pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements
Authors: Weijia Yi, Nitin Jonathan Myers, Geethu Joseph
Date: 2023-10-11 00:00:00

This paper develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different. Consequently, standard sparsity-aware algorithms, which ignore phase errors, fail when channel measurements are acquired over multiple beam refinement protocol packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. Our method iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors. We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks.
Proactive Monitoring via Jamming in Fluid Antenna Systems
Authors: Junteng Yao, Tuo Wu, Xiazhi Lai, Ming Jin, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong
Date: 2023-10-11 00:00:00
Tasks: Position

This paper investigates the efficacy of utilizing fluid antenna system (FAS) at a legitimate monitor to oversee suspicious communication. The monitor switches the antenna position to minimize its outage probability for enhancing the monitoring performance. Our objective is to maximize the average monitoring rate, whose expression involves the integral of the first-order Marcum $Q$ function. The optimization problem, as initially posed, is non-convex owing to its objective function. Nevertheless, upon substituting with an upper bound, we provide a theoretical foundation confirming the existence of a unique optimal solution for the modified problem, achievable efficiently by the bisection search method. Furthermore, we also introduce a locally closed-form optimal resolution for maximizing the average monitoring rate. Empirical evaluations confirm that the proposed schemes outperform conventional benchmarks considerably.
Symbol-Level Precoding for Average SER Minimization in Multiuser MISO Systems
Authors: Yafei Wang, Hongwei Hou, Wenjin Wang, Xinping Yi
Date: 2023-10-11 00:00:00

This paper investigates symbol-level precoding (SLP) for high-order quadrature amplitude modulation (QAM) aimed at minimizing the average symbol error rate (SER), leveraging both constructive interference (CI) and noise power to gain superiority in full signal-to-noise ratio (SNR) ranges. We first construct the SER expression with respect to the transmitted signal and the rescaling factor, based on which the problem of average SER minimization subject to total transmit power constraint is further formulated. Given the non-convex nature of the objective, solving the above problem becomes challenging. Due to the differences in constraints between the transmit signal and the rescaling factor, we propose the double-space alternating optimization (DSAO) algorithm to optimize the two variables on orthogonal Stiefel manifold and Euclidean spaces, respectively. To facilitate QAM demodulation instead of affording impractical signaling overhead, we further develop a block transmission scheme to keep the rescaling factor constant within a block. Simulation results demonstrate that the proposed SLP scheme exhibits a significant performance advantage over existing state-of-the-art SLP schemes.
Epidemiological impacts of age structures on human malaria transmission
Authors: Quentin Richard, Marc Choisy, Ramsès Djidjou-Demasse, Thierry Lefèvre
Date: 2023-10-11 00:00:00

Malaria is one of the most common mosquito-borne diseases widespread in tropical and subtropical regions, causing thousands of deaths every year in the world. In a previous paper, we formulated an age-structured model containing three structural variables: (i) the chronological age of human and mosquito populations, (ii) the time since they are infected, and (iii) humans waning immunity (i.e. the progressive loss of protective antibodies after recovery). In the present paper, we expand the analysis of this age-structured model and focus on the derivation of entomological and epidemiological results commonly used in the literature, following the works of Smith and McKenzie. We generalize their results to the age-structured case. In order to quantify the impact of neglecting structuring variables such as chronological age, we assigned values from the literature to our model parameters. While some parameters values are readily accessible from the literature, at least those about the human population, the parameters concerning mosquitoes are less commonly documented and the values of a number of them (e.g. mosquito survival in the presence or in absence of infection) can be discussed extensively.
Score-Based Generative Models for Designing Binding Peptide Backbones
Authors: John D Boom, Matthew Greenig, Pietro Sormanni, Pietro Liò
Date: 2023-10-10 00:00:00
Tasks: Diversity, Protein Design

