pwc-archive/papers-with-abstracts huggingface dataset:
When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications
Authors: Jun Wu, Weijie Yuan, Lajos Hanzo
Date: 2023-06-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized. In particular, the UAV serves both as a downlink transmitter and a radar receiver. The legitimate user (Bob) roams on ground through a series of unknown locations, while the eavesdropper moves following a fixed known trajectory. To maximize the real-time secrecy rate, we propose an extended Kalman filtering (EKF)-based method for tracking and predicting Bob's location at the UAV based on the delay measurements extracted from the sensing echoes. We then formulate a non-convex real-time trajectory design problem and develop an efficient iterative algorithm for finding a near optimal solution. Our numerical results demonstrate that the proposed algorithm is capable of accurately tracking Bob and strikes a compelling legitimate vs. leakage rate trade-off.
M$^3$SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration
Authors: Xiang Cheng, Ziwei Huang, Lu Bai, Haotian Zhang, Mingran Sun, Boxun Liu, Sijiang Li, Jianan Zhang, Minson Lee
Date: 2023-06-25 00:00:00

The sixth generation (6G) of mobile communication system is witnessing a new paradigm shift, i.e., integrated sensing-communication system. A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research. This paper develops a novel simulation dataset, named M3SC, for mixed multi-modal (MMM) sensing-communication integration, and the generation framework of the M3SC dataset is further given. To obtain multi-modal sensory data in physical space and communication data in electromagnetic space, we utilize AirSim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data. Furthermore, the in-depth integration and precise alignment of AirSim, WaveFarer, and Wireless InSite are achieved. The M3SC dataset covers various weather conditions, various frequency bands, and different times of the day. Currently, the M3SC dataset contains 1500 snapshots, including 80 RGB images, 160 depth maps, 80 LiDAR point clouds, 256 sets of mmWave waveforms with 8 radar point clouds, and 72 channel impulse response (CIR) matrices per snapshot, thus totaling 120,000 RGB images, 240,000 depth maps, 120,000 LiDAR point clouds, 384,000 sets of mmWave waveforms with 12,000 radar point clouds, and 108,000 CIR matrices. The data processing result presents the multi-modal sensory information and communication channel statistical properties. Finally, the MMM sensing-communication application, which can be supported by the M3SC dataset, is discussed.
Significance of Medium-Wave AM Radio Broadcasting for Enhanced Disaster Resilience in Japan: A Case Study in the Kanto Region and Fukui using Nonpowered HOOPRA
Authors: Eiichi Shoji
Date: 2023-06-24 00:00:00

On the occasion of centenary anniversary of the Great Kanto Earthquake and commencement of radio broadcasting in Japan, this study reiterates the paramount importance of medium-wave (MW) AM broadcasting in safeguarding public safety and security. Utilizing the electromagnetic principles of MW, the author has earlier developed hoop-shaped radio (HOOPRA), which is a battery-free sustainable radio receiver. This study aims to determine the maximum achievable reception distance with HOOPRA for broadcasts from public stations, such as Nihon Hoso Kyokai (NHK) JOFG (927 kHz, 5 kW) in Fukui, and NHK JOAK (594 kHz, 300 kW), and JOAB (693 kHz, 500 kW), in the Kanto region. The significance of the findings in this study is that approximately 38 million individuals in the Kanto region, residing within an 80 km radius of JOAK or JOAB, can access broadcasts using only the energy of radio waves with HOOPRA. Additionally, ~0.4 million people in Fukui, within a 15 km radius of JOFG, can potentially be recipients of the broadcast. Given that most transmitting stations operate at 5 kW nationwide, HOOPRA can be effectively utilized within a 15 km radius of each station. Moreover, these outcomes validate the efficacy of HOOPRA as a radio receiver and provide valuable insights into the global potential applicability of MW AM radios. Furthermore, the current investigation underscores the need to reevaluate the significance of terrestrial MW broadcasting as a vital source of emergency information, especially in the event of anticipated natural disasters, such as the predicted Nankai Trough Earthquake.
Demonstrating the Merits of Integrating Multipath Signals into 5G LoS-Based Positioning Systems for Navigation in Challenging Environments
Authors: Qamar Bader, Sharief Saleh, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2023-06-23 00:00:00

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS)communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domain, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system's performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system's behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system's robustness, particularly when low-cost 5G receivers with a limited resolution for angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).
An elementary proof of the dual representation of Expected Shortfall
Authors: Martin Herdegen, Cosimo Munari
Date: 2023-06-26 00:00:00

We provide an elementary proof of the dual representation of Expected Shortfall on the space of integrable random variables over a general probability space. Unlike the results in the extant literature, our proof only exploits basic properties of quantile functions and can thus be easily implemented in any graduate course on risk measures. As a byproduct, we obtain a new proof of the subadditivity of Expected Shortfall.
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
Authors: Haitao Lin, Yufei Huang, Odin Zhang, Lirong Wu, Siyuan Li, ZhiYuan Chen, Stan Z. Li
Date: 2023-05-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Drug Design

