pwc-archive/papers-with-abstracts huggingface dataset:
Systematizing Cellular Complexity: A Hilbertian Approach To Biological Problems
Authors: Nima Dehghani
Date: 2022-10-05 00:00:00

Examining individual components of cellular systems has been successful in uncovering molecular reactions and interactions. However, the challenge lies in integrating these components into a comprehensive system-scale map. This difficulty arises due to factors such as missing links (unknown variables), overlooked nonlinearities in high-dimensional parameter space, downplayed natural noisiness and stochasticity, and a lack of focus on causal influence and temporal dynamics. Composite static and phenomenological descriptions, while appearing complicated, lack the essence of what makes the biological systems truly "complex". The formalization of system-level problems is therefore important in constructing a meta-theory of biology. Addressing fundamental aspects of cellular regulation, adaptability, and noise management is vital for understanding the robustness and functionality of biological systems. These aspects encapsulate the challenges that cells face in maintaining stability, responding to environmental changes, and harnessing noise for functionality. This work examines these key problems that cells must solve, serving as a template for such formalization and as a step towards the axiomatization of biological investigations. Through a detailed exploration of cellular mechanisms, particularly homeostatic configuration, ion channels and harnessing noise, this paper aims to illustrate complex concepts and theories in a tangible context, providing a bridge between abstract theoretical frameworks and concrete biological phenomena.
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Authors: Keir Adams, Connor W. Coley
Date: 2022-10-06 00:00:00
Tasks: Drug Design, valid

Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands. 3D deep generative models have the potential to automate this exploration of shape-conditioned 3D chemical space; however, no existing models can reliably generate valid drug-like molecules in conformations that adopt a specific shape such as a known binding pose. We introduce a new multimodal 3D generative model that enables shape-conditioned 3D molecular design by equivariantly encoding molecular shape and variationally encoding chemical identity. We ensure local geometric and chemical validity of generated molecules by using autoregressive fragment-based generation with heuristic bonding geometries, allowing the model to prioritize the scoring of rotatable bonds to best align the growing conformational structure to the target shape. We evaluate our 3D generative model in tasks relevant to drug design including shape-conditioned generation of chemically diverse molecular structures and shape-constrained molecular property optimization, demonstrating its utility over virtual screening of enumerated libraries.
An identification and testing strategy for proxy-SVARs with weak proxies
Authors: Giovanni Angelini, Giuseppe Cavaliere, Luca Fanelli
Date: 2022-10-10 00:00:00
Tasks: valid

When proxies (external instruments) used to identify target structural shocks are weak, inference in proxy-SVARs (SVAR-IVs) is nonstandard and the construction of asymptotically valid confidence sets for the impulse responses of interest requires weak-instrument robust methods. In the presence of multiple target shocks, test inversion techniques require extra restrictions on the proxy-SVAR parameters other those implied by the proxies that may be difficult to interpret and test. We show that frequentist asymptotic inference in these situations can be conducted through Minimum Distance estimation and standard asymptotic methods if the proxy-SVAR can be identified by using `strong' instruments for the non-target shocks; i.e. the shocks which are not of primary interest in the analysis. The suggested identification strategy hinges on a novel pre-test for the null of instrument relevance based on bootstrap resampling which is not subject to pre-testing issues, in the sense that the validity of post-test asymptotic inferences is not affected by the outcomes of the test. The test is robust to conditionally heteroskedasticity and/or zero-censored proxies, is computationally straightforward and applicable regardless of the number of shocks being instrumented. Some illustrative examples show the empirical usefulness of the suggested identification and testing strategy.
Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data
Authors: Kazuhiko Kakamu
Date: 2022-10-11 00:00:00

This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Therefore, the posterior distributions are close to the true distributions even when the different data generating process is accounted for. Moreover, promising results for Gini coefficients encouraged us to apply our method to real data from Japan. The empirical examples indicate two subgroups in Japan (2020) and the Gini coefficients' integrity.
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
Authors: Matias D. Cattaneo, Yingjie Feng, Filippo Palomba, Rocio Titiunik
Date: 2022-10-10 00:00:00
Tasks: Prediction Intervals, Uncertainty Quantification, valid

