pwc-archive/papers-with-abstracts huggingface dataset:
Monolithic Silicon-Photonics Linear-Algebra Accelerators Enabling Next-Gen Massive MIMO
Authors: Tzu-Chien Hsueh, Yeshaiahu Fainman, Bill Lin
Date: 2024-02-13 00:00:00

A system-on-chip (SoC) photonic-electronic linear-algebra accelerator with the features of wavelength-division-multiplexing (WDM) based broadband photodetections and high-dimensional matrix-inversion operations fabricated in advanced monolithic silicon-photonics (M-SiPh) semiconductor process technology is proposed to achieve substantial leaps in computation density and energy efficiency, including realistic considerations of energy/area overhead due to electronic/photonic on-chip conversions, integrations, and calibrations through holistic co-design methodologies to support linear-detection based massive multiple-input multiple-output (MIMO) decoding technology requiring the inversion of channel matrices and other emergent applications limited by linear-algebra computation capacities.
On the Stability of Undesirable Equilibria in the Quadratic Program Framework for Safety-Critical Control
Authors: Matheus F. Reis, A. Pedro Aguiar
Date: 2024-02-12 00:00:00

Control Lyapunov functions (CLFs) and Control Barrier Functions (CBFs) have been used to develop provably safe controllers by means of quadratic programs (QPs). This framework guarantees safety in the form of trajectory invariance with respect to a given set, but it can introduce undesirable equilibrium points to the closed loop system, which can be asymptotically stable. In this work, we present a detailed study of the formation and stability of equilibrium points with the CLF-CBF-QP framework with multiple CBFs. In particular, we prove that undesirable equilibrium points occur for most systems, and their stability is dependent on the CLF and CBF geometrical properties. We introduce the concept of CLF-CBF compatibility for a system, regarding a CLF-CBF pair inducing no stable equilibrium points other than the CLF global minimum on the corresponding closed-loop dynamics. Sufficient conditions for CLF-CBF compatibility for LTI and drift-less full-rank systems with quadratic CLF and CBFs are derived, and we propose a novel control strategy to induce smooth changes in the CLF geometry at certain regions of the state space in order to satisfy the CLF-CBF compatibility conditions, aiming to achieve safety with respect to multiple safety objectives and quasi-global convergence of the trajectories towards the CLF minimum. Numeric simulations illustrate the applicability of the proposed method.
Cosmic radiation drives quasi-periodic changes in the diversity of siliceous marine microplankton
Authors: Péter Ozsvárt, Emma Kun
Date: 2024-02-14 00:00:00
Tasks: Diversity

Radiolarians are significant contributors to the oceanic primary productivity and the global silica cycle in the last 500 Myr. Their diversity throughout the Phanerozoic shows periodic fluctuations. We identify a possible abiotic candidate for driving these patterns which seems to potentially influence radiolarian diversity changes during this period at a significance level of $\sim 2.2 \sigma$. Our finding suggests a significant correlation between the origination of new radiolaria species and maximum excursions of the Solar system from the Galactic plane, where the magnetic shielding of cosmic rays is expected to be weaker. We connect the particularly strong radiolaria blooming during the Middle Triassic to the so-called Mesozoic dipole-low of the geomagnetic field, which was in its deepest state when radiolarias were blooming. According to the scenario, high-energy cosmic rays presumably implied particular damage to the DNA during the maximum excursions which may trigger large chromosomal abnormalities leading to the appearance of a large number of new genera and species during these periods.
A general mechanism for enhancer-insulator pairing reveals heterogeneous dynamics in long-distant 3D gene regulation
Authors: Lucas Hedström, Ralf Metzler, Ludvig Lizana
Date: 2024-02-14 00:00:00
Tasks: Blocking

Cells regulate fates and complex body plans using spatiotemporal signaling cascades that alter gene expression. Enhancers, short DNA sequences (50-150 base pairs), help coordinate these cascades by attracting regulatory proteins to enhance the transcription of distal genes by binding to promoters. In humans, there are hundreds of thousands of enhancers dispersed across the genome, which poses a challenging coordination task to prevent unintended gene activation. To mitigate this problem, the genome contains additional DNA elements, insulators, that block enhancer-promoter interactions. However, there is an open problem with how the insulation works, especially as enhancer-insulator pairs may be separated by millions of base pairs. Based on recent empirical data from Hi-C experiments, this paper proposes a new mechanism that challenges the common paradigm that rests on specific insulator-insulator interactions. Instead, this paper introduces a stochastic looping model where enhancers bind weakly to surrounding chromatin. After calibrating the model to experimental data, we use simulations to study the broad distribution of hitting times between an enhancer and a promoter when there are blocking insulators. In some cases, there is a large difference between average and most probable hitting times, making it difficult to assign a typical time scale, hinting at highly defocused regulation times. We also map our computational model onto a resetting problem that allows us to derive several analytical results. Besides offering new insights into enhancer-insulator interactions, our paper advances the understanding of gene regulatory networks and causal connections between genome folding and gene activation.
Multichannel Nonlinear Equalization in Coherent WDM Systems based on Bi-directional Recurrent Neural Networks
Authors: Stavros Deligiannidis, Kyle R. H. Bottrill, Kostas Sozos, Charis Mesaritakis, Periklis Petropoulos, Adonis Bogris
Date: 2023-06-24 00:00:00

