pwc-archive/papers-with-abstracts huggingface dataset:
Correlation based Imaging for rotating satellites
Authors: Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
Date: 2021-11-01 00:00:00

We consider imaging of fast moving small objects in space, such as low earth orbit satellites, which are also rotating around a fixed axis. The imaging system consists of ground based, asynchronous sources of radiation and several passive receivers above the dense atmosphere. We use the cross-correlation of the received signals to reduce distortions from ambient medium fluctuations. Imaging with correlations also has the advantage of not requiring any knowledge about the probing pulse and depends weakly on the emitter positions. We account for the target's orbital velocity by introducing the necessary Doppler compensation. To image a fast rotating object we also need to compensate for the rotation. We show that the rotation parameters can be extracted directly from the auto-correlation of the data before the formation of the image. We then investigate and analyze an imaging method that relies on backpropagating the cross-correlation data structure to two points rather than one, thus forming an interference matrix. The proposed imaging method consists of estimating the reflectivity as the top eigenvector of the migrated cross-correlation data interference matrix. We call this the rank-1 image and show that it provides superior image resolution compared to the usual single-point migration scheme for fast moving and rotating objects. Moreover, we observe a significant improvement in resolution due to the rotation leading to a diffraction limited resolution. We carry out a theoretical analysis that illustrates the role of the two point migration method as well as that of the inverse aperture and rotation in improving resolution. Extensive numerical simulations support the theoretical results.
Fluctuation relations and fitness landscapes of growing cell populations
Authors: Arthur Genthon, David Lacoste
Date: 2020-04-09 00:00:00
Tasks: Relation

We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independently of the model used to generate divisions and growth in the cell population. These relations lead to estimators of the population growth rate, which can be very efficient as we demonstrate by an analysis of a set of mother machine data. These fluctuation relations lead to general and important inequalities between the mean number of divisions and the doubling time of the population. We also study the fitness landscape, a concept based on the two samplings mentioned above, which quantifies the correlations between a phenotypic trait of interest and the number of divisions. We obtain explicit results when the trait is the age or the size, for age and size-controlled models.
Global cell-cell communication enables spatial segregation of cells in organoids of the inner cell mass
Authors: Simon Schardt, Sabine C. Fischer
Date: 2021-11-02 00:00:00

During development, cell fates are determined through a combination of intracellular transcriptional regulations and extracellular signaling. As a result, spatial patterns of different cell types arise. We investigate the decision between epiblast and primitive endoderm cells in the inner cell mass of the preimplantation mouse embryo. Our computational model uses global cell signaling for the pattern formation. By varying the signal dispersion, cell type arrangements ranging from a checkerboard to an engulfing pattern can be generated. Pair correlation functions provide a well-suited way of characterizing the model output. With these, we established a quantitative comparison between the simulation results and experimental data of inner cell mass organoids. We obtained an astonishing agreement. Thus, our model proves its capability to replicate the cell differentiation patterns, making global signaling a strong contender to explain pattern formation in the preimplantation embryo.
Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
Authors: Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
Date: 2021-10-29 00:00:00

In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special case). We show that c-HQMMs are equivalent to a constrained tensor network (more precisely, circular Local Purified State with positive-semidefinite decomposition) model. This equivalence enables us to provide an efficient learning model for c-HQMMs. The proposed learning approach is evaluated on six real datasets and demonstrates the advantage of c-HQMMs on multiple datasets as compared to HQMMs, circular HMMs, and HMMs.
Central Limit Theory for Models of Strategic Network Formation
Authors: Konrad Menzel
Date: 2021-11-02 00:00:00

We provide asymptotic approximations to the distribution of statistics that are obtained from network data for limiting sequences that let the number of nodes (agents) in the network grow large. Network formation is permitted to be strategic in that agents' incentives for link formation may depend on the ego and alter's positions in that endogenous network. Our framework does not limit the strength of these interaction effects, but assumes that the network is sparse. We show that the model can be approximated by a sampling experiment in which subnetworks are generated independently from a common equilibrium distribution, and any dependence across subnetworks is captured by state variables at the level of the entire network. Under many-player asymptotics, the leading term of the approximation error to the limiting model established in Menzel (2015b) is shown to be Gaussian, with an asymptotic bias and variance that can be estimated consistently from a single network.
Integrated Assessment Modeling of Korea 2050 Carbon Neutrality Technology Pathways
Authors: Hanwoong Kim, Haewon McJeon, Dawoon Jung, Hanju Lee, Candelaria Bergero, Jiyong Eom
Date: 2021-11-02 00:00:00