In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To solve this, we propose D3FG, a functional-group-based diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. And the two kinds of components can together form complicated fragments that enhance ligand-protein interactions. To be specific, in the diffusion process, D3FG diffuses the data distribution of the positions, orientations, and types of the components into a prior distribution; In the generative process, the noise is gradually removed from the three variables by denoisers parameterized with designed equivariant graph neural networks. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. Besides, D3FG as a solution to a new task of molecule elaboration, could generate molecules with high affinities based on existing ligands and the hotspots of target proteins.
Parametric modeling of mechanical effects on circadian oscillators
Authors: Keith E. Kennedy, Juan F. Abenza, Leone Rossetti, Xavier Trepat, Pablo Villoslada, Jordi Garcia-Ojalvo
Date: 2023-06-24 00:00:00

Circadian rhythms are archetypical examples of nonlinear oscillations. While these oscillations are usually attributed to circuits of biochemical interactions among clock genes and proteins, recent experimental studies reveal that they are also affected by the cell's mechanical environment. Here we extend a standard biochemical model of circadian rhythmicity to include mechanical effects in a parametric manner. Using experimental observations to constrain the model, we suggest specific ways in which the mechanical signal might affect the clock. Additionally, a bifurcation analysis of the system predicts that these mechanical signals need to be within an optimal range for circadian oscillations to occur.
SumVg: Total heritability explained by all variants in genome-wide association studies based on summary statistics with standard error estimates
Authors: Hon-Cheong So, Xiao Xue, Pak-Chung Sham
Date: 2023-06-25 00:00:00
Tasks: All

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits and diseases, and a key question is how much heritability could be explained by all variants in GWAS. One widely used approach that relies on summary statistics only is LD score regression (LDSC), however the approach requires certain assumptions on the SNP effects (all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the true z-statistics from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of results and applicability of the approach. In this study we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. Simulations showed that delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. Particularly, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. In addition, we applied our method to estimate SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg, available at https://github.com/lab-hcso/Estimating-SE-of-total-heritability/ . In conclusion, SumVg may provide a useful alternative tool for SNP heritability and SE estimates, which does not rely on distributional assumptions of SNP effects.
Regulation of Mouse Learning and Mood by the Anti-Inflammatory Cytokine Interleukin-10
Authors: Ryan Joseph Worthen
Date: 2023-06-26 00:00:00
Tasks: Object Recognition

Major depressive disorder is a widespread mood disorder. One of the most debilitating symptoms patients often experience is cognitive impairment. Recent findings suggest that inflammation is associated with depression and impaired cognition. Pro-inflammatory cytokines are elevated in the blood of depressed patients and impair learning and memory processes, suggesting that an anti-inflammatory approach might be beneficial for both depression and cognition. Utilizing the learned helplessness paradigm, we first established a mouse model of depression in which learning and memory are impaired. We found that learned helplessness (LH) impaired novel object recognition (NOR) and spatial working memory. LH mice also exhibited reduced hippocampal dendritic spine density and increased microglial activation compared to non-shocked (NS) mice or mice that were subjected to the learned helpless paradigm but did not exhibit learned helplessness (non-learned helpless, or NLH). These effects were mediated by microglia, as treatment with PLX5622, which depletes microglia and macrophages, restored learning and memory and hippocampal dendritic spine density in LH mice. However, PLX5622 also impaired learning and memory and reduced hippocampal dendritic spine density in NLH mice, suggesting that microglia in NLH mice are involved in the production of molecules that promote learning and memory. We found that microglial interleukin (IL)-10 levels were reduced in LH mice and IL-10 administration was sufficient to restore NOR, spatial working memory, and hippocampal dendritic spine density in LH mice, and in NLH mice treated with PLX5622, consistent with a pro-cognitive role for IL-10. Altogether, these data demonstrate the critical role of IL-10 in promoting learning and memory after learned helplessness.
Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample
Authors: David Benrimoh, Victoria L. Fisher, Rashina Seabury, Ely Sibarium, Catalina Mourgues, Doris Chen, Albert Powers
Date: 2023-06-24 00:00:00
Tasks: Hallucination

There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Much past work demonstrating prior overweighting has used simple, non-linguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. We administered a linguistic version of the Conditioned Hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, recent auditory hallucinations, stimulus thresholds, and stimulus detection were collected; data was used to fit parameters of a Hierarchical Gaussian Filter model of perceptual inference to determine how latent perceptual states influenced task behavior. Replicating past results, higher CH rates were associated with measures of higher hallucination-proneness and recent hallucinatory experiences; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to recent hallucinatory experiences. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and tended to have lower response confidence, consistent with lower precision of sensory evidence. We show that hallucination-prone individuals in the general population have increased conditioned hallucination rates using a linguistic version of the CH task, and replicated the finding that increased CH rates and recent hallucinations correlate with increased prior weighting. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness. *contributed equally
On Computational Mechanisms for Shared Intentionality, and Speculation on Rationality and Consciousness
Authors: John Rushby
Date: 2023-06-03 00:00:00
Tasks: Attribute