Kerr nonlinearity in the form of self- and cross-phase modulation imposes a fundamental limitation to the capacity of wavelength division multiplexed (WDM) optical communication systems. Digital back-propagation (DBP), that requires solving the inverse-propagating nonlinear Schr\"odinger equation (NLSE), is a widely adopted technique for the mitigation of impairments induced by Kerr nonlinearity. However, multi-channel DBP is too complex to be implemented commercially in WDM systems. Recurrent neural networks (RNNs) have been recently exploited for nonlinear signal processing in the context of optical communications. In this work, we propose multi-channel equalization through a bidirectional vanilla recurrent neural network (bi-VRNN) in order to improve the performance of the single-channel bi-VRNN algorithm in the transmission of WDM M-QAM signals. We compare the proposed digital algorithm to full-field DBP and to the single channel bi-RNN in order to reveal its merits with respect to both performance and complexity. We finally provide experimental verification through a QPSK metro link, showcasing over 2.5 dB optical signal-to-noise ratio (OSNR) gain and up to 43% complexity reduction with respect to the single-channel RNN and the DBP.
Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs
Authors: Masayuki Sawada, Takuya Ishihara, Daisuke Kurisu, Yasumasa Matsuda
Date: 2024-02-14 00:00:00
Tasks: regression, valid

We introduce a multivariate local-linear estimator for multivariate regression discontinuity designs in which treatment is assigned by crossing a boundary in the space of running variables. The dominant approach uses the Euclidean distance from a boundary point as the scalar running variable; hence, multivariate designs are handled as uni-variate designs. However, the bandwidth selection with the distance running variable is suboptimal and inefficient for the underlying multivariate problem. We handle multivariate designs as multivariate. In this study, we develop a novel asymptotic normality for multivariate local-polynomial estimators. Our estimator is asymptotically valid and can capture heterogeneous treatment effects over the boundary. We demonstrate the effectiveness of our estimator through numerical simulations. Our empirical illustration of a Colombian scholarship study reveals a richer heterogeneity of the treatment effect that is hidden in the original estimates.
Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation
Authors: Jackson Bunting, Paul Diegert, Arnaud Maurel
Date: 2024-02-13 00:00:00

We provide identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: known heterogeneity, initially unknown heterogeneity that may be revealed over time, and transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and realized outcomes. We establish identification of the outcome equation parameters and the distribution of the unobservables, under the standard assumption that unknown heterogeneity and uncertainty are normally distributed. We also show that, absent known heterogeneity, the model is identified without making any distributional assumption. We then derive the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a tractable profile likelihood-based estimation procedure. Our estimator exhibits good finite-sample properties. Finally, we illustrate our approach with an application to ability learning in the context of occupational choice. Our results point to substantial ability learning based on realized wages.
On Bayesian Filtering for Markov Regime Switching Models
Authors: Nigar Hashimzade, Oleg Kirsanov, Tatiana Kirsanova, Junior Maih
Date: 2024-02-12 00:00:00
Tasks: Time Series

This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Keynesian DSGE model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. We show that the canonical IMM filter is faster and is no less, and often more, accurate than its competitors within IMM and GPB families, the latter including the commonly used Kim and Nelson (1999) filter. Using it with the matching smoother improves the precision in recovering unobserved variables by about 25 percent. Furthermore, applying it to the U.S. 1947-2023 macroeconomic time series, we successfully identify significant past policy shifts including those related to the post-Covid-19 period. Our results demonstrate the practical applicability and potential of the proposed routines in macroeconomic analysis.
Database for the meta-analysis of the social cost of carbon (v2025.1)
Authors: Richard S. J. Tol
Date: 2024-02-14 00:00:00

