pwc-archive/papers-with-abstracts huggingface dataset:
sLORETA is equivalent to single dipole scanning
Authors: Malte B. Höltershinken, Tim Erdbrügger, Carsten H. Wolters
Date: 2024-08-25 00:00:00

In this paper, we show that each member of a family of reconstruction approaches, which includes the classical sLORETA approach and the eLORETA approach, is exactly equivalent to a single dipole scan in some inner product norm. Additionally, we investigate NAI/AG and SAM/UNG beamformers and show that, in scenarios with a single active source with additive uncorrelated noise, they are also equivalent to some form of dipole scanning. As a particular consequence, this shows that, under ideal conditions in a noisy single source case, NAI and SAM (resp. AG and UNG) beamformers are equivalent. Finally, we also discuss how to generalize scalar beamformers to vector beamformers.
Improving occupant comfort through real-time Predictive control of Indoor environment using Predicted-Mean Vote model and MLP
Authors: Madhan Kumar S, Yaswanth Kannan G, Kavin Krishna K, Berlin Hency V
Date: 2024-08-24 00:00:00

The Predicted Mean Vote (PMV) index is a widely accepted method in the building automation sector because it can precisely estimate indoor thermal comfort levels depending on a variety of environmental parameters. This study suggests an experimental setup for automated real-time optimization of heating, ventilation and air-conditioning (HVAC) operations in closed spaces utilizing PMV-based modelling and Multilayer perceptron (MLP) based prediction, with an experimental setup which includes ESP32 and BME280 sensor. The main objective of this paper is to employ the MLP algorithm and predicted mean vote model to dynamically predict comfort differences considering the fluctuations in environmental conditions such as temperature and humidity. The proposed method is implemented across various settings, encompassing both an anechoic chamber and laboratory environments. The findings indicate that the proposed approach effectively enhances the management of HVAC systems in confined areas, leading to higher energy efficiency and enhanced indoor thermal comfort. The effectiveness of the suggested solution is supported by experimental validation, which shows that significant energy savings are achieved while maintaining comfort levels for occupants within.
Metamaterial-Inspired Quad-Band Notch Filter for LTE Band Receivers and WPT Applications
Authors: R. Keshavarz, Y. Miyanaga, M. Yamamoto, T. Hikage, N. Shariati
Date: 2024-08-24 00:00:00

A new compact quad-band notch filter (QBNF) based on the extended composite right and left-handed transmission line (E-CRLH TL) has been presented. As known, E-CRLH TL behaves like a quad-band structure. A microstrip TL which is loaded with an open-ended E-CRLH TL is presented as a QBNF. Four unwanted frequencies were used in a dual-band LTE receiver as four notch frequencies which must be eliminated (0.9 GHz, 1.3 GHz, 2.55 GHz, and 3.35 GHz). Also, this QBNF can be applied to simultaneous wireless power and data transfer (SWPDT) system to isolate the wireless power circuit from the data communication circuit. A design technique for the proposed QBNF is presented and its performance is validated using full-wave simulation results and theoretical analysis. The main advantage of this design is an overall rejection greater than 20dB at selected unwanted frequencies. Good agreements between the full-wave simulation and equivalent circuit model results have been achieved which verified the effectiveness of the proposed circuit model. The proposed QBNF is designed on an FR-4 substrate and the dimension of the proposed QBNF is 20X22 mm.
Optical Semantic Communication through Multimode Fiber: From Symbol Transmission to Sentiment Analysis
Authors: Zheng Gao, Ting Jiang, Mingming Zhang, Hao Wu, Ming Tang
Date: 2024-08-23 00:00:00
Tasks: Semantic Communication, Sentiment Analysis

