pwc-archive/papers-with-abstracts huggingface dataset:
An Overview of Channel Models for NGSO Satellites
Authors: Victor Monzon Baeza, Eva Lagunas, Hayder Al-Hraishawi, Symeon Chatzinotas
Date: 2022-09-21 00:00:00

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.
Joint PMD Tracking and Nonlinearity Compensation with Deep Neural Networks
Authors: Prasham Jain, Lutz Lampe, Jeebak Mitra
Date: 2022-09-21 00:00:00
Tasks: Transfer Learning

Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical fiber communication systems. Machine learning based solutions such as learned digital backpropagation (LDBP) and the recently proposed deep convolutional recurrent neural network (DCRNN) have been shown to be effective for fiber nonlinearity compensation (NLC). Incorporating distributed compensation of polarization mode dispersion (PMD) within the learned models can improve their performance even further but at the same time, it also couples the compensation of nonlinearity and PMD. Consequently, it is important to consider the time variation of PMD for such a joint compensation scheme. In this paper, we investigate the impact of PMD drift on the DCRNN model with distributed compensation of PMD. We propose a transfer learning based selective training scheme to adapt the learned neural network model to changes in PMD. We demonstrate that fine-tuning only a small subset of weights as per the proposed method is sufficient for adapting the model to PMD drift. Using decision directed feedback for online learning, we track continuous PMD drift resulting from a time-varying rotation of the state of polarization (SOP). We show that transferring knowledge from a pre-trained base model using the proposed scheme significantly reduces the re-training efforts for different PMD realizations. Applying the hinge model for SOP rotation, our simulation results show that the learned models maintain their performance gains while tracking the PMD.
A Transponder Aggregator with Efficient Use of Filtering Function for Transponder Noise Suppression
Authors: Kenya Suzuki, Osamu Moriwaki, Koichi Hadama, Keita Yamaguchi, Hiroki Taniguchi, Yoshiaki Kisaka, Daisuke Ogawa, Makoto Takeshita, Stefano Camatel, Yiran Ma, Mitsunori Fukutoku
Date: 2022-09-21 00:00:00

Colorless, directionless, and contentionless reconfigurable optical add/drop multiplexing (CDC-ROADM) provides highly flexible physical layer network configuration. Such CDC-ROADM must operate in multiple wavelength bands which are being increasingly implemented in optical transmission systems. The operation in C+L bands requires switch devices used in CDC-ROADM to also be capable of multiband operation. Recent studies on wavelength division multiplexing (WDM) systems have pointed out the impact of amplified spontaneous emission (ASE) noise generated by signals of different wavelengths, which causes OSNR degradation. Therefore, it is desirable to filter out the ASE noise from different transponders when multiplexing multiple wavelengths at the transmitter side, especially in a system with non-wavelength selective combiners such as directional couplers and multicast switches. The use of transponder aggregators with filtering functions, such as the M x N wavelength selective switch (WSS), is preferable for this filtering. However, the downside of these devices is that it is difficult to provide economical multiband support. Therefore, we propose an economical transponder aggregator configuration by allowing a certain amount of ASE superposition and reducing the number of filtering functions. In this paper, we fabricated a prototype of the proposed transponder aggregator by combining silica-based planar lightwave circuit technology and C+L band WSS, both commercially available, and verified its feasibility through transmission experiments. The novel transponder aggregator is a practical solution for a multiband CDC-ROADM system with improved OSNR performance.
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
Authors: Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Date: 2022-09-14 00:00:00
Tasks: Link Prediction

Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.
Development of theoretical frameworks in neuroscience: a pressing need in a sea of data
Authors: Horacio G. Rotstein, Fidel Santamaria
Date: 2022-09-20 00:00:00

Neuroscience is undergoing dramatic progress because of the vast data streams derived from the new technologies product of the BRAIN initiative and other enterprises. As any other scientific field, neuroscience benefits from having clear definitions of its theoretical components and their interactions. This allows generating theories that integrate knowledge, provide mechanistic insights, and predict results under new experimental conditions. However, theoretical neuroscience is a heterogeneous field that has not yet agreed on how to build theories or whether it is desirable to have an overarching theory or whether theories are simply tools to understand the brain. Here we advocate for the need of developing theoretical frameworks as a basis of generating common theoretical structures. We enumerate the elements of theoretical frameworks we deem necessary for any theory in neuroscience. In particular, we address the notions of paradigms, models, and scales of organizations. We then identify areas with pressing needs to develop brain theories: integration of statistical and dynamic approaches; multi-scale integration; coding; and interpretability in the context of Artificial Intelligence. We also point out that future theoretical frameworks would benefit from the incorporation of the principles of Evolution as a fundamental structure rather than purely mathematical or engineering principles. Rather than providing definite answers, the objective of this paper is to serve as an initial and succinct presentation of these topics to encourage discussion and further in depth development of each topic.
Prevalence of mutualism in a simple model of microbial co-evolution
Authors: Luciano Stucchi, Javier Galeano, Juan Manuel Pastor, José María Iriondo, José A. Cuesta
Date: 2022-09-20 00:00:00

