pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Sparse Acoustic Array Geometries for Indoor Localization
Authors: Georg K. J. Fischer, Niklas Thiedecke, Thomas Schaechtle, Andrea Gabbrielli, Fabian Höflinger, Alexander Stolz, Stefan J. Rupitsch
Date: 2024-06-13 00:00:00
Tasks: Indoor Localization

Angle-of-Arrival estimation technology, with its potential advantages, emerges as an intriguing choice for indoor localization. Notably, it holds the promise of reducing installation costs. In contrast to ToF/TDoA based systems, AoA-based approaches require a reduced number of nodes for effective localization. This characteristic establishes a trade-off between installation costs and the complexity of hardware and software. Moreover, the appeal of acoustic localization is further heightened by its capacity to provide cost-effective hardware solutions while maintaining a high degree of accuracy. Consequently, acoustic AoA estimation technology stands out as a feasible and compelling option in the field of indoor localization. Sparse arrays additionally have the ability to estimate the DoA of more sources than available sensors by placing sensors in a specific geometry. In this contribution, we introduce a measurement platform designed to evaluate various sparse array geometries experimentally. The acoustic microphone array comprises 64 microphones arranged in an 8x8 grid, following an Uniform Rectangular Array (URA) configuration, with a grid spacing of 8.255 mm. This configuration achieves a spatial Nyquist frequency of approximately 20.8 kHz in the acoustic domain at room temperature. Notably, the array exhibits a mean spherical error of 1.26{\deg} when excluding higher elevation angles. The platform allows for masking sensors to simulate sparse array configurations. We assess four array geometries through simulations and experimental data, identifying the Open-Box and Nested array geometries as robust candidates. Additionally, we demonstrate the array's capability to concurrently estimate the directions of three emitting sources using experimental data, employing waveforms consisting of orthogonal codes.
Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios
Authors: Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, Guanghua Yang
Date: 2024-06-13 00:00:00

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system's spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.
Joint Hybrid Transceiver and Reflection Matrix Design for RIS-Aided mmWave MIMO Cognitive Radio Systems
Authors: Jitendra Singh, Suraj Srivastava, Surya P. Yadav, Aditya K. Jagannatham, Lajos Hanzo
Date: 2024-06-13 00:00:00
Tasks: Unity

In this work, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) cognitive radio (CR) downlink operating in the underlay mode is investigated. The cognitive base station (CBS) communicates with multiple secondary users (SUs), each having multiple RF chains in the presence of a primary user (PU). We conceive a joint hybrid transmit precoder (TPC), receiver combiner (RC), and RIS reflection matrix (RM) design, which maximizes the sum spectral efficiency (SE) of the secondary system while maintaining the interference induced at the PU below a specified threshold. To this end, we formulate the sum-SE maximization problem considering the total transmit power (TP), the interference power (IP), and the non-convex unity modulus constraints of the RF TPC, RF RC, and RM. To solve this highly non-convex problem, we propose a two-stage hybrid transceiver design in conjunction with a novel block coordinate descent (BCD)-successive Riemannian conjugate gradient (SRCG) algorithm. We initially decompose the RF TPC, RC, and RM optimization problem into a series of sub-problems and subsequently design pairs of RF TPC and RC vectors, followed by successively optimizing the elements of the RM using the iterative BCD-SRCG algorithm. Furthermore, based on the effective baseband (BB) channel, the BB TPC and BB RC are designed using the proposed direct singular value decomposition (D-SVD) and projection based SVD (P-SVD) methods. Subsequently, the proportional water-filling solution is proposed for optimizing the power, which maximizes the weighted sum-SE of the system. Finally, simulation results are provided to compare our proposed schemes to several benchmarks and quantify the impact of other parameters on the sum-SE of the system.
Real-time Digital RF Emulation -- II: A Near Memory Custom Accelerator
Authors: Mandovi Mukherjee, Xiangyu Mao, Nael Rahman, Coleman DeLude, Joe Driscoll, Sudarshan Sharma, Payman Behnam, Uday Kamal, Jongseok Woo, Daehyun Kim, Sharjeel Khan, Jianming Tong, Jamin Seo, Prachi Sinha, Madhavan Swaminathan, Tushar Krishna, Santosh Pande, Justin Romberg, Saibal Mukhopadhyay
Date: 2024-06-13 00:00:00

