pwc-archive/papers-with-abstracts huggingface dataset:
Online Demodulation and Trigger for Flux-ramp Modulated SQUID Signals
Authors: N. Karcher, T. Muscheid, T. Wolber, D. Richter, C. Enss, S. Kempf, O. Sander
Date: 2021-10-22 00:00:00
Tasks: Event Detection

Due to the periodic characteristics of SQUIDs, a suitable linearization technique is required for SQUID-based readout. Flux-ramp modulation is a common linearization technique and is typically applied for the readout of a microwave-SQUID-multiplexer as well as since recently also for dc-SQUIDs. Flux-ramp modulation requires another stage in the signal processing chain to demodulate the SQUID output signal before further processing. For cryogenic microcalorimenters, these events are given by fast exponentially rising and slowly exponentially decaying pulses which shall be detected by a trigger engine and recorded by a storage logic. Since the data rate can be decreased significantly by demodulation and event detection, it is desirable to do both steps on the deployed fast FPGA logic during measurement before passing the data to a general-purpose processor. In this contribution, we show the implementation of efficient multi-channel flux-ramp demodulation computed at run-time on a SoC-FPGA. Furthermore, a concept and implementation for an online trigger and buffer mechanism with its theoretical trigger loss rates depending on buffer size is presented. Both FPGA modules can be operated with up to 500 MHz clock frequency and can efficiently process 32 channels. Correct functionality and data reduction capability of the modules are demonstrated in measurements utilizing magnetic microcalorimeter irradiated with an Iron-55 source for event generation and read out by a microwave SQUID multiplexer.
Precision of morphogen-driven tissue patterning during development is enhanced through contact-mediated cellular interactions
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Sitabhra Sinha
Date: 2021-10-25 00:00:00

Embryonic development involves pattern formation characterized by the emergence of spatially localized domains characterized by distinct cell fates resulting from differential gene expression. The boundaries demarcating these domains are precise and consistent within a species despite stochastic fluctuations in the morphogen molecular concentration that provides positional information to the cells, as well as, the intrinsic noise in molecular processes that interpret this information to guide fate determination. We show that local interactions between physically adjacent cells mediated by receptor-ligand binding utilizes the asymmetry between the fate-determining genes to yield a switch-like response to the global signal provided by the morphogen. This results in robust developmental outcomes with a consistent identity of the gene that is dominantly expressed at each cellular location, thereby substantially reducing the uncertainty in the location of the boundary between distinct fates.
Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
Authors: Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske
Date: 2021-10-22 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning

High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.
Reconfigurable and Real-Time Nyquist OTDM Demultiplexing in Silicon Photonics
Authors: Arijit Misra, Karanveer Singh, Janosch Meier, Christian Kress, Tobias Schwabe, Stefan Preußler, J. Christoph Scheytt, Thomas Schneider
Date: 2021-10-19 00:00:00
Tasks: channel selection

We demonstrate for the first time, to the best of our knowledge, reconfigurable and real-time orthogonal time-domain demultiplexing of coherent multilevel Nyquist signals in silicon photonics. No external pulse source is needed and frequencytime coherence is used to sample the incoming Nyquist OTDM signal with orthogonal sinc-shaped Nyquist pulse sequences using Mach-Zehnder modulators. All the parameters such as bandwidth and channel selection are completely tunable in the electrical domain. The feasibility of this scheme is demonstrated through a demultiplexing experiment over the entire C-band (1530 nm - 1550 nm), employing 24 Gbaud Nyquist QAM signals due to experimental constraints on the transmitter side. However, the silicon Mach-Zehnder modulator with a 3-dB bandwidth of only 16 GHz can demultiplex Nyquist pulses of 90 GHz optical bandwidth suggesting a possibility to reach symbol rates up to 90 GBd in an integrated Nyquist transceiver.
Real-Time Wave Mitigation for Water-Air OWC Systems Via Beam Tracking
Authors: Yujie Di, Yingjie Shao, Lian-Kuan Chen
Date: 2021-10-16 00:00:00

