pwc-archive/papers-with-abstracts huggingface dataset:
Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays
Authors: Tianyi Liu, Sai Pavan Deram, Khaled Ardah, Martin Haardt, Marc E. Pfetsch, Marius Pesavento
Date: 2024-06-23 00:00:00
Tasks: parameter estimation, subspace methods, Super-Resolution

Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. While subspace-based methods offer cost-effective super-resolution parameter estimation, they demand precise array calibration, posing challenges for large antennas. In contrast, sparsity-based approaches outperform subspace methods, especially in scenarios with limited snapshots or correlated sources. This study focuses on direction-of-arrival (DOA) estimation using a partly calibrated rectangular array with fully calibrated subarrays. A gridless sparse formulation leveraging shift invariances in the array is developed, yielding two competitive algorithms under the alternating direction method of multipliers (ADMM) and successive convex approximation frameworks, respectively. Numerical simulations show the superior error performance of our proposed method, particularly in highly correlated scenarios, compared to the conventional subspace-based methods. It is demonstrated that the proposed formulation can also be adopted in the fully calibrated case to improve the robustness of the subspace-based methods to the source correlation. Furthermore, we provide a generalization of the proposed method to a more challenging case where a part of the sensors is unobservable due to failures.
Enhanced Robustness in Wireless Communications through Unified Sequency-Frequency Multiplexing
Authors: Zahir Alsulaimawi
Date: 2024-06-21 00:00:00

In the evolving wireless communications landscape, addressing the challenges of multipath fading and high mobility remains paramount. This paper introduces the Unified Sequency-Frequency Multiplexing (USFM) framework, a pioneering modulation scheme designed to significantly improve signal robustness and system performance by harnessing the integrated strengths of both sequency and frequency domains. At the heart of USFM lies the Joint Sequency-Frequency Transform (JSFT), a novel mathematical operation that seamlessly merges the characteristics of the Walsh-Hadamard Transform (WHT) and the Fast Fourier Transform (FFT). Through rigorous mathematical modeling, we delineate the theoretical foundation of USFM, supported by theorems and lemmas that underscore its potential to mitigate common channel impairments more effectively than existing modulation schemes. Furthermore, we propose an optimization process, guided by machine learning algorithms, to dynamically adapt the signal based on real-time Channel State Information (CSI), ensuring optimal performance under diverse conditions. Empirical simulations demonstrate the superior performance of USFM in scenarios characterized by Rayleigh fading and Doppler effects, highlighting its advantages in terms of error probability reduction and spectral efficiency. The USFM framework represents a significant leap forward in communication theory and offers practical implications for designing future wireless systems that require high reliability and adaptability.
Efficient machine learning for motion sensing for lighting applications
Authors: Fetze Pijlman
Date: 2024-06-21 00:00:00

The use of machine learning for building a classifier in signal processing for motion sensing presents unique challenges. This paper proposes a novel method that effectively addresses the combination of skewed data sets and optimization requirements. By utilizing a customized loss function and a product of probability models, our approach achieves a fully automated and efficient machine learning process. Additionally, our resulting probability models offer reduced complexity, making them ideal for embedded applications. Our method offers a promising solution for motion sensing applications that require accurate and efficient classification.
Generalized Measures of Population Synchrony
Authors: Francis C. Motta, Kevin McGoff, Breschine Cummins, Steven B. Haase
Date: 2024-06-23 00:00:00

Synchronized behavior among individuals is a ubiquitous feature of populations. Understanding mechanisms of (de)synchronization demands meaningful, interpretable, computable quantifications of synchrony, relevant to measurements that can be made of dynamic populations. Despite the importance to analyzing and modeling populations, existing notions of synchrony often lack rigorous definitions, may be specialized to a particular experimental system and/or measurement, or may have undesirable properties that limit their utility. We introduce a notion of synchrony for populations of individuals occupying a compact metric space that depends on the Fr\'{e}chet variance of the distribution of individuals. We establish several fundamental and desirable mathematical properties of this synchrony measure, including continuity and invariance to metric scaling. We establish a general approximation result that controls the disparity between synchrony in the true space and the synchrony observed through a discretization of state space, as may occur when observable states are limited by measurement constraints. We develop efficient algorithms to compute synchrony in a variety of state spaces, including all finite state spaces and empirical distributions on the circle, and provide accessible implementations in an open-source Python module. To demonstrate the usefulness of the synchrony measure in biological applications, we investigate several biologically relevant models of mechanisms that can alter the dynamics of synchrony over time, and reanalyze published data concerning the dynamics of the intraerythrocytic developmental cycles of $\textit{Plasmodium}$ parasites. We anticipate that the rigorous definition of population synchrony and the mathematical and biological results presented here will be broadly useful in analyzing and modeling populations in a variety of contexts.
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
Authors: Jason Yang, Ariane Mora, Shengchao Liu, Bruce J. Wittmann, Anima Anandkumar, Frances H. Arnold, Yisong Yue
Date: 2024-06-21 00:00:00
Tasks: Classification, Out-of-Distribution Generalization, Retrieval

Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task and compare it to the recent method, CLIPZyme. CARE is available at https://github.com/jsunn-y/CARE/.
A compact model of Escherichia coli core and biosynthetic metabolism
Authors: Marco Corrao, Hai He, Wolfram Liebermeister, Elad Noor, Arren Bar-Even
Date: 2024-06-24 00:00:00

Metabolic models condense biochemical knowledge about organisms in a structured and standardised way. As large-scale network reconstructions are readily available for many organisms, genome-scale models are being widely used among modellers and engineers. However, these large models can be difficult to analyse and visualise and occasionally generate predictions that are hard to interpret or even biologically unrealistic. Of the thousands of enzymatic reactions in a typical bacterial metabolism, only a few hundred form the metabolic pathways essential to produce energy carriers and biosynthetic precursors. These pathways carry relatively high flux, are central to maintaining and reproducing the cell, and provide precursors and energy to engineered metabolic pathways. Focusing on these central metabolic subsystems, we present iCH360, a manually curated medium-scale model of energy and biosynthesis metabolism for the well-studied bacterium Escherichia coli K-12 MG1655. The model is a sub-network of the most recent genome-scale reconstruction, iML1515, and comes with an updated layer of database annotations and with a range of metabolic maps for visualisation. We enriched the stoichiometric network with extensive biological information and quantitative data, enhancing the scope and applicability of the model. In addition, we assess the properties of this model in comparison to its genome-scale parent and demonstrate the use of the network and supporting data in various scenarios, including enzyme-constrained flux balance analysis, elementary flux mode analysis, and thermodynamic analysis. Overall, we believe this model holds the potential to become a reference medium-scale metabolic model for E. coli.
Spatial Contrast: A Vintage Retinal Model of Color Vision Produces the Center-Surround Opponent Color Phenomena; The Model Was Previously Shown To Produce Mutually Exclusive Opponent Colors by a Different Mechanism
Authors: Lane Yoder
Date: 2024-06-24 00:00:00

The on-off phenomena of opponent colors in center-surround may be the best-known facts of retinal processing of information. Apparently, however, no explicit model has been proposed that shows how neurons can be connected to produce the center-surround phenomena. Here it is shown that a previous simple model of color vision can produce these phenomena, including the detection of edge orientation and motion famously discovered by Hubel and Wiesel. The model was previously shown to produce major phenomena central to color vision, including mutually exclusive opponent colors. Although the opponencies of mutually exclusive colors and center-surround involve the same color pairs, red-green and blue-yellow, the model produces them by two different mechanisms. Perceptions of two colors are mutually exclusive because only one cone type can have the most, or least, absorption of photons. Two colors have the on-off opponency of center-surround because they have the same network designs up to the ganglion cells, with the inputs reversed there. On-off opponencies with different colors are possible, but natural selection evidently did not choose them.
Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden
Authors: István Mórocz, Mojtaba Jouzizadeh, Amir H. Ghaderi, Hamed Cheraghmakani, Seyed M. Baghbanian, Reza Khanbabaie, Andrei Mogoutov
Date: 2024-06-21 00:00:00
Tasks: Diagnostic, EEG

Background: Any treatment of multiple sclerosis should preserve mental function, considering how cognitive deterioration interferes with quality of life. However, mental assessment is still realized with neuro-psychological tests without monitoring cognition on neurobiological grounds whereas the ongoing neural activity is readily observable and readable. Objectives: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms quantitatively discriminate normal from pathological patterns in an EEG. Methods: Baseline recordings from a prior EEG study of 93 subjects, 37 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets. Results: A significant correlation arose between brain states predictors, clinical data and disease duration. Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 82% while using bands alone arrived at 64%. Conclusions: Brain states analysis successfully distinguishes controls from patients with MS. The congruity with disease duration is a neurobiological indicator for disease accumulation over time. Our results imply that data-driven comparisons of EEG data may complement customary diagnostic methods in neurology and psychiatry. However, thinking ahead for quantitative monitoring of disease time course and treatment efficacy, we hope to have established the analytic principles applicable to longitudinal clinical studies.
Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
Authors: Yong Xie
Date: 2024-06-18 00:00:00