A near memory hardware accelerator, based on a novel direct path computational model, for real-time emulation of radio frequency systems is demonstrated. Our evaluation of hardware performance uses both application-specific integrated circuits (ASIC) and field programmable gate arrays (FPGA) methodologies: 1). The ASIC testchip implementation, using TSMC 28nm CMOS, leverages distributed autonomous control to extract concurrency in compute as well as low latency. It achieves a $518$ MHz per channel bandwidth in a prototype $4$-node system. The maximum emulation range supported in this paradigm is $9.5$ km with $0.24$ $\mu$s of per-sample emulation latency. 2). The FPGA-based implementation, evaluated on a Xilinx ZCU104 board, demonstrates a $9$-node test case (two Transmitters, one Receiver, and $6$ passive reflectors) with an emulation range of $1.13$ km to $27.3$ km at $215$ MHz bandwidth.
Real-time Digital RF Emulation -- I: The Direct Path Computational Model
Authors: Coleman DeLude, Joe Driscoll, Mandovi Mukherjee, Nael Rahman, Uday Kamal, Xiangyu Mao, Sharjeel Khan, Hariharan Sivaraman, Eric Huang, Jeffrey McHarg, Madhavan Swaminathan, Santosh Pande, Saibal Mukhopadhyay, Justin Romberg
Date: 2024-06-13 00:00:00

In this paper we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of $M$ objects all interacting with one another, the tapped delay line model's computational requirements scale as $O(M^3)$ per sample: there are $O(M^2)$ channels, each with $O(M)$ complexity. In this paper, we develop a new ``direct path" model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an $O(M^2)$ per sample scaling of the computational requirements. The impact of this is drastic, a $200$ object scenario sees about a $100\times$ reduction in the number of per sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle level C++ simulator of a hardware implementation of the model.
Doppler-Robust Maximum Likelihood Parametric Channel Estimation for Multiuser MIMO-OFDM
Authors: Enrique T. R. Pinto, Markku Juntti
Date: 2024-06-12 00:00:00

The high directionality and intense Doppler effects of millimeter wave (mmWave) and sub-terahertz (subTHz) channels demand accurate localization of the users and a new paradigm of channel estimation. For orthogonal frequency division multiplexing (OFDM) waveforms, estimating the geometric parameters of the radio channel can make these systems more Doppler-resistant and also enhance sensing and positioning performance. In this paper, we derive a multiuser, multiple-input multiple-output (MIMO), maximum likelihood, parametric channel estimation algorithm for uplink sensing, which is capable of accurately estimating the parameters of each multipath that composes each user's channel under severe Doppler shift conditions. The presented method is one of the only Doppler-robust currently available algorithms that does not rely on line search.
Sequential MAP Parametric OFDM Channel Estimation for Joint Sensing and Communication
Authors: Enrique T. R. Pinto, Markku Juntti
Date: 2024-06-12 00:00:00
Tasks: Integrated sensing and communication

Uplink sensing is still a relatively unexplored scenario in integrated sensing and communication which can be used to improve positioning and sensing estimates. We introduce a pilot-based maximum likelihood, and a maximum a posteriori parametric channel estimation procedure using an orthogonal frequency division multiplexing (OFDM) waveform in uplink sensing. The algorithm is capable of estimating the multipath components of the channel, such as the angles of arrival, departure, path coefficient, and the delay and Doppler terms. As an advantage, when compared to other existing methods, the proposed procedure presents expressions for exact alternating coordinate updates, which can be further improved to achieve a competitive multipath channel estimation tool.
Bridging Simulation and Measurements through Ray-Launching Analysis: A Study in a Complex Urban Scenario Environment
Authors: Xueyun Long, Amina Fellan, Mario Pauli, Hans D. Schotten, Thomas Zwick
Date: 2024-06-12 00:00:00

With the rapid increase in mobile subscribers, there is a drive towards achieving higher data rates, prompting the use of higher frequencies in future wireless communication technologies. Wave propagation channel modeling for these frequencies must be considered in conjunction with measurement results. This paper presents a ray-launching (RL)-based simulation in a complex urban scenario characterized by an undulating terrain with a high density of trees. The simulation results tend to closely match the reported measurements when more details are considered. This underscores the benefits of using the RL method, which provides detailed space-time and angle-delay results.
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition
Authors: Yuan Liao, Yuhong Zhang, Shenghuan Wang, Xiruo Zhang, Yiling Zhang, Wei Chen, Yuzhe Gu, Liya Huang
Date: 2024-06-12 00:00:00
Tasks: Contrastive Learning, EEG, EEG Emotion Recognition, Emotion Recognition

