pwc-archive/papers-with-abstracts huggingface dataset:
Buckling-induced transmission switching in phononic waveguides in the presence of disorder
Authors: Ali Kanj, Alexander F. Vakakis, Sameh Tawfick
Date: 2023-01-27 00:00:00

On-chip phononic circuits tailor the transmission of elastic waves, which can couple to electronic and photonic systems, enabling new signal manipulation capabilities. Phononic circuits rely on waveguides that transmit elastic waves within desired frequency passbands, typically designed based on the Bloch modes of the waveguide constitutive cell, assuming linearity and periodicity. MEMS waveguides composed of coupled drumhead (membrane) resonators offer tunable MHz operation frequencies for applications in nonlinear optomechanics, topological insulators, phononic cavities, and acoustic switching. Here, we construct a reduced-order model (ROM) to demonstrate the switching of signal transmission in drumhead-resonator waveguides due to thermoelastic buckling. The ROM shows that buckling amplifies existing structural disorders, breaking the periodicity required for waveguide transmission through the first passband. This periodicity breaking manifests in the localization of the first-passband modes, like classical Anderson localization caused by disorders. The proposed ROM is essential to study the investigated phenomena since Bloch mode analysis fails for weakly-disordered (< 5%) finite waveguides due to the disorder amplification caused by the thermoelastic buckling. The illustrated transmission control should be useful for logical acoustic operations, like switching, and can be extended to 2D circuits in the future.
On the optimality of Kalman Filter for Fault Detection
Authors: Jinming Zhou, Yucai Zhu
Date: 2023-01-27 00:00:00
Tasks: Fault Detection

Kalman filter is widely used for residual generation in fault detection. It leads to optimality in fault detection using some performance indices and also leads to statistically sound residual evaluation and threshold setting. This paper shows that these nice features do not necessarily imply an optimal fault detection performance. Based on a performance index related to fault detection rate and false alarm rate, several occasions where Kalman filter should not be used are pointed out; further the residual evaluation and threshold setting are discussed, in which it is pointed out that in stochastic setting an optimal statistical test of Kamlan filter is not related to optimality of commonly used detection performance indicators. The theoretical analysis is verified through Monte Carlo simulations and Tennessee Eastman process (TEP) dataset.
Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes
Authors: Yifan Lin, Enlu Zhou
Date: 2023-01-26 00:00:00

We consider infinite-horizon Markov Decision Processes where parameters, such as transition probabilities, are unknown and estimated from data. The popular distributionally robust approach to addressing the parameter uncertainty can sometimes be overly conservative. In this paper, we utilize the recently proposed formulation, Bayesian risk Markov Decision Process (BR-MDP), to address parameter (or epistemic) uncertainty in MDPs. To solve the infinite-horizon BR-MDP with a class of convex risk measures, we propose a computationally efficient approach called approximate bilevel difference convex programming (ABDCP). The optimization is performed offline and produces the optimal policy that is represented as a finite state controller with desirable performance guarantees. We also demonstrate the empirical performance of the BR-MDP formulation and the proposed algorithm.
Competitive exclusion and Hebbian couplings in random generalised Lotka-Volterra systems
Authors: Enrique Rozas Garcia, Mark J. Crumpton, Tobias Galla
Date: 2023-01-27 00:00:00

We study communities emerging from generalised random Lotka--Volterra dynamics with a large number of species with interactions determined by the degree of niche overlap. Each species is endowed with a number of traits, and competition between pairs of species increases with their similarity in trait space. This leads to a model with random Hopfield-like interactions. We use tools from the theory of disordered systems, notably dynamic mean field theory, to characterise the statistics of the resulting communities at stable fixed points and determine analytically when stability breaks down. Two distinct types of transition are identified in this way, both marked by diverging abundances, but differing in the behaviour of the integrated response function. At fixed points only a fraction of the initial pool of species survives. We numerically study the eigenvalue spectra of the interaction matrix between extant species. We find evidence that the two types of dynamical transition are, respectively, associated with the bulk spectrum or an outlier eigenvalue crossing into the right half of the complex plane.
Synthetic Difference In Differences Estimation
Authors: Damian Clarke, Daniel Pailañir, Susan Athey, Guido Imbens
Date: 2023-01-27 00:00:00

In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-in-differences can be used in a wide class of circumstances where treatment effects on some particular policy or event are desired, and repeated observations on treated and untreated units are available over time. We lay out the theory underlying SDID, both when there is a single treatment adoption date and when adoption is staggered over time, and discuss estimation and inference in each of these cases. We introduce the sdid command which implements these methods in Stata, and provide a number of examples of use, discussing estimation, inference, and visualization of results.
Simple Difference-in-Differences Estimation in Fixed-T Panels
Authors: Nicholas Brown, Kyle Butts, Joakim Westerlund
Date: 2023-01-26 00:00:00
Tasks: valid