Evolutionary transitions among ecological interactions are widely known, although their detailed dynamics remain absent for most population models. Adaptive dynamics has been used to illustrate how the parameters of population models might shift through evolution, but within an ecological regime. Here we use adaptive dynamics combined with a generalised logistic model of population dynamics to show that transitions of ecological interactions might appear as a consequence of evolution. To this purpose we introduce a two-microbial toy model in which population parameters are determined by a bookkeeping of resources taken from (and excreted to) the environment, as well as from the byproducts of the other species. Despite its simplicity, this model exhibits all kinds of potential ecological transitions, some of which resemble those found in nature. Overall, the model shows a clear trend toward the emergence of mutualism.
Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets
Authors: Yutong Lu, Gesine Reinert, Mihai Cucuringu
Date: 2022-09-21 00:00:00

The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in the market within a short period of time, into five types. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies which we develop using COIs achieve conspicuous returns and Sharpe ratios, in an extensive experimental setup on a universe of 457 stocks using daily data for a period of four years.
Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection
Authors: Alireza Taravat, Matthias P. Wagner, Rogerio Bonifacio, David Petit
Date: 2021-02-16 00:00:00
Proceeding: Remote Sensing 2021 2
Tasks: Boundary Detection

Accurate spatial information of agricultural fields is important for providing actionable
information to farmers, managers, and policymakers. On the other hand, the automated detection of
field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-
cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy
for field boundary detection based on the fully convolutional network architecture called ResU-Net.
The benefits of this model are two-fold: first, residual units ease training of deep networks. Second,
rich skip connections within the network could facilitate information propagation, allowing us to
design networks with fewer parameters but better performance in comparison with the traditional
U-Net model. An extensive experimental analysis is performed over the whole of Denmark using
Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms.
The presented results show that the ResU-Net model has a better performance with an average
F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an
average F1 score of 0.88 and an average Jaccard coefficient of 0.77.
Physical Interaction: Reconstructing Hand-object Interactions with Physics
Authors: Haoyu Hu, Xinyu Yi, Hao Zhang, Jun-Hai Yong, Feng Xu
Date: 2022-09-22 00:00:00
Tasks: Object

Single view-based reconstruction of hand-object interaction is challenging due to the severe observation missing caused by occlusions. This paper proposes a physics-based method to better solve the ambiguities in the reconstruction. It first proposes a force-based dynamic model of the in-hand object, which not only recovers the unobserved contacts but also solves for plausible contact forces. Next, a confidence-based slide prevention scheme is proposed, which combines both the kinematic confidences and the contact forces to jointly model static and sliding contact motion. Qualitative and quantitative experiments show that the proposed technique reconstructs both physically plausible and more accurate hand-object interaction and estimates plausible contact forces in real-time with a single RGBD sensor.
CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
Authors: Sherif Abdulatif, Ruizhe Cao, Bin Yang
Date: 2022-09-22 00:00:00
Tasks: Audio Super-Resolution, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Denoising, Generative Adversarial Network, Speech Denoising, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation, Super-Resolution

In this work, we further develop the conformer-based metric generative adversarial network (CMGAN) model for speech enhancement (SE) in the time-frequency (TF) domain. This paper builds on our previous work but takes a more in-depth look by conducting extensive ablation studies on model inputs and architectural design choices. We rigorously tested the generalization ability of the model to unseen noise types and distortions. We have fortified our claims through DNS-MOS measurements and listening tests. Rather than focusing exclusively on the speech denoising task, we extend this work to address the dereverberation and super-resolution tasks. This necessitated exploring various architectural changes, specifically metric discriminator scores and masking techniques. It is essential to highlight that this is among the earliest works that attempted complex TF-domain super-resolution. Our findings show that CMGAN outperforms existing state-of-the-art methods in the three major speech enhancement tasks: denoising, dereverberation, and super-resolution. For example, in the denoising task using the Voice Bank+DEMAND dataset, CMGAN notably exceeded the performance of prior models, attaining a PESQ score of 3.41 and an SSNR of 11.10 dB. Audio samples and CMGAN implementations are available online.
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
Authors: James Harrison, Luke Metz, Jascha Sohl-Dickstein
Date: 2022-09-22 00:00:00
Tasks: Inductive Bias