Recent advances in non-invasive EEG technology have broadened its application in emotion recognition, yielding a multitude of related datasets. Yet, deep learning models struggle to generalize across these datasets due to variations in acquisition equipment and emotional stimulus materials. To address the pressing need for a universal model that fluidly accommodates diverse EEG dataset formats and bridges the gap between laboratory and real-world data, we introduce a novel deep learning framework: the Contrastive Learning based Diagonal Transformer Autoencoder (CLDTA), tailored for EEG-based emotion recognition. The CLDTA employs a diagonal masking strategy within its encoder to extracts full-channel EEG data's brain network knowledge, facilitating transferability to the datasets with fewer channels. And an information separation mechanism improves model interpretability by enabling straightforward visualization of brain networks. The CLDTA framework employs contrastive learning to distill subject-independent emotional representations and uses a calibration prediction process to enable rapid adaptation of the model to new subjects with minimal samples, achieving accurate emotion recognition. Our analysis across the SEED, SEED-IV, SEED-V, and DEAP datasets highlights CLDTA's consistent performance and proficiency in detecting both task-specific and general features of EEG signals related to emotions, underscoring its potential to revolutionize emotion recognition research.
Interference Analysis for Coexistence of UAVs and Civil Aircrafts Based on Automatic Dependent Surveillance-Broadcast
Authors: Yiyang Liao, Ziye Jia, Chao Dong, Lei Zhang, Qihui Wu, Huiling Hu, Zhu Han
Date: 2024-06-12 00:00:00

Due to the advantages of high mobility and easy deployment, unmanned aerial vehicles (UAVs) are widely applied in both military and civilian fields. In order to strengthen the flight surveillance of UAVs and guarantee the airspace safety, UAVs can be equipped with the automatic dependent surveillance-broadcast (ADS-B) system, which periodically sends flight information to other aircrafts and ground stations (GSs). However, due to the limited resource of channel capacity, UAVs equipped with ADS-B results in the interference between UAVs and civil aircrafts (CAs), which further impacts the accuracy of received information at GSs. In detail, the channel capacity is mainly affected by the density of aircrafts and the transmitting power of ADS-B. Hence, based on the three-dimensional poisson point process, this work leverages the stochastic geometry theory to build a model of the coexistence of UAVs and CAs and analyze the interference performance of ADS-B monitoring system. From simulation results, we reveal the effects of transmitting power, density, threshold and pathloss on the performance of the ADS-B monitoring system. Besides, we provide the suggested transmitting power and density for the safe coexistence of UAVs and CAs.
Collective Invasion: When does domain curvature matter?
Authors: Joseph J. Pollacco, Ruth E. Baker, Philip K. Maini
Date: 2024-06-12 00:00:00

Real-world cellular invasion processes often take place in curved geometries. Such problems are frequently simplified in models to neglect the curved geometry in favour of computational simplicity, yet doing so risks inaccuracy in any model-based predictions. To quantify the conditions under which neglecting a curved geometry are justifiable, we examined solutions to the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) model, a paradigm nonlinear reaction-diffusion equation typically used to model spatial invasion, on an annular geometry. Defining $\epsilon$ as the ratio of the annulus thickness $\delta$ and radius $r_0$ we derive, through an asymptotic expansion, the conditions under which it is appropriate to ignore the domain curvature, a result that generalises to other reaction-diffusion equations with constant diffusion coefficient. We further characterise the nature of the solutions through numerical simulation for different $r_0$ and $\delta$. Thus, we quantify the size of the deviation from an analogous simulation on the rectangle, and how this deviation changes across the width of the annulus. Our results grant insight into when it is appropriate to neglect the domain curvature in studying travelling wave behaviour in reaction-diffusion equations.
Functional voxel hierarchy and afferent capacity revealed mental state transition on dynamic correlation resting-state fMRI
Authors: Dong Soo Lee, Hyun Joo Kim, Youngmin Huh, Yeon Koo Kang, Wonseok Whi, Hyekyoung Lee, Hyejin Kang
Date: 2024-06-12 00:00:00
Tasks: Functional Connectivity

Voxel hierarchy on dynamic brain graphs is produced by k core percolation on functional dynamic amplitude correlation of resting-state fMRI. Directed graphs and their afferent/efferent capacities are produced by Markov modeling of the universal cover of undirected graphs simultaneously with the calculation of volume entropy. Positive and unsigned negative brain graphs were analyzed separately on sliding-window representation to underpin the visualization and quantitation of mental dynamic states with their transitions. Voxel hierarchy animation maps of positive graphs revealed abrupt changes in coreness k and kmaxcore, which we called mental state transitions. Afferent voxel capacities of the positive graphs also revealed transient modules composed of dominating voxels/independent components and their exchanges representing mental state transitions. Animation and quantification plots of voxel hierarchy and afferent capacity corroborated each other in underpinning mental state transitions and afferent module exchange on the positive directed functional connectivity graphs. We propose the use of spatiotemporal trajectories of voxels on positive dynamic graphs to construct hierarchical structures by k core percolation and quantified in- and out-flows of information of voxels by volume entropy/directed graphs to subserve diverse resting mental state transitions on resting-state fMRI graphs in normal human individuals.
Foundations for reconstructing early microbial life
Authors: Betul Kacar
Date: 2024-06-13 00:00:00