In a water-air optical wireless communication (OWC) channel, dynamic ocean waves may significantly deflect the light beam from its original direction, thus deteriorating the communication performance. In this letter, a beam tracking system to mitigate wave-induced communication degradation is experimentally demonstrated. By employing the beam tracking on the PAM6 system, a maximum of 486% improvement (from 140 Mb/s to 820 Mb/s) in throughput is realized at a 1.2-m air distance for an average wave slope changing rate of 0.34 rad/s. For PAM4 signals, the packet loss rate reduces significantly from 75% to 11%, and a maximum throughput of 1.25 Gb/s is achieved.
Deep learning-based design of broadband GHz complex and random metasurfaces
Authors: Tianning Zhang, Chun Yun Kee, Yee Sin Ang, L. K. Ang
Date: 2021-09-16 00:00:00

We are interested to explore the limit in using deep learning (DL) to study the electromagnetic response for complex and random metasurfaces, without any specific applications in mind. For simplicity, we focus on a simple pure reflection problem of a broadband electromagnetic (EM) plane wave incident normally on such complex metasurfaces in the frequency regime of 2 to 12 GHz. In doing so, we create a deep learning (DL) based framework called metasurface design deep convolutional neural network (MSDCNN) for both the forward and inverse design of three different classes of complex metasurfaces: (a) Arbitrary connecting polygons, (b) Basic pattern combination, and (c) Fully random binary patterns. The performance of each metasurface is evaluated and cross-benchmarked. Dependent on the type of complex metasurfaces, sample size, and DL algorithms used, MSDCNN is able to provide good agreements and can be a faster design tool for complex metasurfaces as compared to the traditional full-wave electromagnetic simulation methods. However, no single universal deep convolutional neural network (DCNN) model can work well for all metasurface classes based on detailed statistical analysis (such as mean, variance, kurtosis, mean squared error). Our findings report important information on the advantages and limitation of current DL models in designing these ultimately complex metasurfaces.
Radical pairs may explain reactive oxygen species-mediated effects of hypomagnetic field on neurogenesis
Authors: Rishabh, Hadi Zadeh-Haghighi, Dennis Salahub, Christoph Simon
Date: 2021-10-25 00:00:00
Tasks: Hippocampus, Triplet

Exposures to a hypomagnetic field can affect biological processes. Recently, it has been observed that hypomagnetic field exposure can adversely affect adult hippocampal neurogenesis and hippocampus-dependent cognition in mice. In the same study, the role of reactive oxygen species (ROS) in hypomagnetic field effects has been demonstrated. However, the mechanistic reasons behind this effect are not clear. This study proposes a radical pair mechanism based on a flavin-superoxide radical pair to explain the modulation of ROS production and the attenuation of adult hippocampal neurogenesis in a hypomagnetic field. The results of our calculations favor a singlet-born radical pair over a triplet-born radical pair. Our model predicts hypomagnetic field effects on the triplet/singlet yield of comparable strength as the effects observed in experimental studies on adult hippocampal neurogenesis. Our predictions are also in qualitative agreement with experimental results on superoxide concentration and other observed ROS effects. We also predict the effects of applied magnetic fields and oxygen isotopic substitution on adult hippocampal neurogenesis. Our findings strengthen the idea that nature might harness quantum resources in the context of the brain.
Functional instrumental variable regression with an application to estimating the impact of immigration on native wages
Authors: Dakyung Seong, Won-Ki Seo
Date: 2021-10-25 00:00:00
Tasks: regression

Functional linear regression gets its popularity as a statistical tool to study the relationship between function-valued response and exogenous explanatory variables. However, in practice, it is hard to expect that the explanatory variables of interest are perfectly exogenous, due to, for example, the presence of omitted variables and measurement error. Despite its empirical relevance, it was not until recently that this issue of endogeneity was studied in the literature on functional regression, and the development in this direction does not seem to sufficiently meet practitioners' needs; for example, this issue has been discussed with paying particular attention on consistent estimation and thus distributional properties of the proposed estimators still remain to be further explored. To fill this gap, this paper proposes new consistent FPCA-based instrumental variable estimators and develops their asymptotic properties in detail. Simulation experiments under a wide range of settings show that the proposed estimators perform considerably well. We apply our methodology to estimate the impact of immigration on native wages.
Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects
Authors: Whitney K. Newey, Sami Stouli
Date: 2020-09-04 00:00:00

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
Slow Movers in Panel Data
Authors: Yuya Sasaki, Takuya Ura
Date: 2021-10-22 00:00:00

Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). In the presence of many slow movers, conventional econometric methods can fail to work. We propose a novel method of robust inference for the average partial effects in correlated random coefficient models robustly across various distributions of within-variations, including the cases with many stayers and/or many slow movers in a unified manner. In addition to this robustness property, our proposed method entails smaller biases and hence improves accuracy in inference compared to existing alternatives. Simulation studies demonstrate our theoretical claims about these properties: the conventional 95% confidence interval covers the true parameter value with 37-93% frequencies, whereas our proposed one achieves 93-96% coverage frequencies.
Computational Efficiency in Multivariate Adversarial Risk Analysis Models
Authors: Michael Macgregor Perry, Hadi El-Amine
Date: 2021-10-25 00:00:00
Tasks: Computational Efficiency, Decision Making

In this paper we address the computational feasibility of the class of decision theoretic models referred to as adversarial risk analyses (ARA). These are models where a decision must be made with consideration for how an intelligent adversary may behave and where the decision-making process of the adversary is unknown, and is elicited by analyzing the adversary's decision problem using priors on his utility function and beliefs. The motivation of this research was to develop a computational algorithm that can be applied across a broad range of ARA models; to the best of our knowledge, no such algorithm currently exists. Using a two-person sequential model, we incrementally increase the size of the model and develop a simulation-based approximation of the true optimum where an exact solution is computationally impractical. In particular, we begin with a relatively large decision space by considering a theoretically continuous space that must be discretized. Then, we incrementally increase the number of strategic objectives which causes the decision space to grow exponentially. The problem is exacerbated by the presence of an intelligent adversary who also must solve an exponentially large decision problem according to some unknown decision-making process. Nevertheless, using a stylized example that can be solved analytically we show that our algorithm not only solves large ARA models quickly but also accurately selects to the true optimal solution. Furthermore, the algorithm is sufficiently general that it can be applied to any ARA model with a large, yet finite, decision space.
Analyzing a Complex Game for the South China Sea Fishing Dispute using Response Surface Methodologies
Authors: Michael Macgregor Perry
Date: 2021-10-25 00:00:00

The South China Sea (SCS) is one of the most economically valuable resources on the planet, and as such has become a source of territorial disputes between its bordering nations. Among other things, states compete to harvest the multitude of fish species in the SCS. In an effort to gain a competitive advantage states have turned to increased maritime patrols, as well as the use of "maritime militias," which are fishermen armed with martial assets to resist the influence of patrols. This conflict suggests a game of strategic resource allocation where states allocate patrols intelligently to earn the greatest possible utility. The game, however, is quite computationally challenging when considering its size (there are several distinct fisheries in the SCS), the nonlinear nature of biomass growth, and the influence of patrol allocations on costs imposed on fishermen. Further, uncertainty in player behavior attributed to modeling error requires a robust analysis to fully capture the dispute's dynamics. To model such a complex scenario, this paper employs a response surface methodology to assess optimal patrolling strategies and their impact on realized utilities. The methodology developed successfully finds strategies which are more robust to behavioral uncertainty than a more straight-forward method.
Housing property rights and social integration of migrant population: based on the 2017 china migrants' dynamic survey
Authors: Jingwen Tan, Shixi Kang
Date: 2021-10-24 00:00:00

Push-pull theory, one of the most important macro theories in demography, argues that population migration is driven by a combination of push (repulsive) forces at the place of emigration and pull (attractive) forces at the place of emigration. Based on the push-pull theory, this paper shows another practical perspective of the theory by measuring the reverse push and pull forces from the perspective of housing property rights. We use OLS and sequential Probit models to analyze the impact of urban and rural property rights factors on the social integration of the migrant population-based, on "China Migrants' Dynamic Survey". We found that after controlling for personal and urban characteristics, there is a significant negative effect of rural property rights (homestead) ownership of the mobile population on their socio-economic integration, and cultural and psychological integration in the inflow area. The effect of urban house price on social integration of the migrant population is consistent with the "inverted U-shaped" nonlinear assumption: when the house price to income ratio of the migrant population in the inflow area increases beyond the inflection point, its social integration level decreases. That is, there is an inverse push force and pull force mechanism of housing property rights on population mobility.
Reciprocity or community: Different cultural pathways to cooperation and welfare
Authors: Anna Gunnthorsdottir, Palmar Thorsteinsson
Date: 2021-10-22 00:00:00