Learned optimizers -- neural networks that are trained to act as optimizers -- have the potential to dramatically accelerate training of machine learning models. However, even when meta-trained across thousands of tasks at huge computational expense, blackbox learned optimizers often struggle with stability and generalization when applied to tasks unlike those in their meta-training set. In this paper, we use tools from dynamical systems to investigate the inductive biases and stability properties of optimization algorithms, and apply the resulting insights to designing inductive biases for blackbox optimizers. Our investigation begins with a noisy quadratic model, where we characterize conditions in which optimization is stable, in terms of eigenvalues of the training dynamics. We then introduce simple modifications to a learned optimizer's architecture and meta-training procedure which lead to improved stability, and improve the optimizer's inductive bias. We apply the resulting learned optimizer to a variety of neural network training tasks, where it outperforms the current state of the art learned optimizer -- at matched optimizer computational overhead -- with regard to optimization performance and meta-training speed, and is capable of generalization to tasks far different from those it was meta-trained on.
A Generalist Neural Algorithmic Learner
Authors: Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković
Date: 2022-09-22 00:00:00
Tasks: Graph Neural Network, Learning to Execute

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner -- a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry. We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by "incorporating" knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art. We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models.
Simulation-based inference of Bayesian hierarchical models while checking for model misspecification
Authors: Florent Leclercq
Date: 2022-09-22 00:00:00

This paper presents recent methodological advances to perform simulation-based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for model misspecification. Our approach is based on a two-step framework. First, the latent function that appears as second layer of the BHM is inferred and used to diagnose possible model misspecification. Second, target parameters of the trusted model are inferred via SBI. Simulations used in the first step are recycled for score compression, which is necessary to the second step. As a proof of concept, we apply our framework to a prey-predator model built upon the Lotka-Volterra equations and involving complex observational processes.
Challenges in Visual Anomaly Detection for Mobile Robots
Authors: Dario Mantegazza, Alessandro Giusti, Luca M. Gambardella, Andrea Rizzoli, Jérôme Guzzi
Date: 2022-09-22 00:00:00
Tasks: Anomaly Detection

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.
Amortized Variational Inference: A Systematic Review
Authors: Ankush Ganguly, Sanjana Jain, Ukrit Watchareeruetai
Date: 2022-09-22 00:00:00
Tasks: Representation Learning, Variational Inference

The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem. This property enables VI to be faster than several sampling-based techniques. However, the traditional VI algorithm is not scalable to large data sets and is unable to readily infer out-of-bounds data points without re-running the optimization process. Recent developments in the field, like stochastic-, black box-, and amortized-VI, have helped address these issues. Generative modeling tasks nowadays widely make use of amortized VI for its efficiency and scalability, as it utilizes a parameterized function to learn the approximate posterior density parameters. In this paper, we review the mathematical foundations of various VI techniques to form the basis for understanding amortized VI. Additionally, we provide an overview of the recent trends that address several issues of amortized VI, such as the amortization gap, generalization issues, inconsistent representation learning, and posterior collapse. Finally, we analyze alternate divergence measures that improve VI optimization.
Batch Bayesian optimisation via density-ratio estimation with guarantees
Authors: Rafael Oliveira, Louis Tiao, Fabio Ramos
Date: 2022-09-22 00:00:00
Tasks: Bayesian Inference, Bayesian Optimisation, Decision Making, Density Ratio Estimation, Sequential Decision Making

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-making process which estimates the utility of query points via an acquisition function and a prior over functions, such as a Gaussian process. Recently, however, a reformulation of BO via density-ratio estimation (BORE) allowed reinterpreting the acquisition function as a probabilistic binary classifier, removing the need for an explicit prior over functions and increasing scalability. In this paper, we present a theoretical analysis of BORE's regret and an extension of the algorithm with improved uncertainty estimates. We also show that BORE can be naturally extended to a batch optimisation setting by recasting the problem as approximate Bayesian inference. The resulting algorithms come equipped with theoretical performance guarantees and are assessed against other batch and sequential BO baselines in a series of experiments.
A data-driven interpretation of the stability of molecular crystals
Authors: Rose K. Cersonsky, Maria Pakhnova, Edgar A. Engel, Michele Ceriotti
Date: 2022-09-21 00:00:00

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials.