pwc-archive/papers-with-abstracts huggingface dataset:
Control Barrier Functions for Linear Continuous-Time Input-Delay Systems with Limited-Horizon Previewable Disturbances
Authors: Tarun Pati, Seunghoon Hwang, Sze Zheng Yong
Date: 2024-03-07 00:00:00

Cyber-physical and autonomous systems are often equipped with mechanisms that provide predictions/projections of future disturbances, e.g., road curvatures, commonly referred to as preview or lookahead, but this preview information is typically not leveraged in the context of deriving control barrier functions (CBFs) for safety. This paper proposes a novel limited preview control barrier function (LPrev-CBF) that avoids both ends of the spectrum, where on one end, the standard CBF approach treats the (previewable) disturbances simply as worst-case adversarial signals and on the other end, a recent Prev-CBF approach assumes that the disturbances are previewable and known for the entire future. Moreover, our approach applies to input-delay systems and has recursive feasibility guarantees since we explicitly take input constraints/bounds into consideration. Thus, our approach provides strong safety guarantees in a less conservative manner than standard CBF approaches while considering a more realistic setting with limited preview and input delays.
Impact of the Antenna on the Sub-Terahertz Indoor Channel Characteristics: An Experimental Approach
Authors: Priyangshu Sen, Sherif Badran, Vitaly Petrov, Arjun Singh, Josep M. Jornet
Date: 2024-03-07 00:00:00

Terahertz-band (100 GHz-10 THz) communication is a promising radio technology envisioned to enable ultra-high data rate, reliable and low-latency wireless connectivity in next-generation wireless systems. However, the low transmission power of THz transmitters, the need for high gain directional antennas, and the complex interaction of THz radiation with common objects along the propagation path make crucial the understanding of the THz channel. In this paper, we conduct an extensive channel measurement campaign in an indoor setting (i.e., a conference room) through a channel sounder with 0.1 ns time resolution and 20 GHz bandwidth at 140 GHz. Particularly, the impact of different antenna directivities (and, thus, beam widths) on the channel characteristics is extensively studied. The experimentally obtained dataset is processed to develop the path loss model and, subsequently, derive key channel metrics such as the path loss exponent, delay spread, and K-factor. The results highlight the multi-faceted impact of the antenna gain on the channel and, by extension, the wireless system and, thus, show that an antenna-agnostic channel model cannot capture the propagation characteristics of the THz channel.
A Crosstalk-Aware Timing Prediction Method in Routing
Authors: Leilei Jin, Jiajie Xu, Wenjie Fu, Hao Yan, Longxing Shi
Date: 2024-03-07 00:00:00
Tasks: Prediction

With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay. During the routing, the crosstalk effect is typically modeled by predicting coupling capacitance with congestion information. However, the timing estimation tends to be overly pessimistic, as the crosstalk-induced delay depends not only on the coupling capacitance but also on the signal arrival time. This work presents a crosstalk-aware timing estimation method using a two-step machine learning approach. Interconnects that are physically adjacent and overlap in signal timing windows are filtered first. Crosstalk delay is predicted by integrating physical topology and timing features without relying on post-routing results and the parasitic extraction. Experimental results show a match rate of over 99% for identifying crosstalk nets compared to the commercial tool on the OpenCores benchmarks, with prediction results being more accurate than those of other state-of-the-art methods.
Low Complexity Radio Frequency Interference Mitigation for Radio Astronomy Using Large Antenna Array
Authors: Zaid Bin Tariq, Teviet Creighton, Louis P. Dartez, Naofal Al-Dhahir, Murat Torlak
Date: 2024-03-07 00:00:00
Tasks: Astronomy

With the ongoing growth in radio communications, there is an increased contamination of radio astronomical source data, which hinders the study of celestial radio sources. In many cases, fast mitigation of strong radio frequency interference (RFI) is valuable for studying short lived radio transients so that the astronomers can perform detailed observations of celestial radio sources. The standard method to manually excise contaminated blocks in time and frequency makes the removed data useless for radio astronomy analyses. This motivates the need for better radio frequency interference (RFI) mitigation techniques for array of size M antennas. Although many solutions for mitigating strong RFI improves the quality of the final celestial source signal, many standard approaches require all the eigenvalues of the spatial covariance matrix ($\textbf{R} \in \mathbb{C}^{M \times M}$) of the received signal, which has $O(M^3)$ computation complexity for removing RFI of size $d$ where $\textit{d} \ll M$. In this work, we investigate two approaches for RFI mitigation, 1) the computationally efficient Lanczos method based on the Quadratic Mean to Arithmetic Mean (QMAM) approach using information from previously-collected data under similar radio-sky-conditions, and 2) an approach using a celestial source as a reference for RFI mitigation. QMAM uses the Lanczos method for finding the Rayleigh-Ritz values of the covariance matrix $\textbf{R}$, thus, reducing the computational complexity of the overall approach to $O(\textit{d}M^2)$. Our numerical results, using data from the radio observatory Long Wavelength Array (LWA-1), demonstrate the effectiveness of both proposed approaches to remove strong RFI, with the QMAM-based approach still being computationally efficient.
Optical turbulence profiling at the Table Mountain Facility with the Laser Communication Relay Demonstration GEO downlink
Authors: Marcus Birch, Sabino Piazzolla, Preston Hooser, Francis Bennet, Tony Travouillon, William Buehlman
Date: 2024-03-07 00:00:00

