pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating Antimicrobial Peptide Discovery with Latent Structure
Authors: Danqing Wang, Zeyu Wen, Fei Ye, Lei LI, Hao Zhou
Date: 2022-11-28 00:00:00
Tasks: Quantization

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Authors: Meiyi Zhu, Chunyan Feng, Caili Guo, Nan Jiang, Osvaldo Simeone
Date: 2022-12-19 00:00:00
Tasks: Decoder

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Authors: Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
Date: 2022-12-17 00:00:00
Tasks: Brain Computer Interface, Decision Making, Deep Reinforcement Learning

Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the way for the creation of intelligent human-like avatars. Our approach takes a first step toward the Metaverse systems in which the digital avatars are envisioned to be more intelligent by collecting and analyzing brain signals through cellular networks. In our proposed system, Metaverse users experience Metaverse applications while sending their brain signals via uplink wireless channels in order to create intelligent human-like avatars at the base station. As such, the digital avatars can not only give useful recommendations for the users but also enable the system to create user-driven applications. Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner. To this end, we propose a hybrid training algorithm that utilizes recent advances in deep reinforcement learning to address the problem. Specifically, our hybrid training algorithm contains three deep neural networks cooperating with each other to enable better realization of the mixed decision-making and classification problem. Simulation results show that our proposed framework can jointly address resource allocation for the system and classify brain signals of the users with highly accurate predictions.
Load Mitigation and Power Tracking Control for Multi-Rotor Turbines
Authors: Horst Schulte, Urs Giger
Date: 2022-12-18 00:00:00

A model-based feasible control strategy for multi-rotor systems is presented, pursuing two control objectives simultaneously: Mechanical loads on the main tower are to be mitigated, and an externally determined power change is to be followed to obtain fast power reference response in power systems. For this purpose, a scalable control strategy consisting of two levels is proposed: The first level consists of the decentralized control of each rotor unit. By using an LPV formalism, it is shown how the nonlinearities of the controlled system are considered in the design using a decentralized wind speed observer of each rotor to improve the overall closed-loop performance. To mitigate the lateral loads on the multi-rotor main tower caused by asymmetric rotor thrust forces, a higher-level controller is introduced. Finally, the applicability of the controller structure is demonstrated by simulation studies.
Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems
Authors: Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Date: 2022-12-17 00:00:00
Tasks: Decision Making

In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties. These algorithms often make assumptions about the underlying unknown dynamics and, as a result, can provide safety guarantees. This is more challenging for other widely used learning-based decision making algorithms such as reinforcement learning. Furthermore, the majority of existing approaches assume access to state measurements, which can be restrictive in practice. In this paper, inspired by the literature on safety filters and robust output-feedback control, we present a robust predictive output-feedback safety filter (RPOF-SF) framework that provides safety certification to an arbitrary controller applied to an uncertain nonlinear control system. The proposed RPOF-SF combines a robustly stable observer that estimates the system state from noisy measurement data and a predictive safety filter that renders an arbitrary controller safe by (possibly) minimally modifying the controller input to guarantee safety. We show in theory that the proposed RPOF-SF guarantees constraint satisfaction despite disturbances applied to the system. We demonstrate the efficacy of the proposed RPOF-SF algorithm using an uncertain mass-spring-damper system.
Design and Simulation of a Micro-coiled Digitally-Controlled Variable Inductor with a Monolithically Integrated MEMS Switch
Authors: Abdelhameed Sharaf, S. M. Eladl, A. Nasr, Mohamed Serry
Date: 2022-12-17 00:00:00

This work introduces the design analysis simulation and a standard MEMS fabrication process for a three dimensional microcoil with a magnetic core and a digital switch configuration using a completely integrated fully MEMS compatible process to achieve a digitally controlled inductance. The proposed design can also be utilized as a micro transformer. The proposed design consists of five identical 3D coils and their corresponding MEMS switches. These coils are digitally controlled to achieve a variable inductor ranging from one fifth of the coil inductance up to five times the coil inductance. A standard five layer Polymumps process is proposed to fabricate the microcoils and the integrated switches. Each micro coil is anchored directly on chip connected to the input signal from one side and the other is connected to the switch. The Ni based magnetic core improves the coil response by confining and guiding the magnetic field in the magnetic device compared to Si core based by more than five times. The presented coil has the number of windings limited by the designed length and the minimum spacing that can be realized by standard optical lithography. The coil diameter is also restricted by the limits defined by optical lithography whereas the maximum height realizable by the Polymumps process limits the height of the magnetic core and accordingly results in lower inductor performance. Based on this technique we present coils ranging from 100 um in length and ten winding up to 1000 um in length and 100 windings. The new monolithically integrated MEMS switches act as selectors to achieve a variable inductance with digital control to allow the selection among n inductance steps where n is the number of coils.
Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics
Authors: Wenjun Mei, Julien M. Hendrickx, Ge Chen, Francesco Bullo, Florian Dörfler
Date: 2022-12-17 00:00:00