In a laboratory experiment we compare voluntary cooperation in Iceland and the US. We furthermore compare the associated thought processes across cultures. The two countries have similar economic performance, but survey measures show that they differ culturally. Our hypotheses are based on two such measures, The Inglehart cultural world map and the Knack and Keefers scale of civic attitudes toward large-scale societal functioning. We prime the participants with different social foci, emphasizing in one a narrow grouping and in the other a larger social unit. In each country we implement this using two different feedback treatments. Under group feedback, participants only know the contributions by the four members of their directly cooperating group. Under session feedback they are informed of the contributions within their group as well as by everyone else in the session. Under group feedback, cooperation levels do not differ between the two cultures. However, under session feedback cooperation levels increase in Iceland and decline in the US. Even when contribution levels are the same members of the two cultures differ in their motives to cooperate: Icelanders tend to cooperate unconditionally and US subjects conditionally. Our findings indicate that different cultures can achieve similar economic and societal performance through different cultural norms and suggest that cooperation should be encouraged through culturally tailored suasion tactics. We also find that some decision factors such as Inequity Aversion do not differ across the two countries, which raises the question whether they are human universals.
The Impact of the Coronavirus Pandemic on New York City Real Estate: First Evidence
Authors: Jeffrey P. Cohen, Felix L. Friedt, Jackson P. Lautier
Date: 2021-10-22 00:00:00

We investigate whether pandemic-induced contagion disamenities and income effects arising due to COVID-related unemployment adversely affected real estate prices of one- or two-family owner-occupied properties across New York City (NYC). First, OLS hedonic results indicate that greater COVID case numbers are concentrated in neighborhoods with lower-valued properties. Second, we use a repeat-sales approach for the period 2003 to 2020, and we find that both the possibility of contagion and pandemic-induced income effects adversely impacted home sale prices. Estimates suggest sale prices fell by roughly $60,000 or around 8% in response to both of the following: 1,000 additional infections per 100,000 residents; and a 10-percentage point increase in unemployment in a given Modified Zip Code Tabulation Area (MODZCTA). These price effects were more pronounced during the second wave of infections. Based on cumulative MODZCTA infection rates through 2020, the estimated COVID-19 price discount ranged from approximately 1% to 50% in the most affected neighborhoods, and averaged 14%. The contagion effect intensified in the more affluent, but less densely populated NYC neighborhoods, while the income effect was more pronounced in the most densely populated neighborhoods with more rental properties and greater population shares of foreign-born residents. This disparity implies the pandemic may have been correlated with a wider gap in housing wealth in NYC between homeowners in lower-priced and higher-priced neighborhoods.
How To Sell (or Procure) in a Sequential Auction
Authors: Kenneth Hendricks, Thomas Wiseman
Date: 2021-10-25 00:00:00

A seller with one unit of a good faces N\geq3 buyers and a single competitor who sells one other identical unit in a second-price auction with a reserve price. Buyers who do not get the seller's good will compete in the competitor's subsequent auction. We characterize the optimal mechanism for the seller in this setting. The first-order approach typically fails, so we develop new techniques. The optimal mechanism features transfers from buyers with the two highest valuations, allocation to the buyer with the second-highest valuation, and a withholding rule that depends on the highest two or three valuations. It can be implemented by a modified third-price auction or a pay-your-bid auction with a rebate. This optimal withholding rule raises significantly more revenue than would a standard reserve price. Our analysis also applies to procurement auctions. Our results have implications for sequential competition in mechanisms.
On the Behavioral Consequences of Reverse Causality
Authors: Ran Spiegler
Date: 2021-10-23 00:00:00

Reverse causality is a common causal misperception that distorts the evaluation of private actions and public policies. This paper explores the implications of this error when a decision maker acts on it and therefore affects the very statistical regularities from which he draws faulty inferences. Using a quadratic-normal parameterization and applying the Bayesian-network approach of Spiegler (2016), I demonstrate the subtle equilibrium effects of a certain class of reverse-causality errors, with illustrations in diverse areas: development psychology, social policy, monetary economics and IO. In particular, the decision context may protect the decision maker from his own reverse-causality causal error. That is, the cost of reverse-causality errors can be lower for everyday decision makers than for an outside observer who evaluates their choices.