We propose and validate a novel optical semantic transmission scheme using multimode fiber (MMF). By leveraging the frequency sensitivity of intermodal dispersion in MMFs, we achieve high-dimensional semantic encoding and decoding in the frequency domain. Our system maps symbols to 128 distinct frequencies spaced at 600 kHz intervals, demonstrating a seven-fold increase in capacity compared to conventional communication encoding. We further enhance spectral efficiency by implementing 4-level pulse amplitude modulation (PAM-4), achieving 9.12 bits/s/Hz without decoding errors. Additionally, we explore the application of this system for sentiment analysis using the IMDb movie review dataset. By encoding semantically similar symbols to adjacent frequencies, the system's noise tolerance is effectively improved, facilitating accurate sentiment analysis. This work highlights the potential of MMF-based semantic communication to enhance both capacity and robustness in optical communication systems, offering promising applications in bandwidth-constrained and noisy environments.
HiFAKES: High-frequency synthetic appliance signatures generator for non-intrusive load monitoring
Authors: Ilia Kamyshev, Sahar Moghimian, Henni Ouerdane
Date: 2024-08-22 00:00:00
Tasks: Diversity, Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) rely on data-driven methods and require extensive datasets of power consumption over a long period of time. However, the scarcity of datasets negatively impacts the performance of current NILM algorithms. Further, while the vast majority of these datasets are made by collecting low-frequency sampling power consumption patterns, high-frequency datasets, which would significantly improve the accuracy of load identification, are comparatively few due to the time and costs that such data collection entail. As a response to these problems, synthetic datasets have been developed. Existing synthetic datasets for NILM, while useful, typically depend on large training datasets and are evaluated using a single aggregated test metric, which cannot precisely reflect the fidelity, diversity, and novelty of the data. To address this gap, we introduce HiFAKES, a novel physics-informed, training-free generative model designed to produce high-frequency sampling synthetic power signatures. HiFAKES generates synthetic data with superior fidelity and diversity. Indeed, the $\alpha$-precision and $\beta$-recall indicators are respectively about 2.5 times larger than those for existing models, this makes our proposed model highly effective for NILM applications. HiFAKES operates by generating reference signatures templates based on real appliance signatures data, and by replicating these with some features variations, thus simulating both known and novel appliances. A key advantage of this model is its ability to generate unlimited high-frequency synthetic appliance data from a single training example. The generated data closely mimics real-world power consumption, as demonstrated by comprehensive evaluations, including the so-called 3D metric ($\alpha$-precision, $\beta$-recall, authenticity) and domain classifier tests.
Some bounds on positive equilibria in mass action networks
Authors: Murad Banaji
Date: 2024-09-10 00:00:00

We present some results helpful for parameterising positive equilibria, and bounding the number of positive nondegenerate equilibria, in mass action networks. Any mass action network naturally gives rise to a set of polynomial equations whose positive solutions are precisely the positive equilibria of the network. Here we derive alternative systems of equations, often also polynomial, whose solutions are in smooth, one-to-one correspondence with positive equilibria of the network. Often these alternative systems are simpler than the original mass action equations, and allow us to infer useful bounds on the number of positive equilibria. The alternative equation systems can also be helpful for parameterising the equilibrium set explicitly, for deriving descriptions of the parameter regions for multistationarity, and for studying bifurcations. We present the main construction, some bounds which follow for particular classes of networks, numerous examples, and some open questions and conjectures.
Risk measures on incomplete markets: a new non-solid paradigm
Authors: Vasily Melnikov
Date: 2024-09-08 00:00:00

We study risk measures $\varphi:E\longrightarrow\mathbb{R}\cup\{\infty\}$, where $E$ is a vector space of random variables which a priori has no lattice structure$\unicode{x2014}$a blind spot of the existing risk measures literature. In particular, we address when $\varphi$ admits a tractable dual representation (one which does not contain non-$\sigma$-additive signed measures), and whether one can extend $\varphi$ to a solid superspace of $E$. The existence of a tractable dual representation is shown to be equivalent, modulo certain technicalities, to a Fatou-like property, while extension theorems are established under the existence of a sufficiently regular lift, a potentially non-linear mechanism of assigning random variable extensions to certain linear functionals on $E$. Our motivation is broadening the theory of risk measures to spaces without a lattice structure, which are ubiquitous in financial economics, especially when markets are incomplete.
A deep primal-dual BSDE method for optimal stopping problems
Authors: Jiefei Yang, Guanglian Li
Date: 2024-09-11 00:00:00

We present a new deep primal-dual backward stochastic differential equation framework based on stopping time iteration to solve optimal stopping problems. A novel loss function is proposed to learn the conditional expectation, which consists of subnetwork parameterization of a continuation value and spatial gradients from present up to the stopping time. Notable features of the method include: (i) The martingale part in the loss function reduces the variance of stochastic gradients, which facilitates the training of the neural networks as well as alleviates the error propagation of value function approximation; (ii) this martingale approximates the martingale in the Doob-Meyer decomposition, and thus leads to a true upper bound for the optimal value in a non-nested Monte Carlo way. We test the proposed method in American option pricing problems, where the spatial gradient network yields the hedging ratio directly.
Pattern based learning and optimisation through pricing for bin packing problem
Authors: Huayan Zhang, Ruibin Bai, Tie-Yan Liu, Jiawei Li, Bingchen Lin, Jianfeng Ren
Date: 2024-08-27 00:00:00