Mechanistic and tractable mathematical models play a key role in understanding how social influence shapes public opinions. Recently, a weighted-median mechanism has been proposed as a new micro-foundation of opinion dynamics and validated via experimental data. Numerical studies also indicate that this new mechanism recreates some non-trivial real-world features of opinion evolution. In this paper, we conduct a thorough theoretical analysis of the weighted-median opinion dynamics. We fully characterize the set of all equilibria, and we establish the almost-sure finite-time convergence for any initial condition. Moreover, we prove a necessary and sufficient graph-theoretic condition for the almost-sure convergence to consensus, as well as a sufficient graph-theoretic condition for almost-sure persistent dissensus. It turns out that the weighted-median opinion dynamics, despite its simplicity in form, exhibit rich dynamical behavior that depends on some delicate network structures. To complement our sufficient conditions for almost-sure dissensus, we further prove that, given the influence network, determining whether the system almost surely achieves persistent dissensus is NP-hard, which reflects the complexity the network topology contributes to opinion evolution.
The determining role of covariances in large networks of stochastic neurons
Authors: Vincent Painchaud, Patrick Desrosiers, Nicolas Doyon
Date: 2022-12-19 00:00:00

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation, and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.
Identification of time-varying counterfactual parameters in nonlinear panel models
Authors: Irene Botosaru, Chris Muris
Date: 2022-12-18 00:00:00
Tasks: counterfactual

We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our main results focus on static models, with a set of results applying to models without any exogeneity conditions. We show that the survival distribution of counterfactual outcomes is identified (point or partial) in this class of models. This parameter is a building block for most partial and marginal effects of interest in applied practice that are based on the average structural function as defined by Blundell and Powell (2003, 2004). To the best of our knowledge, ours are the first results on average partial and marginal effects for binary choice and ordered choice models with two-way fixed effects and non-logistic errors.
PAC-Bayesian Treatment Allocation Under Budget Constraints
Authors: Daniel F. Pellatt
Date: 2022-12-18 00:00:00
Tasks: Generalization Bounds

This paper considers the estimation of treatment assignment rules when the policy maker faces a general budget or resource constraint. Utilizing the PAC-Bayesian framework, we propose new treatment assignment rules that allow for flexible notions of treatment outcome, treatment cost, and a budget constraint. For example, the constraint setting allows for cost-savings, when the costs of non-treatment exceed those of treatment for a subpopulation, to be factored into the budget. It also accommodates simpler settings, such as quantity constraints, and doesn't require outcome responses and costs to have the same unit of measurement. Importantly, the approach accounts for settings where budget or resource limitations may preclude treating all that can benefit, where costs may vary with individual characteristics, and where there may be uncertainty regarding the cost of treatment rules of interest. Despite the nomenclature, our theoretical analysis examines frequentist properties of the proposed rules. For stochastic rules that typically approach budget-penalized empirical welfare maximizing policies in larger samples, we derive non-asymptotic generalization bounds for the target population costs and sharp oracle-type inequalities that compare the rules' welfare regret to that of optimal policies in relevant budget categories. A closely related, non-stochastic, model aggregation treatment assignment rule is shown to inherit desirable attributes.
Liquid Democracy. Two Experiments on Delegation in Voting
Authors: Victoria Mooers, Joseph Campbell, Alessandra Casella, Lucas de Lara, Dilip Ravindran
Date: 2022-12-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Proponents of participatory democracy praise Liquid Democracy: decisions are taken by referendum, but voters delegate their votes freely. When better informed voters are present, delegation can increase the probability of a correct decision. However, delegation must be used sparely because it reduces the information aggregated through voting. In two different experiments, we find that delegation underperforms both universal majority voting and the simpler option of abstention. In a tightly controlled lab experiment where the subjects' precision of information is conveyed in precise mathematical terms and very salient, the result is due to overdelegation. In a perceptual task run online where the precision of information is not known precisely, delegation remains very high and again underperforms both majority voting and abstention. In addition, subjects substantially overestimate the precision of the better informed voters, underlining that Liquid Democracy is fragile to multiple sources of noise. The paper makes an innovative methodological contribution by combining two very different experimental procedures: the study of voting rules would benefit from complementing controlled experiments with known precision of information with tests under ambiguity, a realistic assumption in many voting situations.
Understanding the food component of inflation
Authors: Emanuel Kohlscheen
Date: 2022-12-19 00:00:00

This article presents evidence based on a panel of 35 countries over the past 30 years that the Phillips curve relation holds for food inflation. That is, broader economic overheating does push up the food component of the CPI in a systematic way. Further, general inflation expectations from professional forecasters clearly impact food price inflation. The analysis also quantifies the extent to which higher food production and imports, or lower food exports, reduce food inflation. Importantly, the link between domestic and global food prices is typically weak, with passthroughs within a year ranging from 0.07 to 0.16, after exchange rate variations are taken into account.
Optimal pricing for carbon dioxide removal under inter-regional leakage
Authors: Max Franks, Matthias Kalkuhl, Kai Lessmann
Date: 2022-12-19 00:00:00