A singular attribute of humankind is our ability to undertake novel, cooperative behavior, or teamwork. This requires that we can communicate goals, plans, and ideas between the brains of individuals to create shared intentionality. Using the information processing model of David Marr, I derive necessary characteristics of basic mechanisms to enable shared intentionality between prelinguistic computational agents and indicate how these could be implemented in present-day AI-based robots. More speculatively, I suggest the mechanisms derived by this thought experiment apply to humans and extend to provide explanations for human rationality and aspects of intentional and phenomenal consciousness that accord with observation. This yields what I call the Shared Intentionality First Theory (SIFT) for rationality and consciousness. The significance of shared intentionality has been recognized and advocated previously, but typically from a sociological or behavioral point of view. SIFT complements prior work by applying a computer science perspective to the underlying mechanisms.
Superinfection and the hypnozoite reservoir for Plasmodium vivax: a general framework
Authors: Somya Mehra, James M. McCaw, Peter G. Taylor
Date: 2023-06-25 00:00:00
Tasks: Epidemiology

Malaria is a parasitic disease, transmitted by mosquito vectors. Plasmodium vivax presents particular challenges for disease control, in light of an undetectable reservoir of latent parasites (hypnozoites) within the host liver. Superinfection, which is driven by temporally proximate mosquito inoculation and/or hypnozoite activation events, is an important feature of P. vivax. Here, we present a model of hypnozoite accrual and superinfection for P. vivax. To couple host and vector dynamics, we construct a density-dependent Markov population process with countably many types, for which disease extinction is shown to occur almost surely. We also establish a functional law of large numbers, taking the form of an infinite-dimensional system of ordinary differential equations that can also be recovered under the hybrid approximation or a standard compartment modelling approach. Recognising that the subset of these equations that models the infection status of human hosts has precisely the same form as the Kolmogorov forward equations for a Markovian network of infinite server queues with an inhomogeneous batch arrival process, we use physical insight into the evolution of the latter to write down a time-dependent multivariate generating function for the solution. We use this characterisation to collapse the infinite-compartment model into a single integrodifferential equation (IDE) governing the intensity of mosquito-to-human transmission. Through a steady state analysis, we recover a threshold phenomenon for this IDE in terms of a bifurcation parameter $R_0$, with the disease-free equilibrium shown to be uniformly asymptotically stable if $R_0<1$ and an endemic equilibrium solution emerging if $R_0>1$. Our work provides a theoretical basis to explore the epidemiology of P. vivax, and introduces a general strategy for constructing tractable population-level models of malarial superinfection.
Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning
Authors: Joel Ong, Dorien Herremans
Date: 2023-06-08 00:00:00
Tasks: Multi-Task Learning, Time Series

A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks related to volatility, such as forecasting realized volatility as measured by different volatility estimators. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio's performance. These findings demonstrate that MTL can be a powerful tool in finance.
A lower bound for the volatility swap in the lognormal SABR model
Authors: E. Alòs, F. Rolloos, K. Shiraya
Date: 2023-06-26 00:00:00
Tasks: valid

In the short time to maturity limit it is proved that for the conditionally lognormal SABR model the zero vanna implied volatility is a lower bound for the volatility swap strike. The result is valid for all values of the correlation parameter and is a sharper lower bound than the at-the-money implied volatility for correlation less than or equal to zero.
C3S Micro-architectural Enhancement: Spike Encoder Block and Relaxing Gamma Clock (Asynchronous)
Authors: Alok Anand, Ivan Khokhlov, Abhishek Anand
Date: 2023-06-26 00:00:00

The field of neuromorphic computing is rapidly evolving. As both biological accuracy and practical implementations are explored, existing architectures are modified and improved for both purposes. The Temporal Neural Network(TNN) style of architecture is a good basis for approximating biological neurons due to its use of timed pulses to encode data and a voltage-threshold-like system. Using the Temporal Neural Network cortical column C3S architecture design as a basis, this project seeks to augment the network's design. This project takes note of two ideas and presents their designs with the goal of improving existing cortical column architecture. One need in this field is for an encoder that could convert between common digital formats and timed neuronal spikes, as biologically accurate networks are temporal in nature. To this end, this project presents an encoder to translate between binary encoded values and timed spikes to be processed by the neural network. Another need is for the reduction of wasted processing time to idleness, caused by lengthy Gamma cycle processing bursts. To this end, this project presents a relaxation of Gamma cycles to allow for them to end arbitrarily early once the network has determined an output response. With the goal of contributing to the betterment of the field of neuromorphic computer architecture, designs for both a binary-to-spike encoder, as well as a Gamma cycle controller, are presented and evaluated for optimal design parameters, with overall system gain and performance.
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
Authors: Zihang Xu, Ziqing Yang, Yiming Cui, Shijin Wang
Date: 2023-06-27 00:00:00
Tasks: Logical Reasoning, Machine Reading Comprehension, Reading Comprehension

In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical reasoning. Although some methods for it have been put forward, they either are designed in a quite complicated way or rely too much on external structures. In this paper, we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand but highly effective further pre-training task which logically strengthens the pre-trained models with the help of 6 types of logical indicators and a logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0 while keeping competitive general language understanding ability through testing on tasks in GLUE. Besides, at the beginning of the era of large language models, we take several of them like ChatGPT into comparison and find that IDOL still shows its advantage.