We report the first measurement of the atmospheric optical turbulence profile using the transmitted beam from a satellite laser communication terminal. A Ring Image Next Generation Scintillation Sensor (RINGSS) instrument for turbulence profiling, as described in Tokovinin (MNRAS, 502.1, 2021, 747-808), was deployed at the NASA/Jet Propulsion Laboratory's Table Mountain Facility (TMF) in California. The optical turbulence profile was measured with the downlink optical beam from the Laser Communication Relay Demonstration (LCRD) Geostationary satellite. LCRD conducts links with the Optical Communication Telescope Laboratory ground station and the RINGSS instrument was co-located at TMF to conduct measurements. Turbulence profiles were measured at day and night and atmospheric coherence lengths were compared with other turbulence monitors such as a solar scintillometer and Polaris motion monitor. RINGSS sensitivity to boundary layer turbulence, a feature not provided by many profilers, is also shown to agree with a boundary layer scintillometer at TMF (R=0.85). Diurnal evolution of optical turbulence and measured profiles are presented. The correlation of RINGSS with other turbulence monitors (R=0.75-0.86) demonstrates the concept of free-space optical communications turbulence profiling, which could be adopted as a way to support optical ground stations in a future Geostationary feeder link network. These results also provide further evidence that RINGSS, a relatively new instrument concept, correlates well with other instruments in daytime and nighttime turbulence.
High throughput spectroscopy of pL droplets
Authors: Marc Sulliger, Jaime Ortega Arroyo, Romain Quidant
Date: 2024-03-06 00:00:00

Droplet microfluidics offers a versatile platform for analyzing liquid samples. Despite its potential, there is a lack of techniques that allow to reliably probe individual circulating droplets. The prospect of combining droplet microfluidics with sensitive, broadband spectroscopic techniques would therefore unlock new capabilities for various disciplines, including biomedicine and biochemistry. Here we present an integrated optofluidic platform that seamlessly combines droplet microfluidics and advanced hyperspectral imaging. This enables high-resolution, label-free analysis of single picolitre-sized droplets, providing valuable insights into their content. As a proof-of-principle, we demonstrate the ability of our platform to study rapid dynamic changes in a heterogeneous population of plasmonic nanoparticles with millisecond-time resolution. Furthermore, we demonstrate the effectiveness of the platform in biosensing applied to short DNA strands, achieving a detection sensitivity in the range of 100 pM. Finally, we show that the platform provides the flexibility to monitor samples over extended periods of time (hours) in a multiplexed manner.
Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
Authors: Shuan Chen, Yousung Jung
Date: 2024-02-29 00:00:00
Tasks: Retrosynthesis

Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meticulously assembling an extensive set of out-of-distribution (OOD) reactions. Our findings demonstrate that while template-free models exhibit potential in predicting precursors with novel synthesis rules, their top-10 exact-match accuracy in OOD reactions is strikingly modest (< 1%). Furthermore, despite the capability of generating novel reactions, our investigation highlights a recurring issue where more than half of the novel reactions predicted by template-free models are chemically implausible. Consequently, we advocate for the future development of template-free models that integrate considerations of chemical feasibility when navigating unexplored regions of reaction space.
Nonparametric Regression under Cluster Sampling
Authors: Yuya Shimizu
Date: 2024-03-07 00:00:00
Tasks: Density Estimation, regression, valid

This paper develops a general asymptotic theory for nonparametric kernel regression in the presence of cluster dependence. We examine nonparametric density estimation, Nadaraya-Watson kernel regression, and local linear estimation. Our theory accommodates growing and heterogeneous cluster sizes. We derive asymptotic conditional bias and variance, establish uniform consistency, and prove asymptotic normality. Our findings reveal that under heterogeneous cluster sizes, the asymptotic variance includes a new term reflecting within-cluster dependence, which is overlooked when cluster sizes are presumed to be bounded. We propose valid approaches for bandwidth selection and inference, introduce estimators of the asymptotic variance, and demonstrate their consistency. In simulations, we verify the effectiveness of the cluster-robust bandwidth selection and show that the derived cluster-robust confidence interval improves the coverage ratio. We illustrate the application of these methods using a policy-targeting dataset in development economics.
A Logarithmic Mean Divisia Index Decomposition of CO$_2$ Emissions from Energy Use in Romania
Authors: Mariana Carmelia Balanica-Dragomir, Gabriel Murariu, Lucian Puiu Georgescu
Date: 2024-03-07 00:00:00