Carbon dioxide removal (CDR) moves atmospheric carbon to geological or land-based sinks. In a first-best setting, the optimal use of CDR is achieved by a removal subsidy that equals the optimal carbon tax and marginal damages. We derive second-best policy rules for CDR subsidies and carbon taxes when no global carbon price exists but a national government implements a unilateral climate policy. We find that the optimal carbon tax differs from an optimal CDR subsidy because of carbon leakage and a balance of resource trade effect. First, the optimal removal subsidy tends to be larger than the carbon tax because of lower supply-side leakage on fossil resource markets. Second, net carbon exporters exacerbate this wedge to increase producer surplus of their carbon resource producers, implying even larger removal subsidies. Third, net carbon importers may set their removal subsidy even below their carbon tax when marginal environmental damages are small, to appropriate producer surplus from carbon exporters.
Expected Growth Criterion: An Axiomatization
Authors: Joshua Lawson
Date: 2022-12-19 00:00:00

I provide necessary and sufficient conditions for an agent's preferences to be represented by a unique ergodic transformation. Put differently, if an agent seeks to maximize the time average growth of their wealth, what axioms must their preferences obey? By answering this, I provide economic theorists a clear view of where "Ergodicity Economics" deviates from established models.
Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement
Authors: Xiang Hao, Xiaofei Li
Date: 2022-12-18 00:00:00
Tasks: Computational Efficiency, Speech Enhancement

FullSubNet is our recently proposed real-time single-channel speech enhancement network that achieves outstanding performance on the Deep Noise Suppression (DNS) Challenge dataset. A number of variants of FullSubNet have been proposed, but they all focus on the structure design towards better performance and are rarely concerned with computational efficiency. For many speech enhancement applications, a key feature is that system runs on a real-time, latency-sensitive, battery-powered platform, which strictly limits the algorithm latency and computational complexity. In this work, we propose a new architecture named Fast FullSubNet dedicated to accelerating the computation of FullSubNet. Specifically, Fast FullSubNet processes sub-band speech spectra in the mel-frequency domain by using cascaded linear-to-mel full-band, sub-band, and mel-to-linear full-band models such that frequencies involved in the sub-band computation are vastly reduced. After that, a down-sampling operation is proposed for the sub-band input sequence to further reduce the computational complexity along the time axis. Experimental results show that, compared to FullSubNet, Fast FullSubNet has only 13\% computational complexity and 16\% processing time, and achieves comparable or even better performance. Code and audio samples are available at https://github.com/Audio-WestlakeU/FullSubNet.
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRI
Authors: Silpa Babu, Sajan Goud Lingala, Namrata Vaswani
Date: 2022-12-19 00:00:00
Tasks: Compressive Sensing

This work develops a novel set of algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI1 and altGDmin-MRI2), for accelerated dynamic MRI by assuming an approximate low-rank (LR) model on the matrix formed by the vectorized images of the sequence. The LR model itself is well-known in the MRI literature; our contribution is the novel GD-based algorithms which are much faster, memory efficient, and general compared with existing work; and careful use of a 3-level hierarchical LR model. By general, we mean that, with a single choice of parameters, our method provides accurate reconstructions for multiple accelerated dynamic MRI applications, multiple sampling rates and sampling schemes. We show that our methods outperform many of the popular existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively under sampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo radial under sampling, at multiple sampling rates. Evaluations on some prospectively under sampled datasets are also provided. Our second contribution is a mini-batch subspace tracking extension that can process new measurements and return reconstructions within a short delay after they arrive. The recovery algorithm itself is also faster than its batch counterpart.
A Global Constraint to Improve CT Reconstruction Under Non-Ideal Conditions
Authors: Ziyu Shu, Alireza Entezari
Date: 2022-12-19 00:00:00
Tasks: CT Reconstruction, Semantic Segmentation

Background and Objective: The strong demand for medical imaging applications leads to the popularity of the CT reconstruction problem. Researchers proposed multiple constraints to tackle none ideal factors in CT reconstruction such as sparse-view, limited-angle, and low-dose conditions. Most of these constraints such as total variation are local constraints focusing on the relationship between a pixel and its neighbors. In this paper, we propose a new constraint utilizing the global prior of CT images to greatly reduce the streak artifacts and further improve the reconstruction accuracy. Methods: A CT image of the human body contains a limited number of different types of tissues, so pixels in CT images can be grouped into several groups according to their corresponding types. In our work, we focus on the composition classification for individual pixels and utilize it as a global prior, which differs from priors utilized by most current constraints. We propose segmenting pixels based on their gray levels during the reconstruction process, and forcing pixels in the same group to have similar gray levels. Results: Our experiments on the Shepp-Logan phantom and two real CT images from different benchmarks show that the proposed constraint can help the conventional local constraints further improve the reconstruction results under sparse-view, limited-angle, and low-dose conditions. Conclusions: Different from most current constraints focusing on the local prior, our proposed constraint only utilizes the global prior of CT images. In that case, our proposed constraint can collaborate with most local constraints and improve the reconstruction quality significantly. Furthermore, the proposed constraint also has the potential for further improvement, as the composition classification can be done with some more delicate methods, such as neural network related semantic segmentation algorithms.