Score-based generative models (SGMs) have proven to be powerful tools for designing new proteins. Designing proteins that bind a pre-specified target is highly relevant to a range of medical and industrial applications. Despite the flurry of new SGMs in the last year, there has been little systematic exploration of the impact of design choices in SGMs for protein design. Here we present LoopGen, a flexible SGM framework for the design of short binding peptide structures. We apply our framework to design antibody binding loop structures conditional on a target epitope and evaluate a variety of modelling choices in SGM-based protein design. We demonstrate that modelling residue orientations in addition to positions improves not only the quality of the output structures but also their diversity. Additionally, we identify variance schedules that result in significant performance improvements and observe patterns that may motivate the development of better schedules for protein design. Finally, we develop three novel tests to evaluate whether the model generates structures that are appropriately conditioned on an epitope, demonstrating that LoopGen's generated structures are dependent on the structure, sequence, and position of the epitope. Our findings will help guide future development and evaluation of generative models for binding proteins.
Ordinal Characterization of Similarity Judgments
Authors: Jonathan D. Victor, Guillermo Aguilar, Suniyya A. Waraich
Date: 2023-10-11 00:00:00
Tasks: Empirical Judgments

Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive indices that characterize the set of judgments in three ways: compatibility with a symmetric dis-similarity, compatibility with an ultrametric space, and compatibility with an additive tree. Each of the indices is derived from rank-order relationships among the choice probabilities that, as we show, are necessary and sufficient for local consistency with the three respective characteristics. We illustrate this approach with simulations and example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses at https://github.com/jvlab/simrank.
Quantum Memory of Musical Compositions
Authors: Maria Mannone, Omar Costa Hamido
Date: 2023-10-09 00:00:00

The perception and appreciation of beauty in a musical composition appears as being related to the amount of memory and balance between expectation and surprise. In this study, we use a formal computing tool derived from quantum mechanics and adapted to music, to measure the degree of memory of orchestral music, including Liebestod by R. Wagner. We discuss our results and further lines of research.
Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning
Authors: Lukas AW Gemein, Robin T Schirrmeister, Joschka Boedecker, Tonio Ball
Date: 2023-09-22 00:00:00
Tasks: EEG

The brain's biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on longitudinal magnetic resonance imaging data have raised questions on its interpretation. A central question is whether an increased biological age of the brain is indicative of brain pathology and if changes in brain age correlate with diagnosed pathology (state hypothesis). Alternatively, could the discrepancy in brain age be a stable characteristic unique to each individual (trait hypothesis)? To address this question, we present a comprehensive study on brain aging based on clinical EEG, which is complementary to previous MRI-based investigations. We apply a state-of-the-art Temporal Convolutional Network (TCN) to the task of age regression. We train on recordings of the Temple University Hospital EEG Corpus (TUEG) explicitly labeled as non-pathological and evaluate on recordings of subjects with non-pathological as well as pathological recordings, both with examinations at a single point in time and repeated examinations over time. Therefore, we created four novel subsets of TUEG that include subjects with multiple recordings: I) all labeled non-pathological; II) all labeled pathological; III) at least one recording labeled non-pathological followed by at least one recording labeled pathological; IV) similar to III) but with opposing transition (first pathological then non-pathological). The results show that our TCN reaches state-of-the-art performance in age decoding with a mean absolute error of 6.6 years. Our extensive analyses demonstrate that the model significantly underestimates the age of non-pathological and pathological subjects (-1 and -5 years, paired t-test, p <= 0.18 and p <= 0.0066). Furthermore, the brain age gap biomarker is not indicative of pathological EEG.
Network-thinking to optimize surveillance and control of crop parasites. A review
Authors: Andrea Radici, Daniele Bevacqua, Leonardo Miele, Davide Martinetti
Date: 2023-10-11 00:00:00
Tasks: Descriptive