This integrated assessment modeling research analyzes what Korea's 2050 carbon neutrality would require for the national energy system and the role of the power sector concerning the availability of critical mitigation technologies. Our scenario-based assessments show that Korea's current policy falls short of what the nation's carbon-neutrality ambition would require. Across all technology scenarios examined in this study, extensive and rapid energy system transition is imperative, requiring the large-scale deployment of renewables and carbon capture & storage (CCS) early on and negative emission technologies (NETs) by the mid-century. Importantly, rapid decarbonization of the power sector that goes with rapid electrification of end-uses seems to be a robust national decarbonization strategy. Furthermore, we contextualize our net-zero scenario results using policy costs, requirements for natural resources, and the expansion rate of zero-carbon technologies. We find that the availability of nuclear power lowers the required expansion rate of renewables and CCS, alleviating any stress on terrestrial and geological systems. By contrast, the limited availability of CCS without nuclear power necessarily demands a very high penetration of renewables and significantly high policy compliance costs, which would decrease the feasibility of achieving the carbon neutrality target.
A General Revealed Preference Test for Quasilinear Preferences: Theory and Experiments
Authors: Mikhail Freer, Marco Castillo
Date: 2021-11-01 00:00:00

We provide a generalized revealed preference test for quasilinear preferences. The test applies to nonlinear budget sets and non-convex preferences as those found in taxation and nonlinear pricing contexts. We study the prevalence of quasilinear preferences in a laboratory real-effort task experiment with nonlinear wages. The experiment demonstrates the empirical relevance of our test. We find support for either convex (non-separable) preferences or quasilinear preferences but weak support for the hypothesis of both quasilinear and convex preferences.
Antenna Optimization for WBAN Based on Spherical Wave Functions De-Embedding
Authors: Lukas Berkelmann, Hendrik Jäschke, Leonardo Mörlein, Lukas Grundmann, Dirk Manteuffel
Date: 2021-11-02 00:00:00

Antennas for wireless body area networks (WBAN) need to be modeled with adapted methods because the coupling with the body tissue does not allow for a clear separation between antenna and channel. Especially for dynamically varying on-body channels due to changing body poses, e.g. with head-worn antennas, modeling is challenging and design goals for optimal antennas are difficult to determine. Therefore, in this paper, the modeling of WBAN channels using spherical wave functions (SWF) is utilized for antenna de-embedding and for deriving optimal antenna characteristics that maximize the transmission coefficient for the respective channel. It is evaluated how typical factors influencing WBAN channels (different body anatomies, body postures, and varying positions of the communication nodes), can be modeled statistically with SWF. An optimized antenna design is developed based on the derived optimization method, specifically adapted to the channel of on-body links with eye-wear applications. The results with the optimized antenna are compared to other standard antenna designs and validated against measurements.
Terahertz-Band Non-Orthogonal Multiple Access: System- and Link-Level Considerations
Authors: Ahmed Magbool, Hadi Sarieddeen, Nour Kouzayha, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2021-11-02 00:00:00

Non-orthogonal multiple access (NOMA) communications promise high spectral efficiency and massive connectivity, serving multiple users over the same time-frequency-code resources. Higher data rates and massive connectivity are also achieved by leveraging wider bandwidths at higher frequencies, especially in the terahertz (THz) band. This work investigates the prospects and challenges of combining these algorithmic and spectrum enablers in THz-band NOMA communications. We consider power-domain NOMA coupled with successive interference cancellation at the receiver, focusing on multiple-input multiple-output (MIMO) systems as antenna arrays are crucial for THz communications. On the system level, we study the scalability of THz-NOMA beamforming, clustering, and spectrum/power allocation algorithms and motivate stochastic geometry techniques for performance analysis and system modeling. On the link level, we highlight the challenges in channel estimation and data detection and the constraints on computational complexity. We further illustrate future research directions. When properly configured and given sufficient densification, THz-band NOMA communications can significantly improve the performance and capacity of future wireless networks.
Beyond Point Clouds: A Knowledge-Aided High Resolution Imaging Radar Deep Detector for Autonomous Driving
Authors: Ruxin Zheng, Shunqiao Sun, David Scharff, Teresa Wu
Date: 2021-11-01 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