We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly different points in time. From a theoretical perspective, our uncertainty quantification methods improve on prior literature by (i) covering a large class of causal predictands in staggered adoption settings, (ii) allowing for synthetic control methods with possibly nonlinear constraints, (iii) proposing scalable robust conic optimization methods and principled data-driven tuning parameter selection, and (iv) offering valid uniform inference across post-treatment periods. We illustrate our methodology with an empirical application studying the effects of economic liberalization on real GDP per capita for Sub-Saharan African countries. Companion software packages are provided in Python, R, and Stata.
Policy Learning with New Treatments
Authors: Samuel Higbee
Date: 2022-10-10 00:00:00

I study the problem of a decision maker choosing a policy which allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear- and integer-programming formulation. I prove the maximum regret of the estimated policy converges to the lowest possible maximum regret at a rate which is the maximum of N^-1/2 and the rate at which conditional average treatment effects are estimated in the experimental data. In an application to designing targeted subsidies for electrical grid connections in rural Kenya, I find that nearly the entire population should be given a treatment not implemented in the experiment, reducing maximum regret by over 60% compared to the policy that restricts to the treatments implemented in the experiment.
On estimating Armington elasticities for Japan's meat imports
Authors: Satoshi Nakano, Kazuhiko Nishimura
Date: 2022-10-06 00:00:00
Tasks: regression

By fully accounting for the distinct tariff regimes levied on imported meat, we estimate substitution elasticities of Japan's two-stage import aggregation functions for beef, chicken and pork. While the regression analysis crucially depends on the price that consumers face, the post-tariff price of imported meat depends not only on ad valorem duties but also on tariff rate quotas and gate price system regimes. The effective tariff rate is consequently evaluated by utilizing monthly transaction data. To address potential endogeneity problems, we apply exchange rates that we believe to be independent of the demand shocks for imported meat. The panel nature of the data allows us to retrieve the first-stage aggregates via time dummy variables, free of demand shocks, to be used as part of the explanatory variable and as an instrument in the second-stage regression.
FX Resilience around the World: Fighting Volatile Cross-Border Capital Flows
Authors: Louisa Chen, Estelle Xue Liu, Zijun Liu
Date: 2022-10-10 00:00:00

We show that capital flow (CF) volatility exerts an adverse effect on exchange rate (FX) volatility, regardless of whether capital controls have been put in place. However, this effect can be significantly moderated by certain macroeconomic fundamentals that reflect trade openness, foreign assets holdings, monetary policy easing, fiscal sustainability, and financial development. Passing the threshold levels of these macroeconomic fundamentals, the adverse effect of CF volatility may be negligible. We further construct an intuitive FX resilience measure, which provides an assessment of the strength of a country's exchange rates.
Innovation with and without patents
Authors: Josef Taalbi
Date: 2022-10-08 00:00:00

A long-standing discussion is to what extent patents can be used to monitor trends in innovation activity. This study quantifies the amount and quality of information about actual innovation contained in the patent system, based on 4,460 Swedish innovations (1970-2015) that have been matched to international patents. The results show that most innovations were not patented and that among those that were, 43.9% of all innovations, only a fraction can be identified with patent quality data. The best-performing models identify 17% of all information about innovations, equivalent to an information loss of at least 83%. Econometric tests also show that the fraction of innovations responding to strengthened patent laws during the period were on average 8% percent. The overlap between the patent and innovation systems is hence more modest than often assumed. This accentuates the need to, alongside patents, develop versatile approaches in order to induce and monitor various aspects of innovation.
An improved decomposition-based heuristic for truck platooning
Authors: Boshuai Zhao, Roel Leus
Date: 2022-10-11 00:00:00
Tasks: Scheduling

