pwc-archive/papers-with-abstracts huggingface dataset:
Active RIS-Assisted mmWave Indoor Signal Enhancement Based on Transparent RIS
Authors: Hao Feng, Yuping Zhao
Date: 2023-05-16 00:00:00

Due to the serious path loss of millimeter-wave (mmWave), the signal sent by the base station is seriously attenuated when it reaches the indoors. Recent studies have proposed a glass-based metasurface that can enhance mmWave indoor signals. The transparent reconfigurable intelligent surface (RIS) focuses on the mmWave signal to a specific location indoors. In this paper, a novel RIS-assisted mmWave indoor enhancement scheme is proposed, in which a transparent RIS is deployed on the glass to enhance mmWave indoor signals, and three assisted transmission scenarios, namely passive RIS (PRIS), active RIS (ARIS), and a novel hybrid RIS (HRIS) are proposed. This paper aims to maximize the signal-to-noise ratio (SNR) of the received signal for the three assisted transmission scenarios. The closed-form solution to the maximum SNR is presented in the PRIS and the ARIS-assisted transmission scenarios. Meanwhile, the closed-form solution to the maximum SNR for the HRIS-assisted transmission scenario is presented for given active unit cells. In addition, the performance of the proposed scheme is analyzed under three assisted transmission scenarios. The results indicate that under a specific RIS power budget, the ARIS-assisted transmission scenario achieves the highest data rate and energy efficiency. Also, it requires very few unit cells, thus dramatically reducing the size of the metasurface.
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels
Authors: Yifan Wang, Minhao Zhang, Xingbin Tu, Zhipeng Li, Fengzhong Qu, Yan Wei
Date: 2023-05-16 00:00:00
Tasks: Compressive Sensing

Block transmission systems have been proven successful over frequency-selective channels. For time-varying channel such as in high-speed mobile communication and underwater communication, existing equalizers assume that channels over different data frames are independent. However, the real-world channels over different data frames are correlated, thereby indicating potentials for performance improvement. In this paper, we propose a joint channel estimation and equalization/decoding algorithm for a single-carrier system that exploits temporal correlations of channel between transmitted data frames. Leveraging the concept of dynamic compressive sensing, our method can utilize the information of several data frames to achieve better performance. The information not only passes between the channel and symbol, but also the channels over different data frames. Numerical simulations using an extensively validated underwater acoustic model with a time-varying channel establish that the proposed algorithm outperforms the former bilinear generalized approximate message passing equalizer and classic minimum mean square error turbo equalizer in bit error rate and channel estimation normalized mean square error. The algorithm idea we present can also find applications in other bilinear multiple measurements vector compressive sensing problems.
Hierarchical Codebook Design with Dynamic Metasurface Antennas for Energy-Efficient Arrays
Authors: Joseph Carlson, Miguel R. Castellanos, Robert W. Heath Jr
Date: 2023-05-15 00:00:00

Dynamic metasurface antennas (DMA) provide a solution to form compact, cost-effective, energy-efficient multiple-input-multiple output (MIMO) arrays. In this paper, we implement a practical hierarchical codebook with a realistic DMA design through electromagnetic simulations. We leverage existing DMA models to derive a novel method for enhancing the beamforming gain. We find that the proposed method provides better coverage and spectral efficiency results than prior methods. We also present and verify a new technique for creating wide beamwidths through the DMA and hierarchical codebook. Additionally, we use a detailed transmitter architecture model to determine the power consumption savings of the DMA compared to a typical phased array. The DMA largely outperforms a passive phased array in terms of spectral and energy efficiency due to high component loss from a high-resolution passive phase shifter. While the DMA provides lower spectral efficiency results than the active phased array, the DMA achieves a higher energy efficiency because of the significant power consumption for the active phase shifters. Therefore, we find that DMAs in a realistic wireless environment provide sufficient coverage and spectral efficiency compared to typical phased arrays while maintaining a substantially lower power consumption.
A Learning-Inspired Strategy to Design Binary Sequences with Good Correlation Properties: SISO and MIMO Radar Systems
Authors: Omid Rezaei, Mahdi Ahmadi, Mohammad Mahdi Naghsh, Augusto Aubry, Mohammad Mahdi Nayebi, Antonio De Maio
Date: 2023-05-15 00:00:00