As a popular form of knowledge and experience, patterns and their identification have been critical tasks in most data mining applications. However, as far as we are aware, no study has systematically examined the dynamics of pattern values and their reuse under varying conditions. We argue that when problem conditions such as the distributions of random variables change, the patterns that performed well in previous circumstances may become less effective and adoption of these patterns would result in sub-optimal solutions. In response, we make a connection between data mining and the duality theory in operations research and propose a novel scheme to efficiently identify patterns and dynamically quantify their values for each specific condition. Our method quantifies the value of patterns based on their ability to satisfy stochastic constraints and their effects on the objective value, allowing high-quality patterns and their combinations to be detected. We use the online bin packing problem to evaluate the effectiveness of the proposed scheme and illustrate the online packing procedure with the guidance of patterns that address the inherent uncertainty of the problem. Results show that the proposed algorithm significantly outperforms the state-of-the-art methods. We also analysed in detail the distinctive features of the proposed methods that lead to performance improvement and the special cases where our method can be further improved.
Valuation Model of Chinese Convertible Bonds Based on Monte Carlo Simulation
Authors: Yu Liu
Date: 2024-09-10 00:00:00

We tackle the problem of pricing Chinese convertible bonds(CCBs) using Monte Carlo simulation and dynamic programming. At each exercise time, we use the state variables of the underlying stock to regress the continuation value, and apply standard backward induction to get the coefficients from the current time to time zero. This process ultimately determines the CCB price. We then apply this pricing method in simulations and evaluate an underpriced strategy: taking long positions in the 10 most undervalued CCBs and rebalancing daily. The results show that this strategy significantly outperforms the benchmark double-low strategy. In practice, CCB issuers often use a downward adjustment clause to prevent financial distress when a put provision is triggered. Therefore, we model the downward adjustment clause as a probabilistic event that triggers the put provision, thereby integrating it with the put provision in a straightforward manner.
Non-explosivity of endotactic stochastic reaction systems
Authors: Chuang Xu
Date: 2024-09-09 00:00:00
Tasks: Epidemiology

Reaction networks have been widely used as generic models in diverse areas of applied science, such as biology, chemistry, ecology, epidemiology, and computer science. Reaction networks incorporating noisy effect are modelled as continuous time Markov chains (CTMC), and are called stochastic reaction systems. Non-explosivity is a concept that characterizes regularity of CTMCs. In this paper, we study non-explosivity of stochastic reaction systems, in the sense of their underlying CTMCs. By constructing a simple linear Lyapunov function, we obtain non-explosivity for a class of endotactic stochastic reaction systems containing second-order endotactic stochastic mass-action systems as a subset. As a consequence, we prove that every bimolecular weakly reversible stochastic mass-action system is non-explosive. We apply our results to diverse models in biochemistry, epidemiology, ecology, and synthetic biology in the literature.
The Stochastic Gause predator-prey model: noise-induced extinctions and invariance
Authors: Leon Alexander Valencia, Ph. D, Jorge Mario Ramirez Osorio, Jorge Andres Sanchez
Date: 2024-09-08 00:00:00

This paper explores a stochastic Gause predator-prey model with bounded or sub-linear functional response. The model, described by a system of stochastic differential equations, captures the influence of stochastic fluctuations on predator-prey dynamics, with particular focus on the stability, extinction, and persistence of populations. We provide sufficient conditions for the existence and boundedness of solutions, analyze noise-induced extinction events, and investigate the existence of unique stationary distributions for the case of Holing Type I functional response. Our analysis highlights the critical role of noise in determining long-term ecological outcomes, demonstrating that even in cases where deterministic models predict stable coexistence, stochastic noise can drive populations to extinction or alter the system's dynamics significantly.
Exact first passage time distribution for second-order reactions in chemical networks
Authors: Changqian Rao, David Waxman, Wei Lin, Zhuoyi Song
Date: 2024-09-04 00:00:00
Tasks: Computational Efficiency

The first passage time (FPT) is a generic measure that quantifies when a random quantity reaches a specific state. We consider the FTP distribution in nonlinear stochastic biochemical networks, where obtaining exact solutions of the distribution is a challenging problem. Even simple two-particle collisions cause strong nonlinearities that hinder the theoretical determination of the full FPT distribution. Previous research has either focused on analyzing the mean FPT, which provides limited information about a system, or has considered time-consuming stochastic simulations that do not clearly expose causal relationships between parameters and the system's dynamics. This paper presents the first exact theoretical solution of the full FPT distribution in a broad class of chemical reaction networks involving $A + B \rightarrow C$ type of second-order reactions. Our exact theoretical method outperforms stochastic simulations, in terms of computational efficiency, and deviates from approximate analytical solutions. Given the prevalence of bimolecular reactions in biochemical systems, our approach has the potential to enhance the understanding of real-world biochemical processes.
American option pricing using generalised stochastic hybrid systems
Authors: Evelyn Buckwar, Sascha Desmettre, Agnes Mallinger, Amira Meddah
Date: 2024-08-29 00:00:00
Tasks: Computational Efficiency