Truck platooning is a promising transportation mode in which several trucks drive together and thus save fuel consumption by suffering less air resistance. In this paper, we consider a truck platooning system for which we jointly optimize the truck routes and schedules from the perspective of a central platform. We improve an existing decomposition-based heuristic by Luo and Larson (2022), which iteratively solves a routing and scheduling problem, with a cost modification step after each scheduling run. We propose different formulations for the routing and the scheduling problem and embed these into Luo and Larson's framework, and we examine ways to improve their iterative process. In addition, we propose another scheduling heuristic to deal with large instances. The computational results show that our procedure achieves better performance than the existing one under certain realistic settings.
Making Information More Valuable
Authors: Mark Whitmeyer
Date: 2022-10-10 00:00:00

We study what changes to an agent's decision problem increase her value for information. We prove that information becomes more valuable if and only if the agent's reduced-form payoff in her belief becomes more convex. When the transformation corresponds to the addition of an action, the requisite increase in convexity occurs if and only if a simple geometric condition holds, which extends in a natural way to the addition of multiple actions. We apply these findings to two scenarios: a monopolistic screening problem in which the good is information and delegation with information acquisition.
Modeling of Energy Consumption and Streaming Video QoE using a Crowdsourcing Dataset
Authors: Christian Herglotz, Werner Robitza, Matthias Kränzler, André Kaup, Alexander Raake
Date: 2022-10-11 00:00:00

In the past decade, we have witnessed an enormous growth in the demand for online video services. Recent studies estimate that nowadays, more than 1% of the global greenhouse gas emissions can be attributed to the production and use of devices performing online video tasks. As such, research on the true power consumption of devices and their energy efficiency during video streaming is highly important for a sustainable use of this technology. At the same time, over-the-top providers strive to offer high-quality streaming experiences to satisfy user expectations. Here, energy consumption and QoE partly depend on the same system parameters. Hence, a joint view is needed for their evaluation. In this paper, we perform a first analysis of both end-user power efficiency and Quality of Experience of a video streaming service. We take a crowdsourced dataset comprising 447,000 streaming events from YouTube and estimate both the power consumption and perceived quality. The power consumption is modeled based on previous work which we extended towards predicting the power usage of different devices and codecs. The user-perceived QoE is estimated using a standardized model. Our results indicate that an intelligent choice of streaming parameters can optimize both the QoE and the power efficiency of the end user device. Further, the paper discusses limitations of the approach and identifies directions for future research.
Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
Authors: Alexandre Tissier, Wassim Hamidouche, Souhaiel Belhadj Dit Mdalsi, Jarno Vanne, Franck Galpin, Daniel Menard
Date: 2021-03-09 00:00:00

The next-generation Versatile Video Coding (VVC) standard introduces a new Multi-Type Tree (MTT) block partitioning structure that supports Binary-Tree (BT) and Ternary-Tree (TT) splits in both vertical and horizontal directions. This new approach leads to five possible splits at each block depth and thereby improves the coding efficiency of VVC over that of the preceding High Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT) partitioning with a single split per block depth. However, MTT also has brought a considerable impact on encoder computational complexity. In this paper, a two-stage learning-based technique is proposed to tackle the complexity overhead of MTT in VVC intra encoders. In our scheme, the input block is first processed by a Convolutional Neural Network (CNN) to predict its spatial features through a vector of probabilities describing the partition at each 4x4 edge. Subsequently, a Decision Tree (DT) model leverages this vector of spatial features to predict the most likely splits at each block. Finally, based on this prediction, only the N most likely splits are processed by the Rate-Distortion (RD) process of the encoder. In order to train our CNN and DT models on a wide range of image contents, we also propose a public VVC frame partitioning dataset based on existing image dataset encoded with the VVC reference software encoder. Our proposal relying on the top-3 configuration reaches 46.6% complexity reduction for a negligible bitrate increase of 0.86%. A top-2 configuration enables a higher complexity reduction of 69.8% for 2.57% bitrate loss. These results emphasis a better trade-off between VTM intra coding efficiency and complexity reduction compared to the state-of-the-art solutions.
Quantification of Pollen Viability in Lantana camara By Digital Holographic Microscopy
Authors: Vipin Kumar, Nishant Goyal, Abhishek Prasad, Suresh Babu, Kedar Khare, Gitanjali Yadav
Date: 2022-10-10 00:00:00