In this paper, the design of binary sequences exhibiting low values of aperiodic/periodic correlation functions, in terms of Integrated Sidelobe Level (ISL), is pursued via a learning-inspired method. Specifcally, the synthesis of either a single or a burst of codes is addressed, with reference to both Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) radar systems. Two optimization machines, referred to as two-layer and single-layer Binary Sequence Correlation Network (BiSCorN), able to learn actions to design binary sequences with small ISL/Complementary ISL (CISL) for SISO and MIMO systems are proposed. These two networks differ in terms of the capability to synthesize Low-Correlation-Zone (LCZ) sequences and computational cost. Numerical experiments show that proposed techniques can outperform state-of-the-art algorithms for the design of binary sequences and Complementary Sets of Sequences (CSS) in terms of ISL and, interestingly, of Peak Sidelobe Level (PSL).
Time-Consistent Asset Allocation for Risk Measures in a Lévy Market
Authors: Felix Fießinger, Mitja Stadje
Date: 2023-05-16 00:00:00

Focusing on gains & losses relative to a risk-free benchmark instead of terminal wealth, we consider an asset allocation problem to maximize time-consistently a mean-risk reward function with a general risk measure which is i) law-invariant, ii) cash- or shift-invariant, and iii) positively homogeneous, and possibly plugged into a general function. Examples include (relative) Value at Risk, coherent risk measures, variance, and generalized deviation risk measures. We model the market via a generalized version of the multi-dimensional Black-Scholes model using $\alpha$-stable L\'evy processes and give supplementary results for the classical Black-Scholes model. The optimal solution to this problem is a Nash subgame equilibrium given by the solution of an extended Hamilton-Jacobi-Bellman equation. Moreover, we show that the optimal solution is deterministic under appropriate assumptions.
Context-enriched molecule representations improve few-shot drug discovery
Authors: Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer
Date: 2023-04-24 00:00:00
Tasks: Drug Discovery, Few-Shot Learning

A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiveness of this critical phase of the drug discovery process. We introduce a new method for few-shot drug discovery. Its main idea is to enrich a molecule representation by knowledge about known context or reference molecules. Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network. Intuitively, this enrichment step is analogous to a human expert who would associate a given molecule with familiar molecules whose properties are known. The enrichment step reinforces and amplifies the covariance structure of the data, while simultaneously removing spurious correlations arising from the decoration of molecules. Our approach is compared with other few-shot methods for drug discovery on the FS-Mol benchmark dataset. On FS-Mol, our approach outperforms all compared methods and therefore sets a new state-of-the art for few-shot learning in drug discovery. An ablation study shows that the enrichment step of our method is the key to improve the predictive quality. In a domain shift experiment, we further demonstrate the robustness of our method. Code is available at https://github.com/ml-jku/MHNfs.
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
Authors: Cheng Tan, Zhangyang Gao, Lirong Wu, Jun Xia, Jiangbin Zheng, Xihong Yang, Yue Liu, Bozhen Hu, Stan Z. Li
Date: 2023-04-21 00:00:00
Tasks: Specificity

Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studies have utilized complex techniques to generate CDRs, but they suffer from inadequate geometric modeling. Moreover, the common iterative refinement strategies lead to an inefficient inference. In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple the antibody CDR design problem into two stages: (i) geometric modeling of protein complex structures and (ii) sequence-structure co-learning. We develop a novel macromolecular structure invariant embedding, typically for protein complexes, that captures both intra- and inter-component interactions among the backbone atoms, including C$\alpha$, N, C, and O atoms, to achieve comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP for sequence-structure co-learning, allowing sequence and structure representations to implicitly refine each other. This enables our model to design desired sequences and structures in a one-shot manner. Extensive experiments are conducted to evaluate our results at both the sequence and structure levels, which demonstrate that our model achieves superior performance compared to the state-of-the-art antibody CDR design methods.
Smoothing in linear multicompartment biological processes subject to stochastic input
Authors: Alexander P Browning, Adrianne L Jenner, Ruth E Baker, Philip K Maini
Date: 2023-05-03 00:00:00