A new version of the database for the meta-analysis of estimates of the social cost of carbon is presented. New records were added, and new fields on gender and stochasticity.
Portfolio Optimization under Transaction Costs with Recursive Preferences
Authors: Martin Herdegen, David Hobson, Alex S. L. Tse
Date: 2024-02-13 00:00:00
Tasks: Portfolio Optimization

The Merton investment-consumption problem is fundamental, both in the field of finance, and in stochastic control. An important extension of the problem adds transaction costs, which is highly relevant from a financial perspective but also challenging from a control perspective because the solution now involves singular control. A further significant extension takes us from additive utility to stochastic differential utility (SDU), which allows time preferences and risk preferences to be disentangled. In this paper, we study this extended version of the Merton problem with proportional transaction costs and Epstein-Zin SDU. We fully characterise all parameter combinations for which the problem is well posed (which may depend on the level of transaction costs) and provide a full verification argument that relies on no additional technical assumptions and uses primal methods only. The case with SDU requires new mathematical techniques as duality methods break down. Even in the special case of (additive) power utility, our arguments are significantly simpler, more elegant and more far-reaching than the ones in the extant literature. This means that we can easily analyse aspects of the problem which previously have been very challenging, including comparative statics, boundary cases which heretofore have required separate treatment and the situation beyond the small transaction cost regime. A key and novel idea is to parametrise consumption and the value function in terms of the shadow fraction of wealth, which may be of much wider applicability.
An Analysis of the Recovery Path of the Consumer Sector in the Post-Pandemic Era
Authors: Wenbo Lyu, JiaYi Zhu, Yunan Ding, Keming Zhang
Date: 2024-02-11 00:00:00

This paper proposes a referencable pattern of the recovery of the consumption sector, a new dimension to observe and evaluate the intrinsic value of the consumption sector, and proposes the concept of sensory-based consumption and the ranking of the weights of different categories;creates the concept of digital consumption index, coupled with digital RMB index and China-style digital economy index. Finally we explain the internal logic of digital consumption as a consumption upgrade tool and a higher valuation target in the context of China's economic performance in 2022 and the Chinese government's policy in 2023, leading to the investment strategy of roller conduction effect.
Equitable screening
Authors: Filip Tokarski
Date: 2024-02-13 00:00:00

I study the problem of a government providing benefits while considering the perceived equity of the resulting allocation. Such concerns are modeled through an equity constraint requiring that equally deserving agents receive equal allocations. I ask what forms of screening are compatible with equity and show that while the government cannot equitably screen with a single instrument (e.g. payments or wait times), combining multiple instruments, which on their own favor different groups, allows it to screen while still producing an equitable allocation.
Domestic Competitive Balance and International Success: The Case of The Football Industry
Authors: Juan D. Moreno-Ternero, Tim Pawlowski, Shlomo Weber
Date: 2024-02-13 00:00:00

This paper examines the interdependence of international success and competitive balance of domestic sports competitions. More specifically, we apply the notion of the Herfindahl-Hirschman index to examine the effect of international rewards on distortion of competitive balance in domestic competitions and derive conditions under which the level of domestic competitive balance raises or falls. Our results yield interesting policy implications for the regulation of prize schemes in international competitions.
Continuous Representations of Preferences by Means of Two Continuous Functions
Authors: Gianni Bosi, Asier Estevan
Date: 2024-01-24 00:00:00
Tasks: Relation

Let $\precsim$ be a reflexive binary relation on a topological space $(X, \tau )$. A pair $(u,v)$ of continuous real-valued functions on $(X, \tau )$ is said to be a {\em continuous representation} of $\precsim$ if, for all $x,y \in X$, [$(x \precsim y \Leftrightarrow u(x) \leq v(y))$]. In this paper we provide a characterization of the existence of a continuous representation of this kind in the general case when neither the functions $u$ and $v$ nor the topological space $(X,\tau )$ are required to satisfy any particular assumptions. Such characterization is based on a suitable continuity assumption of the binary relation $\precsim$, called {\em weak continuity}. In this way, we generalize all the previous results on the continuous representability of interval orders, and also of total preorders, as particular cases.
Mixture to Mixture: Leveraging Close-talk Mixtures as Weak-supervision for Speech Separation
Authors: Zhong-Qiu Wang
Date: 2024-02-14 00:00:00
Tasks: Speaker Separation, Speech Separation

We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures.
A Comprehensive Review of Software and Hardware Energy Efficiency of Video Decoders
Authors: Matthias Kränzler, Christian Herglotz, André Kaup
Date: 2024-02-14 00:00:00
Tasks: Decoder