Pollen grains represent the male gametes of seed plants and their viability is critical for efficient sexual reproduction in the plant life cycle. Pollen analysis is used in diverse research thematics to address a range of botanical, ecological and geological questions. More recently it has been recognized that pollen may also be a vector for transgene escape from genetically modified crops, and the importance of pollen viability in invasion biology has also been emphasized. In this work, we analyse and report an efficient visual method for assessing the viability of pollen using digital holographic microscopy (DHM). We test this method on pollen grains of the invasive Lantana camara, a well known plant invader known to most of the tropical world. We image pollen grains and show that the quantitative phase information provided by the DHM technique can be readily related to the chromatin content of the individual cells and thereby to pollen viability. Our results offer a new technique for pollen viability assessment that does not require staining, and can be applied to a number of emerging areas in plant science.
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion
Authors: Unni V. S., Pravin Nair, Kunal N. Chaudhury
Date: 2022-10-09 00:00:00
Tasks: Image Restoration, Pansharpening

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network
Authors: Bulla Rajesh, Nandakishore Dusa, Mohammed Javed, Shiv Ram Dubey, P. Nagabhushan
Date: 2022-10-01 00:00:00
Tasks: Computational Efficiency, Generative Adversarial Network, Image Generation

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language Processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper.
Unsupervised classification of the spectrogram zeros
Authors: Juan M. Miramont, François Auger, Marcelo A. Colominas, Nils Laurent, Sylvain Meignen
Date: 2022-10-11 00:00:00
Tasks: Classification, Denoising

The zeros of the spectrogram have proven to be a relevant feature to describe the time-frequency structure of a signal, originated by the destructive interference between components in the time-frequency plane. In this work, a classification of these zeros in three types is introduced, based on the nature of the components that interfere to produce them. Echoing noise-assisted methods, a classification algorithm is proposed based on the addition of independent noise realizations to build a 2D histogram describing the stability of zeros. Features extracted from this histogram are later used to classify the zeros using a non-supervised clusterization algorithm. A denoising approach based on the classification of the spectrogram zeros is also introduced. Examples of the classification of zeros are given for synthetic and real signals, as well as a performance comparison of the proposed denoising algorithm with another zero-based approach.
Data-driven Enhancement of the Time-domain First-order Regular Perturbation Model
Authors: Astrid Barreiro, Gabriele Liga, Alex Alvarado
Date: 2022-10-11 00:00:00

A normalized batch gradient descent optimizer is proposed to improve the first-order regular perturbation coefficients of the Manakov equation, often referred to as kernels. The optimization is based on the linear parameterization offered by the first-order regular perturbation and targets enhanced low-complexity models for the fiber channel. We demonstrate that the optimized model outperforms the analytical counterpart where the kernels are numerically evaluated via their integral form. The enhanced model provides the same accuracy with a reduced number of kernels while operating over an extended power range covering both the nonlinear and highly nonlinear regimes. A $6-7$~dB gain, depending on the metric used, is obtained with respect to the conventional first-order regular perturbation.
Racial Disparities in Pulse Oximetry Cannot Be Fixed With Race-Based Correction
Authors: Neal Patwari, Di Huang, Kiki Bonetta-Misteli
Date: 2022-10-10 00:00:00

Studies have shown pulse oximeter measurements of blood oxygenation have statistical bias that is a function of race, which results in higher rates of occult hypoxemia, i.e., missed detection of dangerously low oxygenation, in patients of color. This paper further characterizes the statistical distribution of pulse ox measurements, showing they also have a higher variance for patients racialized as Black, compared to those racialized as white. We show that no single race-based correction factor will provide equal performance in the detection of hypoxemia. The results have implications for racially equitable pulse oximetry.