Many physical and biological systems rely on the progression of material through multiple independent stages. In viral replication, for example, virions enter a cell to undergo a complex process comprising several disparate stages before the eventual accumulation and release of replicated virions. While such systems may have some control over the internal dynamics that make up this progression, a challenge for many is to regulate behaviour under what are often highly variable external environments acting as system inputs. In this work, we study a simple analogue of this problem through a linear multicompartment model subject to a stochastic input in the form of a mean-reverting Ornstein-Uhlenbeck process, a type of Gaussian process. By expressing the system as a multidimensional Gaussian process, we derive several closed-form analytical results relating to the covariances and autocorrelations of the system, quantifying the smoothing effect discrete compartments afford multicompartment systems. Semi-analytical results demonstrate that feedback and feedforward loops can enhance system robustness, and simulation results probe the intractable problem of the first passage time distribution, which has specific relevance to eventual cell lysis in the viral replication cycle. Finally, we demonstrate that the smoothing seen in the process is a consequence of the discreteness of the system, and does not manifest in system with continuous transport. While we make progress through analysis of a simple linear problem, many of our insights are applicable more generally, and our work enables future analysis into multicompartment processes subject to stochastic inputs.
Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT
Authors: Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva, Anthony N Burkitt, Michael R Ibbotson
Date: 2023-05-16 00:00:00

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.
Decoding Imagined Auditory Pitch Phenomena with an Autoencoder Based Temporal Convolutional Architecture
Authors: Sean Paulsen, Lloyd May, Michael Casey
Date: 2023-05-15 00:00:00

Stimulus decoding of functional Magnetic Resonance Imaging (fMRI) data with machine learning models has provided new insights about neural representational spaces and task-related dynamics. However, the scarcity of labelled (task-related) fMRI data is a persistent obstacle, resulting in model-underfitting and poor generalization. In this work, we mitigated data poverty by extending a recent pattern-encoding strategy from the visual memory domain to our own domain of auditory pitch tasks, which to our knowledge had not been done. Specifically, extracting preliminary information about participants' neural activation dynamics from the unlabelled fMRI data resulted in improved downstream classifier performance when decoding heard and imagined pitch. Our results demonstrate the benefits of leveraging unlabelled fMRI data against data poverty for decoding pitch based tasks, and yields novel significant evidence for both separate and overlapping pathways of heard and imagined pitch processing, deepening our understanding of auditory cognitive neuroscience.
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals
Authors: Jarek Duda
Date: 2023-04-24 00:00:00
Tasks: Brain Computer Interface, Dimensionality Reduction, EEG, Feature Correlation

Electroencephalography (EEG) signals are resultants of extremely complex brain activity. Some details of this hidden dynamics might be accessible through e.g. joint distributions $\rho_{\Delta t}$ of signals of pairs of electrodes shifted by various time delays (lag $\Delta t$). A standard approach is monitoring a single evaluation of such joint distributions, like Pearson correlation (or mutual information), which turns out relatively uninteresting - as expected, there is usually a small peak for zero delay and nearly symmetric drop with delay. In contrast, such a complex signal might be composed of multiple types of statistical dependencies - this article proposes approach to automatically decompose and extract them. Specifically, we model such joint distributions as polynomials, estimated separately for all considered lag dependencies, then with PCA dimensionality reduction we find the dominant joint density distortion directions $f_v$. This way we get a few lag dependent features $a_i(\Delta t)$ describing separate dominating statistical dependencies of known contributions: $\rho_{\Delta t}(y,z)\approx \sum_{i=1}^r a_i(\Delta t)\, f_{v_i}(y,z)$. Such features complement Pearson correlation, extracting hidden more complex behavior, e.g. with asymmetry which might be related with direction of information transfer, extrema suggesting characteristic delays, or oscillatory behavior suggesting some periodicity. There is also discussed extension of Granger causality to such multi-feature joint density analysis, suggesting e.g. two separate causality waves. While this early article is initial fundamental research, in future it might help e.g. with understanding of cortex hidden dynamics, diagnosis of pathologies like epilepsy, determination of precise electrode position, or building brain-computer interface.
Towards the Artificial Brain: A Base Framework for Modelling Consciousness and Unconsciousness
Authors: Daniel Lopes
Date: 2023-04-18 00:00:00