For more than 3.5 billion years, life experienced dramatic environmental extremes on Earth. These include shifts from oxygen-less to over-oxygenated atmospheres and cycling between hothouse conditions and global glaciations. Meanwhile, an ecological revolution took place. The planet evolved from one dominated by microbial life to one containing the plants and animals that are most familiar today. The activities of many key cellular inventions evolved early in the history of life, collectively defining the nature of our biosphere and underpinning human survival. There is a critical need for a new disciplinary synthesis to reveal how microbes and their molecular systems survived ever changing global conditions over deep time. This review critically examines our current understanding of early microbial life and describes the foundations of an emerging area in microbiology and evolutionary synthetic biology to reconstruct the earliest microbial innovations.
Refining the drift barrier hypothesis: a role of recessive gene count and an inhomogeneous Muller`s ratchet
Authors: Luis A. La Rocca, Konrad Gerischer, Anton Bovier, Peter M. Krawitz
Date: 2024-06-13 00:00:00

The drift-barrier hypothesis states that random genetic drift constrains the refinement of a phenotype under natural selection. The influence of effective population size and the genome-wide deleterious mutation rate were studied theoretically, and an inverse relationship between mutation rate and genome size has been observed for many species. However, the effect of the recessive gene count, an important feature of the genomic architecture, is unknown. In a Wright-Fisher model, we studied the mutation burden for a growing number of N completely recessive and lethal disease genes. Diploid individuals are represented with a binary $2 \times N$ matrix denoting wild-type and mutated alleles. Analytic results for specific cases were complemented by simulations across a broad parameter regime for gene count, mutation and recombination rates. Simulations revealed transitions to higher mutation burden and prevalence within a few generations that were linked to the extinction of the wild-type haplotype (least-loaded class). This metastability, that is, phases of quasi-equilibrium with intermittent transitions, persists over $100\,000$ generations. The drift-barrier hypothesis is confirmed by a high mutation burden resulting in population collapse. Simulations showed the emergence of mutually exclusive haplotypes for a mutation rate above 0.02 lethal equivalents per generation for a genomic architecture and population size representing complex multicellular organisms such as humans. In such systems, recombination proves pivotal, preventing population collapse and maintaining a mutation burden below 10. This study advances our understanding of gene pool stability, and particularly the role of the number of recessive disorders. Insights into Muller`s ratchet dynamics are provided, and the essential role of recombination in curbing mutation burden and stabilizing the gene pool is demonstrated.
DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts
Authors: Joel Ong, Dorien Herremans
Date: 2024-06-13 00:00:00
Tasks: Management, Mixture-of-Experts, Multi-Task Learning, Time Series

This paper introduces DeepUnifiedMom, a deep learning framework that enhances portfolio management through a multi-task learning approach and a multi-gate mixture of experts. The essence of DeepUnifiedMom lies in its ability to create unified momentum portfolios that incorporate the dynamics of time series momentum across a spectrum of time frames, a feature often missing in traditional momentum strategies. Our comprehensive backtesting, encompassing diverse asset classes such as equity indexes, fixed income, foreign exchange, and commodities, demonstrates that DeepUnifiedMom consistently outperforms benchmark models, even after factoring in transaction costs. This superior performance underscores DeepUnifiedMom's capability to capture the full spectrum of momentum opportunities within financial markets. The findings highlight DeepUnifiedMom as an effective tool for practitioners looking to exploit the entire range of momentum opportunities. It offers a compelling solution for improving risk-adjusted returns and is a valuable strategy for navigating the complexities of portfolio management.
Bootstrapping Language Models with DPO Implicit Rewards
Authors: Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin
Date: 2024-06-14 00:00:00

Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used in subsequent DPO rounds. We incorporate refinements that debias the length of the responses and improve the quality of the preference dataset to further improve our approach. Our approach, named self-alignment with DPO ImpliCit rEwards (DICE), shows great improvements in alignment and achieves superior performance than Gemini Pro on AlpacaEval 2, reaching 27.55% length-controlled win rate against GPT-4 Turbo, but with only 8B parameters and no external feedback. Our code is available at https://github.com/sail-sg/dice.