Energy and compression efficiency are two essential parts of modern video decoder implementations that have to be considered. This work comprehensively studies the following six video coding formats regarding compression and decoding energy efficiency: AVC, VP9, HEVC, AV1, VVC, and AVM. We first evaluate the energy demand of reference and optimized software decoder implementations. Furthermore, we consider the influence of the usage of SIMD instructions on those decoder implementations. We find that AV1 is a sweet spot for optimized software decoder implementations with an additional energy demand of 16.55% and bitrate savings of -43.95% compared to VP9. We furthermore evaluate the hardware decoding energy demand of four video coding formats. Thereby, we show that AV1 has energy demand increases by 117.50% compared to VP9. For HEVC, we found a sweet spot in terms of energy demand with an increase of 6.06% with respect to VP9. Relative to their optimized software counterparts, hardware video decoders reduce the energy consumption to less than 9% compared to software decoders.
Saliency-aware End-to-end Learned Variable-Bitrate 360-degree Image Compression
Authors: Oguzhan Gungordu, A. Murat Tekalp
Date: 2024-02-14 00:00:00
Tasks: Image Compression

Effective compression of 360$^\circ$ images, also referred to as omnidirectional images (ODIs), is of high interest for various virtual reality (VR) and related applications. 2D image compression methods ignore the equator-biased nature of ODIs and fail to address oversampling near the poles, leading to inefficient compression when applied to ODI. We present a new learned saliency-aware 360$^\circ$ image compression architecture that prioritizes bit allocation to more significant regions, considering the unique properties of ODIs. By assigning fewer bits to less important regions, significant data size reduction can be achieved while maintaining high visual quality in the significant regions. To the best of our knowledge, this is the first study that proposes an end-to-end variable-rate model to compress 360$^\circ$ images leveraging saliency information. The results show significant bit-rate savings over the state-of-the-art learned and traditional ODI compression methods at similar perceptual visual quality.
Motion-Adaptive Inference for Flexible Learned B-Frame Compression
Authors: M. Akin Yilmaz, O. Ugur Ulas, Ahmet Bilican, A. Murat Tekalp
Date: 2024-02-13 00:00:00
Tasks: Video Compression

While the performance of recent learned intra and sequential video compression models exceed that of respective traditional codecs, the performance of learned B-frame compression models generally lag behind traditional B-frame coding. The performance gap is bigger for complex scenes with large motions. This is related to the fact that the distance between the past and future references vary in hierarchical B-frame compression depending on the level of hierarchy, which causes motion range to vary. The inability of a single B-frame compression model to adapt to various motion ranges causes loss of performance. As a remedy, we propose controlling the motion range for flow prediction during inference (to approximately match the range of motions in the training data) by downsampling video frames adaptively according to amount of motion and level of hierarchy in order to compress all B-frames using a single flexible-rate model. We present state-of-the-art BD rate results to demonstrate the superiority of our proposed single-model motion-adaptive inference approach to all existing learned B-frame compression models.
Rate-Splitting Multiple Access for Quantized ISAC LEO Satellite Systems: A Max-Min Fair Energy-Efficient Beam Design
Authors: Ziang Liu, Longfei Yin, Wonjae Shin, Bruno Clerckx
Date: 2024-02-14 00:00:00
Tasks: Fairness, ISAC, Quantization

Low earth orbit (LEO) satellite systems with sensing functionality are envisioned to facilitate global-coverage service and emerging applications in 6G. Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication. To effectively control the interference, a rate-splitting multiple access (RSMA) scheme is employed as the interference management strategy in the system design. On the other hand, to address the limited power supply at the LEO satellites, we consider low-resolution quantization digital-to-analog converters (DACs) at the transmitter to reduce power consumption, which grows exponentially with the number of quantization bits. Additionally, optimizing the total energy efficiency (EE) of the system is a common practice to save the power. However, this metric lacks fairness among users. To ensure this fairness and further enhance EE, we investigate the max-min fairness EE of the RSMA-assisted integrated sensing and communications (ISAC)-LEO satellite system. In this system, the satellite transmits a quantized dual-functional signal serving downlink users while detecting a target. Specifically, we optimize the precoders for maximizing the minimal EE among all users, considering the power consumption of each radio frequency (RF) chain under communication and sensing constraints. To tackle this optimization problem, we proposed an iterative algorithm based on successive convex approximation (SCA) and Dinkelbach's method. Numerical results illustrate that the proposed design and RSMA architecture outperforms strategies maximizing the total EE of the system, space-division multiple access (SDMA), and orthogonal multiple access (OMA) in terms of max-min fairness EE and the communication-sensing trade-off.