One of the current AI issues depicted in popular culture is the fear of conscious super AIs that try to take control over humanity. And as computational power goes upwards and that turns more and more into a reality, understanding artificial brains might be increasingly important to control and drive AI towards the benefit of our societies. This paper proposes a base framework to aid the development of autonomous multipurpose artificial brains. To approach that, we propose to first model the functioning of the human brain by reflecting and taking inspiration from the way the body, the consciousness and the unconsciousness interact. To do that, we tried to model events such as sensing, thinking, dreaming and acting, thoughtfully or unconsciously. We believe valuable insights can already be drawn from the analysis and critique of the presented framework, and that it might be worth implementing it, with or without changes, to create, study, understand and control artificially conscious systems.
AIomics: exploring more of the proteome using mass spectral libraries extended by AI
Authors: Lewis Y. Geer, Joel Lapin, Douglas J. Slotta, Tytus D. Mak, Stephen E. Stein
Date: 2023-05-16 00:00:00
Tasks: Specificity

The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.
Finite Difference Solution Ansatz approach in Least-Squares Monte Carlo
Authors: Jiawei Huo
Date: 2023-05-16 00:00:00

This article presents a simple but effective and efficient approach to improve the accuracy and stability of Least-Squares Monte Carlo. The key idea is to construct the ansatz of conditional expected continuation payoff using the finite difference solution from one dimension, to be used in linear regression. This approach bridges between solving backward partial differential equations and Monte Carlo simulation, aiming at achieving the best of both worlds. In a general setting encompassing both local and stochastic volatility models, the ansatz is proven to act as a control variate, reducing the mean squared error, thereby leading to a reduction of the final pricing error. We illustrate the technique with realistic examples including Bermudan options, worst of issuer callable notes and expected positive exposure on European options under valuation adjustments.
Searching for the "Holy Grail" of sponsorship-linked marketing: A generalizable sponsorship ROI model
Authors: Jonathan A. Jensen
Date: 2023-04-11 00:00:00
Tasks: Marketing, Survival Analysis

Marketers routinely allocate a significant portion of their budget to sponsorship. However, isolating the return on investment from such efforts has remained a challenge. Thus, a dataset of more than 5,800 sponsorships is analyzed using survival analysis approaches that utilizes the sponsor's renewal of the sponsorship as a proxy for positive ROI. In addition to its contribution to the sponsorship-linked marketing literature, the resulting model can be utilized by managers to generate predicted values relative to the sponsor's probability of renewal and the ultimate duration of time the sponsor will remain with the property, representing a novel managerial contribution.
Equity Protection Swaps: A New Type of Investment Insurance for Holders of Superannuation Accounts
Authors: Huansang Xu, Ruyi Liu, Marek Rutkowski
Date: 2023-04-12 00:00:00

We propose to develop a new class of investment insurance products for holders of superannuation accounts in Australia, which we tentatively call equity protection swaps (EPSs). An EPS is a standalone financial derivative, which is reminiscent of a total return swap but also shares some features with the variable annuity known as the registered index-linked annuity (RILA). The buyer of an EPS obtains partial protection against losses on a reference portfolio and, in exchange, agrees to share portfolio gains with the insurance provider if the realized return on a reference portfolio is above a predetermined threshold. Formally, a generic EPS consists of protection and fee legs with participation rates agreed upon by the provider and holder. A general fair pricing formula for an EPS is obtained by considering a static hedging strategy based on traded European options. It is argued that to make the contract appealing to holders, the provider should select appropriate protection and fee rates that make the fair premium at the contract's inception equal to zero. A numerical study based on the Black-Scholes model and empirical tests based on market data for S\&P~500 and S&P/ASX~200 indices for 2020-2022 demonstrates the benefits of an EPS as an efficient investment insurance tool for superannuation accounts.