develop a novel technique that defines a subset of the uncertainty set with a probabilistic guarantee. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms. Notably, the scale of this reformulation is invariant with the sample size. As a result, a number of samples are easily incorporated without using sophisticated decomposition algorithms. Numerical simulations on 6- and 24-bus test systems demonstrate the economic and computational efficiency of the proposed model.
Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization
Authors: Shengbo Wang, Shiping Wen, Yin Yang, Yuting Cao, Kaibo Shi, TingWen Huang
Date: 2022-03-29 00:00:00

This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using the more efficient online algorithms through time-varying optimization. The idea lies in that when quadratic programming (QP) or other convex optimization algorithms needed in the CBF-based method is not computation affordable, the alternative suboptimal feasible solutions can be obtained more economically. By using the barrier-based interior point method, the constrained CBF-QP problems are converted into the unconstrained ones with suboptimal solutions tracked by two continuous descent-based algorithms. Considering the lag effect of tracking and exploiting the system information, the prediction method is added to the algorithms which thereby achieves a exponential convergence rate to the time-varying suboptimal solutions. The convergence and robustness of the designed methods as well as the safety criteria of the algorithms are analyzed theoretically. In the end, the effectiveness is illustrated by simulations on the anti-swing and obstacle avoidance tasks.
Improved DC Bus Voltage Control of Single-Phase Grid-Connected Voltage Source Converters for Minimising Bus Capacitance and Line Current Harmonics
Authors: Sakda Somkun, Viboon Chunkag
Date: 2022-03-25 00:00:00

There is a trade-off between transient performance and line current distortion of the DC bus voltage control of single-phase grid-connected voltage source converters. This paper presents an improved DC bus voltage control scheme of such converters using a proportional-integral controller in series with a first-order low pass filter. The extended symmetrical tuning method was adopted in the design of a regulator parameter, which greatly reduced the oscillating component at the double line frequency. The proposed control methodology allowed the loop bandwidth to increase without distorting the line current. Consequently, the DC bus voltage fluctuation or DC bus capacitance was reduced with a shorter settling time during a step load, compared with the conventional scheme. The proposed voltage scheme was found to be robust to the grid voltage variation and was less susceptible to the line voltage harmonics. Simulation and experimental results of a 1.5 kVA PWM rectifier verified the proposed methodology.
Phototactic cyanobacteria as an active matter system
Authors: P. Varuni, Shakti N. Menon, Gautam I. Menon
Date: 2022-03-29 00:00:00
Tasks: Friction

Flocks of birds, schools of fish, mixtures of motors and cytoskeletal filaments, swimming bacteria and driven granular media are systems of interacting motile units that exhibit collective behaviour. These can all be described as active matter systems, since each individual unit takes energy from an internal energy depot and transduces it into work performed on the environment. We review a model for cyanobacterial phototaxis, emphasising the differences from other models for collective behaviour in active matter systems. The interactions between individual cells during phototaxis are dominated by mechanical forces mediated by their physical attachments through type IV pili (T4P) and through the production of "slime", a complex mixture of non-diffusible polysaccharides deposited by cells that acts to decrease friction locally. The slime, in particular, adds a component to the interaction that is local in space but non-local in time, perhaps most comparable to the pheromones laid down in ant trails. Our results suggest that the time-delayed component of the interactions between bacteria qualify their description as a novel active system, which we refer to as "damp" active matter.
Limitations on concentration measurements and gradient discerning times in cellular systems
Authors: Vaibhav Wasnik
Date: 2022-03-29 00:00:00

This work reports on two results. At first we revisit the Berg and Purcell calculation that provides a lower bound to the error in concentration measurement by cells, by considering the realistic case when the cell starts measuring the moment it comes in contact with the chemoattractants, instead of measuring after equilibrating with the chemotactic concentration as done in the classic Berg and Purcell paper. We find that the error in concentration measurement is still the same as evaluated by Berg and Purcell. We next derive a lower bound on measurement time below which it is not possible for the cell to discern extra-cellular chemotactic gradients through spatial sensing mechanisms. This bound is independent of diffusion rate and concentration of the chemoattracts and is instead set by detachment rate of ligands from the cell receptors. The result could help explain experimental observations.
Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology
Authors: Jianhua Xing
Date: 2022-03-26 00:00:00

Cells with the same genome can exist in different phenotypes. and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis development. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
Application of Quantum Computers in Foreign Exchange Reserves Management
Authors: Martin Veselý
Date: 2022-03-29 00:00:00
Tasks: Management, Portfolio Optimization

The main purpose of this article is to evaluate possible applications of quantum computers in foreign exchange reserves management. The capabilities of quantum computers are demonstrated by means of risk measurement using the quantum Monte Carlo method and portfolio optimization using a linear equations system solver (the Harrow-Hassidim-Lloyd algorithm) and quadratic unconstrained binary optimization (the quantum approximate optimization algorithm). All demonstrations are carried out on the cloud-based IBM Quantum(TM) platform. Despite the fact that real-world applications are impossible under the current state of development of quantum computers, it is proven that in principle it will be possible to apply such computers in FX reserves management in the future. In addition, the article serves as an introduction to quantum computing for the staff of central banks and financial market supervisory authorities.