Increasing cultivated lands, crop homogenization and global food trade have fostered the spread of crop pests and diseases. Optimizing crop protection is urgently needed to ensure food safety. One aspect of crop protection is surveillance, which focuses on the early detection of a parasite, and control, aiming to fight and possibly eradicate it. Network theory has been widely used to model the spread of human and animal infectious diseases in systems described through nodes and edges. It has been successfully used to optimize monitoring and immunization campaigns. In crop protection, there is a growing literature using this theory to describe parasites spread and to conceive protection strategies. Here we review the use of network theory in crop protection, from the more descriptive to the more applied approaches aimed to optimize crop protection. We retrace the logical process that has led epidemiological models to rely on network theory, and we provide examples of how the spread of crop parasites has been represented via a network description. We define the objectives of surveillance and control, and we show how these have been declined in the network-based epidemiological sphere and then adapted in the agricultural context. We eventually discuss the discrepancy between the application of network theory in surveillance and control to identify culprits and solutions. We find that: i) scientists have successfully interpreted very different modes of parasitic transmission under the lens of network theory; ii) while network-based surveillance has progressively clarified its objectives and sound tools have been proposed, network-based control has been less studied and applied; iii) network-thinking must address how to properly define edges and nodes at different geographic scale to broad its application in crop protection.
Dynamic trait distribution as a source for shifts in interaction strength and population density
Authors: Zachary Jackson, BingKan Xue
Date: 2023-10-11 00:00:00

Intraspecific trait variation has been increasingly recognized as an important factor in determining species interaction and diversity. Eco-evolutionary models have studied the distribution of trait values within a population that changes over the generations as a result of selection and heritability. Non-heritable traits that can change within the lifetime, such as behavior, can cause trait-mediated indirect effects, often studied by modeling the dynamics of a homogeneous trait. Complementary to these two approaches, we study the distribution of traits within a population and its dynamics on short timescales due to ecological processes. We consider several mechanisms by which the trait distribution can shift dynamically: phenotypic plasticity within each individual, differential growth among individuals, and preferential consumption by the predator. Through a simple predator-prey model that explicitly tracks the trait distribution within the prey, we illustrate some unusual consequences of trait shifts, including the healthy herd effect and the emergent promotion of the prey by the predator. We analyze the cause of these phenomena by identifying the density and trait effects from the predator. Our results show that intraspecific trait variation and the dynamics of its distribution can lead to the modification of species interactions and result in outcomes that are otherwise unexpected.
Valuation Duration of the Stock Market
Authors: Ye Li, Chen Wang
Date: 2023-10-11 00:00:00

At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a valuation-based duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implying a duration of 46 years. What drives valuation duration? We find that market participants have limited information about cash flow beyond one year. Therefore, an increase in valuation duration is due to a decrease in the discount rate rather than good news about long-term growth. Accordingly, valuation duration negatively predicts annual market return with an out-of-sample R2 of 15%, robustly outperforming other predictors in the literature. While the price-dividend ratio reflects the overall valuation level, our valuation-based measure of duration captures the slope of the valuation term structure. We show that valuation duration, as a discount rate proxy, is a critical state variable that augments the price-dividend ratio in spanning the (latent) state space for stock-market dynamics.
Risk valuation of quanto derivatives on temperature and electricity
Authors: Aurélien Alfonsi, Nerea Vadillo
Date: 2023-10-10 00:00:00

This paper develops a coupled model for day-ahead electricity prices and average daily temperature which allows to model quanto weather and energy derivatives. These products have gained on popularity as they enable to hedge against both volumetric and price risks. Electricity day-ahead prices and average daily temperatures are modelled through non homogeneous Ornstein-Uhlenbeck processes driven by a Brownian motion and a Normal Inverse Gaussian L\'evy process, which allows to include dependence between them. A Conditional Least Square method is developed to estimate the different parameters of the model and used on real data. Then, explicit and semi-explicit formulas are obtained for derivatives including quanto options and compared with Monte Carlo simulations. Last, we develop explicit formulas to hedge statically single and double sided quanto options by a portfolio of electricity options and temperature options (CDD or HDD).
Exploiting Language Models as a Source of Knowledge for Cognitive Agents
Authors: James R. Kirk, Robert E. Wray, John E. Laird
Date: 2023-09-05 00:00:00
Tasks: Natural Language Inference, Question Answering, Sentence, Sentence Completion