Carbon emissions have become a specific alarming indicators and intricate challenges that lead an extended argue about climate change. The growing trend in the utilization of fossil fuels for the economic progress and simultaneously reducing the carbon quantity has turn into a substantial and global challenge. The aim of this paper is to examine the driving factors of CO$_2$ emissions from energy sector in Romania during the period 2008-2022 emissions using the log mean Divisia index (LMDI) method and takes into account five items: CO$_2$ emissions, primary energy resources, energy consumption, gross domestic product and population, the driving forces of CO$_2$ emissions, based on which it was calculated the contribution of carbon intensity, energy mixes, generating efficiency, economy, and population. The results indicate that generating efficiency effect -90968.57 is the largest inhibiting index while economic effect is the largest positive index 69084.04 having the role of increasing CO$_2$ emissions.
A dual approach to nonparametric characterization for random utility models
Authors: Nobuo Koida, Koji Shirai
Date: 2024-03-07 00:00:00

This paper develops a novel characterization for random utility models (RUM), which turns out to be a dual representation of the characterization by Kitamura and Stoye (2018, ECMA). For a given family of budgets and its "patch" representation \'a la Kitamura and Stoye, we construct a matrix $\Xi$ of which each row vector indicates the structure of possible revealed preference relations in each subfamily of budgets. Then, it is shown that a stochastic demand system on the patches of budget lines, say $\pi$, is consistent with a RUM, if and only if $\Xi\pi \geq \mathbb{1}$, where the RHS is the vector of $1$'s. In addition to providing a concise quantifier-free characterization, especially when $\pi$ is inconsistent with RUMs, the vector $\Xi\pi$ also contains information concerning (1) sub-families of budgets in which cyclical choices must occur with positive probabilities, and (2) the maximal possible weights on rational choice patterns in a population. The notion of Chv\'atal rank of polytopes and the duality theorem in linear programming play key roles to obtain these results.
A topological characterization of the existence of w-stable sets
Authors: Athanasios Andrikopoulos, Nikolaos Sampanis
Date: 2024-03-07 00:00:00

The theory of optimal choice sets is a solution theory that has a long and well-established tradition in social choice and game theories. Some of important general solution concepts of choice problems when the set of best alternatives does not exist (this problem occurs when the preferences yielded by an economic process are cyclic) is the Stable Set (Von Neumann-Morgenstern set) and its variants (Generalized Stable set, Extended Stable set, m-Stable set and w-Stable set). The theory of w-stable sets solution is more realistic because: (1) It solves the existence problem of solution; (2) It expands the notions of maximal alternative set and (3) The concept of stability is defined in such a way as to prevent a chosen alternative from being dominated by another alternative and sets this stability within the solution. In this paper, we present a topological characterization of the existence of w-Stable sets solution of arbitrary binary relations over non-finite sets of alternatives.
Two-Person Adversarial Games are Zero-Sum: An Elaboration of a Folk Theorem
Authors: M. Ali Khan, Arthur Paul Pedersen, David Schrittesser
Date: 2024-03-06 00:00:00

The observation that every two-person adversarial game is an affine transformation of a zero-sum game is traceable to Luce & Raiffa (1957) and made explicit in Aumann (1987). Recent work of (ADP) Adler et al. (2009), and of Raimondo (2023) in increasing generality, proves what has so far remained a conjecture. We present two proofs of an even more general formulation: the first draws on multilinear utility theory developed by Fishburn & Roberts (1978); the second is a consequence of the ADP proof itself for a special case of a two-player game with a set of three actions.
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
Authors: Xiaoyu Tang, Yixin Lin, Ting Dang, Yuanfang Zhang, Jintao Cheng
Date: 2024-03-07 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech Emotion Recognition (SER) is crucial in human-machine interactions. Mainstream approaches utilize Convolutional Neural Networks or Recurrent Neural Networks to learn local energy feature representations of speech segments from speech information, but struggle with capturing global information such as the duration of energy in speech. Some use Transformers to capture global information, but there is room for improvement in terms of parameter count and performance. Furthermore, existing attention mechanisms focus on spatial or channel dimensions, hindering learning of important temporal information in speech. In this paper, to model local and global information at different levels of granularity in speech and capture temporal, spatial and channel dependencies in speech signals, we propose a Speech Emotion Recognition network based on CNN-Transformer and multi-dimensional attention mechanisms. Specifically, a stack of CNN blocks is dedicated to capturing local information in speech from a time-frequency perspective. In addition, a time-channel-space attention mechanism is used to enhance features across three dimensions. Moreover, we model local and global dependencies of feature sequences using large convolutional kernels with depthwise separable convolutions and lightweight Transformer modules. We evaluate the proposed method on IEMOCAP and Emo-DB datasets and show our approach significantly improves the performance over the state-of-the-art methods.
Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling
Authors: Jianwei Zhang, Yonggang Shi
Date: 2024-03-07 00:00:00
Tasks: Anatomy