The present paper proposes a new treatment effects estimator that is valid when the number of time periods is small, and the parallel trends condition holds conditional on covariates and unobserved heterogeneity in the form of interactive fixed effects. The estimator also allow the control variables to be affected by treatment and it enables estimation of the resulting indirect effect on the outcome variable. The asymptotic properties of the estimator are established and their accuracy in small samples is investigated using Monte Carlo simulations. The empirical usefulness of the estimator is illustrated using as an example the effect of increased trade competition on firm markups in China.
'Good job!' The impact of positive and negative feedback on performance
Authors: Daniel Goller, Maximilian Späth
Date: 2023-01-27 00:00:00

We analyze the causal impact of positive and negative feedback on professional performance. We exploit a unique data source in which quasi-random, naturally occurring variations within subjective ratings serve as positive and negative feedback. The analysis shows that receiving positive feedback has a favorable impact on subsequent performance, while negative feedback does not have an effect. These main results are found in two different environments and for distinct cultural backgrounds, experiences, and gender of the feedback recipients. The findings imply that managers should focus on giving positive motivational feedback.
Heat and Worker Health
Authors: Andrew Ireland, David Johnston, Rachel Knott
Date: 2023-01-27 00:00:00

Extreme heat negatively impacts cognition, learning, and task performance. With increasing global temperatures, workers may therefore be at increased risk of work-related injuries and illness. This study estimates the effects of temperature on worker health using records spanning 1985-2020 from an Australian mandatory insurance scheme. High temperatures are found to cause significantly more claims, particularly among manual workers in outdoor-based industries. These adverse effects have not diminished across time, with the largest effect observed for the 2015-2020 period, indicating increasing vulnerability to heat. Within occupations, the workers most adversely affected by heat are female, older-aged and higher-earning. Finally, results from firm-level panel analyses show that the percentage increase in claims on hot days is largest at "safer" firms.
The effect of primary school education on preventive behaviours during COVID-19 in Japan
Authors: Eiji Yamamura, Yoshiro Tsutsui, Fumio Ohtake
Date: 2023-01-27 00:00:00

Education plays a critical role on promoting preventive behaviours against the spread of pandemics. In Japan, hand-washing education in primary schools was positively correlated with preventive behaviours against COVID-19 transmission for adults in 2020 during the early stages of COVID-19 [1]. The following year, the Tokyo Olympics were held in Japan, and a state of emergency was declared several times. Public perceptions of and risks associated with the pandemic changed drastically with the emergence of COVID-19 vaccines. We re-examine whether effect of hand-washing education on preventive behaviours persisted by covering a longer period of the COVID-19 pandemic than previous studies. 26 surveys were conducted nearly once a month for 30 months from March 2020 (the early stage of COVID-19) to September 2022 in Japan. By corresponding with the same individuals across surveys, we comprehensively gathered data on preventive behaviours during this period. In addition, we asked about hand-washing education they had received in their primary school. We used the data to investigate how and the degree to which school education is associated with pandemic mitigating preventive behaviours. We found that hand-washing education in primary school is positively associated with behaviours such as hand washing and mask wearing as a COVID-19 preventive measure, but not related to staying at home. We observed a statistically significant difference in hand washing between adults who received childhood hand-washing education and those who did not. This difference persisted throughout the study period. In comparison, the difference in mask wearing between the two groups was smaller, but still statistically significant. Furthermore, there was no difference in staying at home between them.
Recovering utility
Authors: Christopher P. Chambers, Federico Echenique, Nicolas S. Lambert
Date: 2023-01-27 00:00:00

We provide sufficient conditions under which a utility function may be recovered from a finite choice experiment. Identification, as is commonly understood in decision theory, is not enough. We provide a general recoverability result that is widely applicable to modern theories of choice under uncertainty. Key is to allow for a monetary environment, in which an objective notion of monotonicity is meaningful. In such environments, we show that subjective expected utility, as well as variational preferences, and other parametrizations of utilities over uncertain acts are recoverable. We also consider utility recovery in a statistical model with noise and random deviations from utility maximization.
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
Authors: Junde Wu, Wei Ji, Huazhu Fu, Min Xu, Yueming Jin, Yanwu Xu
Date: 2023-01-19 00:00:00
Tasks: Image Generation, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive capabilities and sparked much discussion within the community. Recent investigations have further unveiled the utility of DPM in the domain of medical image analysis, as underscored by the commendable performance exhibited by the medical image segmentation model across various tasks. Although these models were originally underpinned by a UNet architecture, there exists a potential avenue for enhancing their performance through the integration of vision transformer mechanisms. However, we discovered that simply combining these two models resulted in subpar performance. To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities. Through comprehensive evaluation, our approach demonstrates superiority over prior state-of-the-art (SOTA) methodologies. Code is released at https://github.com/KidsWithTokens/MedSegDiff
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery
Authors: Xing Zhang, Haiyang Zhang, Nimrod Glazer, Oded Cohen, Eliya Reznitskiy, Shlomi Savariego, Moshe Namer, Yonina C. Eldar
Date: 2023-01-27 00:00:00
Tasks: Quantization