Large language models (LLMs) provide capabilities far beyond sentence completion, including question answering, summarization, and natural-language inference. While many of these capabilities have potential application to cognitive systems, our research is exploiting language models as a source of task knowledge for cognitive agents, that is, agents realized via a cognitive architecture. We identify challenges and opportunities for using language models as an external knowledge source for cognitive systems and possible ways to improve the effectiveness of knowledge extraction by integrating extraction with cognitive architecture capabilities, highlighting with examples from our recent work in this area.
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling
Authors: Clay H. Yoo, Ashiqur R. KhudaBukhsh
Date: 2023-08-05 00:00:00
Tasks: Active Learning, Diversity, Misinformation

This paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and topical diversity of the negative class (dubbed anticontent) as observed in the wild. As a result, these classifiers' strong performance observed on the test set may not translate into real-world settings. In the context of COVID-19 misinformation detection, we conduct an in-the-wild audit of multiple datasets and demonstrate that models trained with several prominently cited recent datasets are vulnerable to anticontent when evaluated in the wild. Second, we present a novel active learning pipeline that requires zero manual annotation and iteratively augments the training data with challenging anticontent, robustifying these classifiers.
On sparse regression, Lp-regularization, and automated model discovery
Authors: Jeremy A. McCulloch, Skyler R. St. Pierre, Kevin Linka, Ellen Kuhl
Date: 2023-10-09 00:00:00
Tasks: L2 Regularization, Model Discovery, regression, Symbolic Regression

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem-specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias; only L0 regularization allows us to transparently fine-tune the trade-off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user-defined properties.
Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service
Authors: Jaewoo Park, Chenghao Quan, Hyungon Moon, Jongeun Lee
Date: 2023-08-17 00:00:00
Tasks: Privacy Preserving

Machine learning models are often provisioned as a cloud-based service where the clients send their data to the service provider to obtain the result. This setting is commonplace due to the high value of the models, but it requires the clients to forfeit the privacy that the query data may contain. Homomorphic encryption (HE) is a promising technique to address this adversity. With HE, the service provider can take encrypted data as a query and run the model without decrypting it. The result remains encrypted, and only the client can decrypt it. All these benefits come at the cost of computational cost because HE turns simple floating-point arithmetic into the computation between long (degree over 1024) polynomials. Previous work has proposed to tailor deep neural networks for efficient computation over encrypted data, but already high computational cost is again amplified by HE, hindering performance improvement. In this paper we show hyperdimensional computing can be a rescue for privacy-preserving machine learning over encrypted data. We find that the advantage of hyperdimensional computing in performance is amplified when working with HE. This observation led us to design HE-HDC, a machine-learning inference system that uses hyperdimensional computing with HE. We carefully structure the machine learning service so that the server will perform only the HE-friendly computation. Moreover, we adapt the computation and HE parameters to expedite computation while preserving accuracy and security. Our experimental result based on real measurements shows that HE-HDC outperforms existing systems by 26~3000 times with comparable classification accuracy.
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices
Authors: Akhil M R, Adithya Krishna V Sharma, Harivardhan Swamy, Pavan A, Ashray Shetty, Anirudh B Sathyanarayana
Date: 2023-08-21 00:00:00
Tasks: Edge-computing, Malware Classification, Malware Detection

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the feasibility of deploying these DNN models on edge devices to enable real-time classification, particularly in resource-constrained scenarios proves to be integral to large IoT systems. By optimizing model architectures and leveraging edge computing capabilities, the proposed methodologies achieve efficient performance even with limited resources. This study contributes to advancing malware detection techniques and emphasizes the significance of integrating cybersecurity measures for the early detection of malware and further preventing the adverse effects caused by such attacks. Optimal considerations regarding the distribution of security tasks to edge devices are addressed to ensure that the integrity and availability of large scale IoT systems are not compromised due to malware attacks, advocating for a more resilient and secure digital ecosystem.