The potentials of automotive radar for autonomous driving have not been fully exploited. We present a multi-input multi-output (MIMO) radar transmit and receive signal processing chain, a knowledge-aided approach exploiting the radar domain knowledge and signal structure, to generate high resolution radar range-azimuth spectra for object detection and classification using deep neural networks. To achieve waveform orthogonality among a large number of transmit antennas cascaded by four automotive radar transceivers, we propose a staggered time division multiplexing (TDM) scheme and velocity unfolding algorithm using both Chinese remainder theorem and overlapped array. Field experiments with multi-modal sensors were conducted at The University of Alabama. High resolution radar spectra were obtained and labeled using the camera and LiDAR recordings. Initial experiments show promising performance of object detection using an image-oriented deep neural network with an average precision of 96.1% at an intersection of union (IoU) of typically 0.5 on 2,000 radar frames.
An immunological autobiography: my year as a COVID-19 vaccine trial participant
Authors: Ross M. Kedl
Date: 2021-11-01 00:00:00

I present here longitudinal evaluation of T and B cell immunity to SARS-CoV2 and variants of concern (VOC) from a single subject (me) over an entire year post vaccination. After enrolling in the Moderna phase III clinical trial, I collected my own biological samples pre- and post-immunization in the event of being a recipient of the experimental vaccine. The evidence strongly supports the conclusion that I did not receive the placebo. The analysis is admittedly limited to an n of 1, but the results fit well with data taken from published works and represent one of the more comprehensive longitudinal evaluations of vaccine-elicited immunity within a single individual yet to be undertaken. Though the data amount to a well-documented anecdote, given its granularity, it is not without its insights and may be of further use in directing future longitudinal studies that have actual statistical significance.
Eigen's Paradox and the Quasispecies Model in a Non-Archimedean Framework
Authors: W. A. Zúñiga-Galindo
Date: 2021-11-02 00:00:00

In this article we present a new p-adic generalization of the Eigen-Schuster model where the genomes (sequences) are represented by words written in the alphabet 0,1,,p-1, where p is a prime number, with a time variable length. The time evolution of the concentration of a sequence is controlled by a p-adic evolution equation. The long term behavior of the concentration of a sequence depends on a fitness function f, a mutation measure Q, and an initial concentration distribution. The new model provides essentially two types of asymptotic scenarios for evolution. If the complexity of sequences grows at the right pace, then in the long term the survival is assured. This agrees with the fact that larger genome size improves the replication fidelity. In other case, the sequences cannot copy themselves with sufficiently fidelity, and in the long term they will not survive. Eigen's paradox is one, among the infinitely many, possible scenarios of the evolution in the long term. The mathematical formulation of this fact requires solving the Cauchy problem for the p-adic Eigen-Schuster model in a rigorous mathematical way. This requires imposing restrictions on the fitness function and on the mutation measure, among other conditions. The study of the mentioned initial value problem requires techniques of p-adic wavelets and heat kernels developed in the last 35 years.
Spectra of Reduced Fractals and Their Applications in Biology
Authors: Diana T. Pham, Zdzislaw E. Musielak
Date: 2021-11-02 00:00:00

Fractals with different levels of self-similarity and magnification are defined as reduced fractals. It is shown that spectra of these reduced fractals can be constructed and used to describe levels of complexity of natural phenomena. Specific applications to biological systems, such as green algae, are performed, and it is suggested that the obtained spectra can be used to classify the considered algae by identifying spectra associated with them. The ranges of these spectra for green algae are determined and their extension to other biological as well as other natural systems is proposed.
Optimal allocation to deferred income annuities
Authors: F. Habib, H. Huang, A. Mauskopf, B. Nikolic, T. S. Salisbury
Date: 2021-11-01 00:00:00