Achieving General Artificial Intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-inspired AI framework, Orangutan. It simulates the structure and computational mechanisms of biological brains on multiple scales, encompassing multi-compartment neuron architectures, diverse synaptic connection modalities, neural microcircuits, cortical columns, and brain regions, as well as biochemical processes including facilitation, feedforward inhibition, short-term potentiation, and short-term depression, all grounded in solid neuroscience. Building upon these highly integrated brain-like mechanisms, I have developed a sensorimotor model that simulates human saccadic eye movements during object observation. The model's algorithmic efficacy was validated through testing with the observation of handwritten digit images.
The unpaved road towards efficient selective breeding in insects for food and feed
Authors: Laura Skrubbeltrang Hansen, Stine Frey Laursen, Simon Bahrndorff, Jesper Givskov Sørensen, Goutam Sahana, Torsten Nygaard Kristensen, Hanne Marie Nielsen
Date: 2024-06-24 00:00:00
Tasks: Diversity

Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future. However, optimisation is required for insect production to realise its full potential. This can be by targeted improvement of traits of interest through selective breeding, an approach which has so far been underexplored and underutilised in insect farming. Here we present a comprehensive review of the selective breeding framework in the context of insect production. We systematically evaluate adjustments of selective breeding techniques to the realm of insects and highlight the essential components integral to the breeding process. The discussion covers every step of a conventional breeding scheme, such as formulation of breeding objectives, phenotyping, estimation of genetic parameters and breeding values, selection of appropriate breeding strategies, and mitigation of issues associated with genetic diversity depletion and inbreeding. This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology, offering an integrated view of the insect breeding research area and uniting knowledge which has previously remained scattered across diverse fields of expertise.
Profit Maximization In Arbitrage Loops
Authors: Yu Zhang, Zichen Li, Tao Yan, Qianyu Liu, Nicolo Vallarano, Claudio Tessone
Date: 2024-06-24 00:00:00

Cyclic arbitrage chances exist abundantly among decentralized exchanges (DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or practitioners usually choose a specific token, such as Ether as input, and optimize their input amount to get the net maximal amount of the specific token as arbitrage profit. By considering the tokens' prices from CEXs in this paper, the new arbitrage profit, called monetized arbitrage profit, will be quantified as the product of the net number of a specific token we got from the arbitrage loop and its corresponding price in CEXs. Based on this concept, we put forward three different strategies to maximize the monetized arbitrage profit for each arbitrage loop. The first strategy is called the MaxPrice strategy. Under this strategy, arbitrageurs start arbitrage only from the token with the highest CEX price. The second strategy is called the MaxMax strategy. Under this strategy, we calculate the monetized arbitrage profit for each token as input in turn in the arbitrage loop. Then, we pick up the most maximal monetized arbitrage profit among them as the monetized arbitrage profit of the MaxMax strategy. The third one is called the Convex Optimization strategy. By mapping the MaxMax strategy to a convex optimization problem, we proved that the Convex Optimization strategy could get more profit in theory than the MaxMax strategy, which is proved again in a given example. We also proved that if no arbitrage profit exists according to the MaxMax strategy, then the Convex Optimization strategy can not detect any arbitrage profit, either. However, the empirical data analysis denotes that the profitability of the Convex Optimization strategy is almost equal to that of the MaxMax strategy, and the MaxPrice strategy is not reliable in getting the maximal monetized arbitrage profit compared to the MaxMax strategy.
An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges
Authors: Yu Zhang, Tao Yan, Jianhong Lin, Benjamin Kraner, Claudio Tessone
Date: 2024-06-24 00:00:00