Normative modeling has emerged as a pivotal approach for characterizing heterogeneity and individual variance in neurodegenerative diseases, notably Alzheimer's disease(AD). One of the challenges of cortical normative modeling is the anatomical structure mismatch due to folding pattern variability. Traditionally, registration is applied to address this issue and recently many studies have utilized deep generative models to generate anatomically align samples for analyzing disease progression; however, these models are predominantly applied to volume-based data, which often falls short in capturing intricate morphological changes on the brain cortex. As an alternative, surface-based analysis has been proven to be more sensitive in disease modeling such as AD, yet, like volume-based data, it also suffers from the mismatch problem. To address these limitations, we proposed a novel generative normative modeling framework by transferring the conditional diffusion generative model to the spherical non-Euclidean domain. Additionally, this approach generates normal feature map distributions by explicitly conditioning on individual anatomical segmentation to ensure better geometrical alignment which helps to reduce anatomical variance between subjects in analysis. We find that our model can generate samples that are better anatomically aligned than registered reference data and through ablation study and normative assessment experiments, the samples are able to better measure individual differences from the normal distribution and increase sensitivity in differentiating cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients.
Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis
Authors: Abdul Rasak Zubair, Oluwaseun Adewunmi Alo
Date: 2024-03-06 00:00:00
Tasks: Texture Classification

Grey Level Co-occurrence Matrix and Grey Level Difference Vector are described and computed for twenty four 128 x 128 x 3 test images along horizontal, vertical and diagonal directions. Second order image statistics such as Contrast, Dissimilarity, Homogeneity (Inverse Difference Moment), Angular Second Moment, Energy, Maximum Probability, Entropy, Mean, Standard Deviation and Correlation are computed and studied. Grey Level Co-occurrence Matrix (GLCM) and Grey Level Difference Vector (GLDV) are described and computed for twenty four 128 x 128 x 3 test images along horizontal, vertical and diagonal directions. Second order image statistics such as Contrast, Dissimilarity, Homogeneity (Inverse Difference Moment), Angular Second Moment (ASM), Energy, Maximum Probability, Entropy, Mean, Standard Deviation and Correlation are computed and studied. The results show that smooth images have lower Contrast values and higher Probability of Occurrence of Difference of same range as rough images having higher Contrast values and lower Probability of Occurrence. The degree of smoothness or roughness of an image may not be exactly the same along horizontal, vertical and diagonal directions. There are significant correlation between Dissimilarity & Contrast, Homogeneity & Contrast, Entropy & Contrast, Energy & Contrast, Standard Deviation & Contrast, Correlation & Contrast, and Probability of Occurrence of Difference of 0-19 & Contrast with correlation coefficients of 0.9322, -0.5011, 0.6681, -0.4255, -0.4914, 0.5428, and -0.8346 respectively.
A divide-and-conquer approach for sparse recovery of high dimensional signals
Authors: Aron Bevelander, Kim Batselier, Nitin Jonathan Myers
Date: 2024-03-07 00:00:00
Tasks: compressed sensing

Compressed sensing (CS) techniques demand significant storage and computational resources, when recovering high-dimensional sparse signals. Block CS (BCS), a special class of CS, addresses both the storage and complexity issues by partitioning the sparse recovery problem into several sub-problems. In this paper, we derive a Welch bound-based guarantee on the reconstruction error with BCS. Our guarantee reveals that the reconstruction quality with BCS monotonically reduces with an increasing number of partitions. To alleviate this performance loss, we propose a sparse recovery technique that exploits correlation across the partitions of the sparse signal. Our method outperforms BCS in the moderate SNR regime, for a modest increase in the storage and computational complexities.
Examination Minutes Measurement of Single-Antenna-Element Beamforming
Authors: Nils L. Johannsen, Lukas Grundmann
Date: 2024-03-07 00:00:00

This document shall provide all knowledge gained in conjunction and preparation with the conducted measurements in the antenna measurement chamber of the Institute of Microwave and Wireless Systems (IMW) of the Leibniz University of Hannover (LUH). The measurements have been prepared and conducted by Lukas Grundmann, IMW, and Nils L. Johannsen, Chair of Information and Coding Theory (ICT) of the Christian- Albrechts-University (CAU) of Kiel. This minute shall allow a simpler understanding and quicker reapplication of the required calibrations and system setup for the measurements of further antennas.