In this paper we employ a lifecycle model that uses utility of consumption and bequest to determine an optimal Deferred Income Annuity (DIA) purchase policy. We lay out a mathematical framework to formalize the optimization process. The method and implementation of the optimization is explained, and the results are then analyzed. We extend our model to control for asset allocation and show how the purchase policy changes when one is allowed to vary asset allocation. Our results indicate that (i.) refundable DIAs are less appealing than non-refundable DIAs because of the loss of mortality credits; (ii.) the DIA allocation region is larger under the fixed asset allocation strategy due to it becoming a proxy for fixed-income allocation; and (iii.) when the investor is allowed to change asset-allocation, DIA allocation becomes less appealing. However, a case for higher DIA allocation can be made for those individuals who perceive their longevity to be higher than the population.
Refundable income annuities: Feasibility of money-back guarantees
Authors: Moshe A. Milevsky, Thomas S. Salisbury
Date: 2021-11-01 00:00:00

Refundable income annuities (IA), such as cash-refund and instalment-refund, differ in material ways from the life-only version beloved by economists. In addition to lifetime income they guarantee the annuitant or beneficiary will receive their money back albeit slowly over time. We document that refundable IAs now represent the majority of sales in the U.S., yet they are mostly ignored by insurance and pension economists. And, although their pricing, duration, and money's-worth-ratio is complicated by recursivity which will be explained, we offer a path forward to make refundable IAs tractable. A key result concerns the market price of cash-refund IAs, when the actuarial present value is grossed-up by an insurance loading. We prove that price is counterintuitively no longer a declining function of age and older buyers might pay more than younger ones. Moreover, there exists a threshold valuation rate below which no price is viable. This may also explain why inflation-adjusted IAs have all but disappeared.
Neural Program Generation Modulo Static Analysis
Authors: Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
Date: 2021-10-26 00:00:00
Proceeding: NeurIPS 2021 12

State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as the generation of entire method bodies. We propose to address this deficiency using weak supervision from a static program analyzer. Our neurosymbolic method allows a deep generative model to symbolically compute, using calls to a static-analysis tool, long-distance semantic relationships in the code that it has already generated. During training, the model observes these relationships and learns to generate programs conditioned on them. We apply our approach to the problem of generating entire Java methods given the remainder of the class that contains the method. Our experiments show that the approach substantially outperforms state-of-the-art transformers and a model that explicitly tries to learn program semantics on this task, both in terms of producing programs free of basic semantic errors and in terms of syntactically matching the ground truth.
Classifying YouTube Comments Based on Sentiment and Type of Sentence
Authors: Rhitabrat Pokharel, Dixit Bhatta
Date: 2021-10-31 00:00:00
Tasks: Sentence, Sentiment Analysis, Vocal Bursts Type Prediction

As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify the comments that best interest the content creators. In this paper, we extract and classify the raw comments into different categories based on both sentiment and sentence types that will help YouTubers find relevant comments for growing their viewership. Existing studies have focused either on sentiment analysis (positive and negative) or classification of sub-types within the same sentence types (e.g., types of questions) on a text corpus. These have limited application on non-traditional text corpus like YouTube comments. We address this challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. We evaluate each combination of statistical measure and the machine learning model using cross validation and $F_1$ scores. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.
An Explanation of In-context Learning as Implicit Bayesian Inference
Authors: Sang Michael Xie, aditi raghunathan, Percy Liang, Tengyu Ma
Date: 2021-11-03 00:00:00
Proceeding: ICLR 2022 4
Tasks: Few-Shot Learning, In-Context Learning, Language Modelling

Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning.
OpenPrompt: An Open-source Framework for Prompt-learning
Authors: Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun
Date: 2021-11-03 00:00:00
Proceeding: ACL 2022 5
Tasks: Prompt Learning

Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to $cloze$-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks. However, no standard implementation framework of prompt-learning is proposed yet, and most existing prompt-learning codebases, often unregulated, only provide limited implementations for specific scenarios. Since there are many details such as templating strategy, initializing strategy, and verbalizing strategy, etc. need to be considered in prompt-learning, practitioners face impediments to quickly adapting the desired prompt learning methods to their applications. In this paper, we present {OpenPrompt}, a unified easy-to-use toolkit to conduct prompt-learning over PLMs. OpenPrompt is a research-friendly framework that is equipped with efficiency, modularity, and extendibility, and its combinability allows the freedom to combine different PLMs, task formats, and prompting modules in a unified paradigm. Users could expediently deploy prompt-learning frameworks and evaluate the generalization of them on different NLP tasks without constraints. OpenPrompt is publicly released at {\url{ https://github.com/thunlp/OpenPrompt}}.