In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in the form of both arbitrage loops (e.g. the arbitrage path starts from token A and back to token A again in the end, A, B,..., A) and non-loops (e.g. the arbitrage path starts from token A and stops at a different token N, A, B,..., N). The Moore-Bellman-Ford algorithm, often coupled with the ``walk to the root" technique, is commonly employed for detecting arbitrage loops in the token graph of decentralized exchanges (DEXs) such as Uniswap. However, a limitation of this algorithm is its ability to recognize only a limited number of arbitrage loops in each run. Additionally, it cannot specify the starting token of the detected arbitrage loops, further constraining its effectiveness in certain scenarios. Another limitation of this algorithm is its incapacity to detect non-loop arbitrage paths between any specified pairs of tokens. In this paper, we develop a new method to solve these problems by combining the line graph and a modified Moore-Bellman-Ford algorithm (MMBF). This method can help to find more arbitrage loops by detecting at least one arbitrage loop starting from any specified tokens in the DEXs and can detect the non-loop arbitrage paths between any pair of tokens. Then, we applied our algorithm to Uniswap V2 and found more arbitrage loops and non-loops indeed compared with applying the Moore-Bellman-Ford (MBF) combined algorithm. The found arbitrage profit by our method in some arbitrage paths can be even as high as one million dollars, far larger than that found by the MBF combined algorithm. Finally, we statistically compare the distribution of arbitrage path lengths and the arbitrage profit detected by both our method and the MBF combined algorithm, and depict how potential arbitrage opportunities change with time by our method.
Computing the SSR
Authors: Peter K. Friz, Jim Gatheral
Date: 2024-06-23 00:00:00

The skew-stickiness-ratio (SSR), examined in detail by Bergomi in his book, is critically important to options traders, especially market makers. We present a model-free expression for the SSR in terms of the characteristic function. In the diffusion setting, it is well-known that the short-term limit of the SSR is 2; a corollary of our results is that this limit is $H+3/2$ where $H$ is the Hurst exponent of the volatility process. The general formula for the SSR simplifies and becomes particularly tractable in the affine forward variance case. We explain the qualitative behavior of the SSR with respect to the shape of the forward variance curve, and thus also path-dependence of the SSR.
Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing
Authors: Jinniao Qiu, Antony Ware, Yang Yang
Date: 2024-06-24 00:00:00
Tasks: Deep Learning

This paper is devoted to the price-storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path-dependence in both price volatility and storage increments. Model calibrations are conducted for both the price and storage dynamics. Further, we discuss the pricing problem of discrete-time swing options using the dynamic programming principle, and a deep learning-based method is proposed for numerical approximations. A numerical algorithm is provided, followed by a convergence analysis result for the deep-learning approach.
Hedging in Sequential Experiments
Authors: Thomas Cook, Patrick Flaherty
Date: 2024-06-22 00:00:00

Experimentation involves risk. The investigator expends time and money in the pursuit of data that supports a hypothesis. In the end, the investigator may find that all of these costs were for naught and the data fail to reject the null. Furthermore, the investigator may not be able to test other hypotheses with the same data set in order to avoid false positives due to p-hacking. Therefore, there is a need for a mechanism for investigators to hedge the risk of financial and statistical bankruptcy in the business of experimentation. In this work, we build on the game-theoretic statistics framework to enable an investigator to hedge their bets against the null hypothesis and thus avoid ruin. First, we describe a method by which the investigator's test martingale wealth process can be capitalized by solving for the risk-neutral price. Then, we show that a portfolio that comprises the risky test martingale and a risk-free process is still a test martingale which enables the investigator to select a particular risk-return position using Markowitz portfolio theory. Finally, we show that a function that is derivative of the test martingale process can be constructed and used as a hedging instrument by the investigator or as a speculative instrument by a risk-seeking investor who wants to participate in the potential returns of the uncertain experiment wealth process. Together, these instruments enable an investigator to hedge the risk of ruin and they enable a investigator to efficiently hedge experimental risk.
Automatic parking planning control method based on improved A* algorithm
Authors: Yuxuan Zhao
Date: 2024-05-24 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

As the trend of moving away from high-precision maps gradually emerges in the autonomous driving industry,traditional planning algorithms are gradually exposing some problems. To address the high real-time, high precision, and high trajectory quality requirements posed by the automatic parking task under real-time perceived local maps,this paper proposes an improved automatic parking planning algorithm based on the A* algorithm, and uses Model Predictive Control (MPC) as the control module for automatic parking.The algorithm enhances the planning real-time performance by optimizing heuristic functions, binary heap optimization, and bidirectional search; it calculates the passability of narrow areas by dynamically loading obstacles and introduces the vehicle's own volume during planning; it improves trajectory quality by using neighborhood expansion and Bezier curve optimization methods to meet the high trajectory quality requirements of the parking task. After obtaining the output results of the planning algorithm, a loss function is designed according to the characteristics of the automatic parking task under local maps, and the MPC algorithm is used to output control commands to drive the car along the planned trajectory. This paper uses the perception results of real driving environments converted into maps as planning inputs to conduct simulation tests and ablation experiments on the algorithm. Experimental results show that the improved algorithm proposed in this paper can effectively meet the special requirements of automatic parking under local maps and complete the automatic parking planning and control tasks.