This paper presents a novel approach to pricing American options using piecewise diffusion Markov processes (PDifMPs), a type of generalised stochastic hybrid system that integrates continuous dynamics with discrete jump processes. Standard models often rely on constant drift and volatility assumptions, which limits their ability to accurately capture the complex and erratic nature of financial markets. By incorporating PDifMPs, our method accounts for sudden market fluctuations, providing a more realistic model of asset price dynamics. We benchmark our approach with the Longstaff-Schwartz algorithm, both in its original form and modified to include PDifMP asset price trajectories. Numerical simulations demonstrate that our PDifMP-based method not only provides a more accurate reflection of market behaviour but also offers practical advantages in terms of computational efficiency. The results suggest that PDifMPs can significantly improve the predictive accuracy of American options pricing by more closely aligning with the stochastic volatility and jumps observed in real financial markets.
Risk-indifference Pricing of American-style Contingent Claims
Authors: Rohini Kumar, Frederick "Forrest" Miller, Hussein Nasralah, Stephan Sturm
Date: 2024-08-26 00:00:00

This paper studies the pricing of contingent claims of American style, using indifference pricing by fully dynamic convex risk measures. We provide a general definition of risk-indifference prices for buyers and sellers in continuous time, in a setting where buyer and seller have potentially different information, and show that these definitions are consistent with no-arbitrage principles. Specifying to stochastic volatility models, we characterize indifference prices via solutions of Backward Stochastic Differential Equations reflected at Backward Stochastic Differential Equations and show that this characterization provides a basis for the implementation of numerical methods using deep learning.
De novo design of high-affinity protein binders with AlphaProteo
Authors: Vinicius Zambaldi, David La, Alexander E. Chu, Harshnira Patani, Amy E. Danson, Tristan O. C. Kwan, Thomas Frerix, Rosalia G. Schneider, David Saxton, Ashok Thillaisundaram, Zachary Wu, Isabel Moraes, Oskar Lange, Eliseo Papa, Gabriella Stanton, Victor Martin, Sukhdeep Singh, Lai H. Wong, Russ Bates, Simon A. Kohl, Josh Abramson, Andrew W. Senior, Yilmaz Alguel, Mary Y. Wu, Irene M. Aspalter, Katie Bentley, David L. V. Bauer, Peter Cherepanov, Demis Hassabis, Pushmeet Kohli, Rob Fergus, Jue Wang
Date: 2024-09-12 00:00:00
Tasks: Protein Design

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.
Multi-variable control to mitigate loads in CRISPRa networks: Extended Version
Authors: Krishna Manoj, Theodore W. Grunberg, Domitilla Del Vecchio
Date: 2024-09-11 00:00:00

The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications.
AbGPT: De Novo Antibody Design via Generative Language Modeling
Authors: Desmond Kuan, Amir Barati Farimani
Date: 2024-09-09 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Specificity

The adaptive immune response, largely mediated by B-cell receptors (BCRs), plays a crucial role for effective pathogen neutralization due to its diversity and antigen specificity. Designing BCRs de novo, or from scratch, has been challenging because of their complex structure and diverse binding requirements. Protein language models (PLMs) have shown remarkable performance in contextualizing and performing various downstream tasks without relying on structural information. However, these models often lack a comprehensive understanding of the entire protein space, which limits their application in antibody design. In this study, we introduce Antibody Generative Pretrained Transformer (AbGPT), a model fine-tuned from a foundational PLM to enable a more informed design of BCR sequences. Using a custom generation and filtering pipeline, AbGPT successfully generated a high-quality library of 15,000 BCR sequences, demonstrating a strong understanding of the intrinsic variability and conserved regions within the antibody repertoire.
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
Authors: Gengmo Zhou, Zhen Wang, Feng Yu, Guolin Ke, Zhewei Wei, Zhifeng Gao
Date: 2024-08-27 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Drug Discovery, Inductive Bias

Virtual Screening is an essential technique in the early phases of drug discovery, aimed at identifying promising drug candidates from vast molecular libraries. Recently, ligand-based virtual screening has garnered significant attention due to its efficacy in conducting extensive database screenings without relying on specific protein-binding site information. Obtaining binding affinity data for complexes is highly expensive, resulting in a limited amount of available data that covers a relatively small chemical space. Moreover, these datasets contain a significant amount of inconsistent noise. It is challenging to identify an inductive bias that consistently maintains the integrity of molecular activity during data augmentation. To tackle these challenges, we propose S-MolSearch, the first framework to our knowledge, that leverages molecular 3D information and affinity information in semi-supervised contrastive learning for ligand-based virtual screening. Drawing on the principles of inverse optimal transport, S-MolSearch efficiently processes both labeled and unlabeled data, training molecular structural encoders while generating soft labels for the unlabeled data. This design allows S-MolSearch to adaptively utilize unlabeled data within the learning process. Empirically, S-MolSearch demonstrates superior performance on widely-used benchmarks LIT-PCBA and DUD-E. It surpasses both structure-based and ligand-based virtual screening methods for AUROC, BEDROC and EF.