Quantization plays a critical role in digital signal processing systems, allowing the representation of continuous amplitude signals with a finite number of bits. However, accurately representing signals requires a large number of quantization bits, which causes severe cost, power consumption, and memory burden. A promising way to address this issue is task-based quantization. By exploiting the task information for the overall system design, task-based quantization can achieve satisfying performance with low quantization costs. In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation. The prototype consists of a tailored configurable combining board, and a software-based processing and demonstration system. Through experiments, we verify that with proper design, the task-based quantization achieves a reduction of 25 fold in memory by reducing from 16 receivers with 16 bits each to 2 receivers with 5 bits each, without compromising signal recovery performance.
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel
Authors: Zhao Zhang, Danping He, Xiping Wang, Ke Guan, Zhangdui Zhong, Jianwu Dou
Date: 2023-01-27 00:00:00
Tasks: Super-Resolution

Mobile channel modeling has always been the core part for design, deployment and optimization of communication system, especially in 5G and beyond era. Deterministic channel modeling could precisely achieve mobile channel description, however with defects of equipment and time consuming. In this paper, we proposed a novel super resolution (SR) model for cluster characteristics prediction. The model is based on deep neural networks with residual connection. A series of simulations at 3.5 GHz are conducted by a three-dimensional ray tracing (RT) simulator in diverse scenarios. Cluster characteristics are extracted and corresponding data sets are constructed to train the model. Experiments demonstrate that the proposed SR approach could achieve better power and cluster location prediction performance than traditional interpolation method and the root mean square error (RMSE) drops by 51% and 78% relatively. Channel impulse response (CIR) is reconstructed based on cluster characteristics, which could match well with the multi-path component (MPC). The proposed method can be used to efficiently and accurately generate big data of mobile channel, which significantly reduces the computation time of RT-only.
Low-Rank Structured Clutter Covariance Matrix Estimation for Airborne STAP Radar
Authors: Tao Zhang, Haifang Zheng, Qijun Luo
Date: 2023-01-27 00:00:00

In space-time adaptive processing (STAP) of the airborne radar system, it is very important to realize sparse restoration of the clutter covariance matrix with a small number of samples. In this paper, a clutter suppression method for airborne forward-looking array radar based on joint statistics and structural priority is proposed, which can estimate the clutter covariance matrix in the case of small samples. Assuming that the clutter covariance matrix obeys the inverse Wishart prior distribution, the maximum posterior estimate is obtained by using the low-rank symmetry of the matrix itself. The simulation results based on the radar forward-looking array model show that compared with the traditional covariance matrix estimation method, the proposed method can effectively improve the clutter suppression performance of airborne radar while efficiently calculating.
Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment
Authors: Jiangtao Liu, Kai Wu, Tao Su, J. Andrew Zhang
Date: 2023-01-27 00:00:00

Joint radar and communications (JRC) can realize two radio frequency (RF) functions using one set of resources, greatly saving hardware, energy and spectrum for wireless systems needing both functions. Frequency-hopping (FH) MIMO radar is a popular candidate for JRC, as the achieved communication symbol rate can greatly exceed radar pulse repetition frequency. However, practical transceiver imperfections can fail many existing theoretical designs. In this work, we unveil for the first time the non-trivial impact of hardware imperfections on FH-MIMO JRC and analytically model the impact. We also design new waveforms and, accordingly, develop a low-complexity algorithm to jointly estimate the hardware imperfections of unsynchronized receiver. Moreover, employing low-cost software-defined radios and commercial off-the-shelf (COTS) products, we build the first FH-MIMO JRC experiment platform with radar and communications simultaneously validated over the air. Corroborated by simulation and experiment results, the proposed designs achieves high performances for both radar and communications.
SMILE: Robust Network Localization via Sparse and Low-Rank Matrix Decomposition
Authors: Lillian Clark, Sampad Mohanty, Bhaskar Krishnamachari
Date: 2023-01-26 00:00:00

Motivated by collaborative localization in robotic sensor networks, we consider the problem of large-scale network localization where location estimates are derived from inter-node radio signals. Well-established methods for network localization commonly assume that all radio links are line-of-sight and subject to Gaussian noise. However, the presence of obstacles which cause non-line-of-sight attenuation present distinct challenges. To enable robust network localization, we present Sparse Matrix Inference and Linear Embedding (SMILE), a novel approach which draws on both the well-known Locally Linear Embedding (LLE) algorithm and recent advances in sparse plus low-rank matrix decomposition. We demonstrate that our approach is robust to noisy signal propagation, severe attenuation due to non-line-of-sight, and missing pairwise measurements. Our experiments include simulated large-scale networks, an 11-node sensor network, and an 18-node network of mobile robots and static anchor radios in a GPS-denied limestone mine. Our findings indicate that SMILE outperforms classical multidimensional scaling (MDS) which ignores the effect of non-line of sight (NLOS), as well as outperforming state-of-the-art robust network localization algorithms that do account for NLOS attenuation including a graph convolutional network-based approach. We demonstrate that this improved accuracy is not at the cost of complexity, as SMILE sees reduced computation time for very large networks which is important for position estimation updates in a dynamic setting, e.g for mobile robots.
3D genome reconstruction from partially phased Hi-C data
Authors: Diego Cifuentes, Jan Draisma, Oskar Henriksson, Annachiara Korchmaros, Kaie Kubjas
Date: 2023-01-27 00:00:00
Tasks: 3D Reconstruction

The 3-dimensional (3D) structure of the genome is of significant importance for many cellular processes. In this paper, we study the problem of reconstructing the 3D structure of chromosomes from Hi-C data of diploid organisms, which poses additional challenges compared to the better-studied haploid setting. With the help of techniques from algebraic geometry, we prove that a small amount of phased data is sufficient to ensure finite identifiability, both for noiseless and noisy data. In the light of these results, we propose a new 3D reconstruction method based on semidefinite programming, paired with numerical algebraic geometry and local optimization. The performance of this method is tested on several simulated datasets under different noise levels and with different amounts of phased data. We also apply it to a real dataset from mouse X chromosomes, and we are then able to recover previously known structural features.
Effect of different selected food processing techniques on control release kinetics of encapsulated lycopene in simulated human gastrointestinal tract
Authors: Mohammad Anwar Ul Alam, Lamin Kassama
Date: 2023-01-26 00:00:00

Nanoencapsulation has become a widespread technique to improve the bioavailability of bioactive compounds like lycopene. Consumption of lycopene rich foods is effective in preventing cancer, diabetes, and cardiovascular diseases due to its strong, oxygen-quenching ability. But the functional activity of lycopene is compromised by light, oxygen, and heat. A biodegradable polymer such as PLA or PLGA is the most effective carrier for encapsulating lycopene due to its excellent biodegradability, biocompatibility, and nontoxigenic effect on the human metabolic system. Hence, the primary objective of this study was to evaluate the effect of pasteurization on bioaccessibility and control release kinetics of encapsulated lycopene nanoparticles in invitro human GIT. In the first objective, sonication time, surfactant, and polymer concentration were considered as the three factors to synthesize polymeric lycopene nanoparticles (LNP) whereas in the second objective, type of pasteurization, encapsulation, and juice concentration were three factors. In Objectives 4 and 5, the type of encapsulation and pasteurization, and digestion time were evaluated as the three factors used to evaluate the in-vitro bioaccessibility of encapsulated lycopene NP. The study evidenced that encapsulation improved the lycopene's bioaccessibility by 70% and more than 60% for conventional pasteurized (CP) and microwave pasteurized (MP) nanoemulsions, respectively without compromising the physicochemical properties of both PLA and PLGA-lycopene nanoparticles. The invitro bioaccessibility study also showed that CP reduced the functional activity of PLALNP by 20% whereas MP had no significant effect on the bioaccessibility of PLA LNP. It is unlikely that the PLGA LNP was more sensitive against MP nanoemulsion than the CP. In conclusion, it was shown that the PLA LNP treated with MP provided the highest bioaccessibility.
Automating Knowledge-Driven Model Recommendation: Methodology, Evaluation, and Key Challenges
Authors: Adam A. Butchy, Cheryl A. Telmer, Natasa Miskov-Zivanov
Date: 2023-01-26 00:00:00

There is significant interest in using existing repositories of biological entities, relationships, and models to automate biological model assembly and extension. Current methods aggregate human-curated biological information into executable, simulatable models, but these models do not resemble human curated models and do not recapitulate experimental results. Here, we outline the process of automated model assembly and extension, while demonstrating it on both synthetic models and human-curated models of biological signaling networks. We begin with an iterative, greedy, and combinatoric approach to automated assembly and demonstrate the key difficulties inherent to contextless assembly. We publicly release the software used in this paper to enable further exploration of this problem.