pwc-archive/papers-with-abstracts huggingface dataset:
CV4Code: Sourcecode Understanding via Visual Code Representations
Authors: Ruibo Shi, Lili Tao, Rohan Saphal, Fran Silavong, Sean J. Moran
Date: 2022-05-11 00:00:00
Tasks: Lexical Analysis, Retrieval

We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from the code snippet by treating each snippet as a two-dimensional image, which naturally encodes the context and retains the underlying structural information through an explicit spatial representation. To codify snippets as images, we propose an ASCII codepoint-based image representation that facilitates fast generation of sourcecode images and eliminates redundancy in the encoding that would arise from an RGB pixel representation. Furthermore, as sourcecode is treated as images, neither lexical analysis (tokenisation) nor syntax tree parsing is required, which makes the proposed method agnostic to any particular programming language and lightweight from the application pipeline point of view. CV4Code can even featurise syntactically incorrect code which is not possible from methods that depend on the Abstract Syntax Tree (AST). We demonstrate the effectiveness of CV4Code by learning Convolutional and Transformer networks to predict the functional task, i.e. the problem it solves, of the source code directly from its two-dimensional representation, and using an embedding from its latent space to derive a similarity score of two code snippets in a retrieval setup. Experimental results show that our approach achieves state-of-the-art performance in comparison to other methods with the same task and data configurations. For the first time we show the benefits of treating sourcecode understanding as a form of image processing task.
Balancing at the edge of excitability: Implications for cell movement
Authors: Debojyoti Biswas, Parijat Banerjee, Pablo A. Iglesias
Date: 2022-05-18 00:00:00

Cells rely on the ability to sense and respond to small spatial differences in chemoattractant concentrations for survival. There is growing evidence that this is accomplished by setting the signaling system near the threshold for activation in an excitable system and using the spatial heterogeneities to alter the threshold thereby biasing cell activity in the direction of the gradient. Here we consider a scheme by which the set point is adaptively set near the bifurcation point, but without explicit knowledge of this point. Through simulation, we show that the method would improve chemotactic efficiency of cells. The results of this paper are based on pioneering work by Eduardo Sontag and coworkers, to whom this paper is dedicated in honor of his 70th birthday.
Ensuring the Safety of Uncertified Linear State-Feedback Controllers via Switching
Authors: Yiwen Lu, Yilin Mo
Date: 2022-05-18 00:00:00

Sustained research efforts have been devoted to learning optimal controllers for linear stochastic dynamical systems with unknown parameters, but due to the corruption of noise, learned controllers are usually uncertified in the sense that they may destabilize the system. To address this potential instability, we propose a "plug-and-play" modification to the uncertified controller which falls back to a known stabilizing controller when the norm of the state exceeds a certain threshold. We show that the switching strategy enhances the safety of the uncertified controller by making the linear-quadratic cost bounded even if the underlying linear feedback gain is destabilizing. We also prove the near-optimality of the proposed strategy by quantifying the maximum performance loss caused by switching as asymptotically negligible. Finally, we demonstrate the effectiveness of the proposed switching strategy using simulation on an industrial process example.
An Improved Adaptive Smo for Speed Estimation of Sensorless Dsfoc Induction Motor Drives and Stability Analysis using Lyapunov Theorem at Low Frequencies
Authors: Appalabathula Venkatesh
Date: 2022-05-18 00:00:00

In this paper, An Improved Adaptive Sliding Mode Observer (ASMO) is proposed to a Sensorless DSFOC Induction Motor Drives and their stability is analyzed. ASMO is used to estimate the Rotor Speed, Rotor Resistance, Flux, Stator and Rotor currents and the developed electromagnetic Torques.To improve the robustness and accuracy of an adaptive SMO during very low frequency operation, the sliding mode flux observer(SMFO) uses independent gains as the correction terms. The gains of current and rotor flux SMOs are designed using Lyapunov stability theory to guarantee the stability and fast convergence of the estimated variables. In this paper concentrated on Simulink Blocks and their graphs are analyzed with the help of mathematical approach. Also, comparison of results with the basic conventional controllers are done and the results proved that the proposed ASMO method shows excellent Transient and Steady state speed estimation by the Adaptive Estimators, particularly at low frequencies.
Bi-Phasic Quasistatic Brain Communication for Fully Untethered Connected Brain Implants
Authors: Baibhab Chatterjee, Mayukh Nath, Gaurav Kumar K, Shulan Xiao, Krishna Jayant, Shreyas Sen
Date: 2022-05-18 00:00:00
Tasks: Blocking

Wireless communication using electro-magnetic (EM) fields acts as the backbone for information exchange among wearable devices around the human body. However, for Implanted devices, EM fields incur high amount of absorption in the tissue, while alternative modes of transmission including ultrasound, optical and magneto-electric methods result in large amount of transduction losses due to conversion of one form of energy to another, thereby increasing the overall end-to-end energy loss. To solve the challenge of powering and communication in a brain implant with low end-end channel loss, we present Bi-Phasic Quasistatic Brain Communication (BP-QBC), achieving < 60dB worst-case end-to-end channel loss at a channel length of 55mm, by avoiding the transduction losses during field-modality conversion. BP-QBC utilizes dipole coupling based signal transmission within the brain tissue using differential excitation in the transmitter and differential signal pick-up at the receiver, and offers 41X lower power w.r.t. traditional Galvanic Human Body Communication at a carrier frequency of 1MHz, by blocking any DC current paths through the brain tissue. Since the electrical signal transfer through the human tissue is electro-quasistatic up to several 10's of MHz range, BP-QBC allows a scalable (bps-10Mbps) duty-cycled uplink from the implant to an external wearable. The power consumption in the BP-QBC TX is only 0.52uW at 1Mbps (with 1% duty cycling), which is within the range of harvested body-coupled power in the downlink from an external wearable to the brain implant. Furthermore, BP-QBC eliminates the need for sub-cranial repeaters, as it utilizes quasi-static electrical signals, thereby avoiding any transduction losses. Such low end-to-end channel loss with high data rates would find applications in neuroscience, brain-machine interfaces, electroceuticals and connected healthcare.
Comparison of Deep Space Navigation Using Optical Imaging, Pulsar Time-of-Arrival Tracking, and/or Radiometric Tracking
Authors: Todd Ely, Shyam Bhaskaran, Nicholas Bradley, T. Joseph W. Lazio, Tomas Martin-Mur
Date: 2022-05-17 00:00:00
Tasks: Autonomous Navigation

Recent advances with space navigation technologies developed by NASA in space-based atomic clocks and pulsar X-ray navigation combined with past successes in autonomous navigation using optical imaging, brings to the forefront the need to compare space navigation using optical, radiometric, and pulsar-based measurements using a common set of assumptions and techniques. This review article examines these navigation data types in two different ways. First, a simplified deep space orbit determination problem is posed that captures key features of the dynamics and geometry, and then each data type is characterized for its ability to solve for the orbit. The data types are compared and contrasted using a semi-analytical approach with geometric dilution of precision techniques. The results provide useful parametric insights into the strengths of each data type. In the second part of the paper, a high-fidelity, Monte Carlo simulation of a Mars cruise, approach, and entry navigation problem is studied. The results found complement the semi-analytic results in the first part, and illustrate specific issues such as each data type's quantitative impact on solution accuracy and their ability to support autonomous delivery to a planet.
Field Distortion Model Based on Fredholm Integral
Authors: Yunqi Sun, Jianfeng Zhou
Date: 2022-05-18 00:00:00
Tasks: model

Field distortion is widespread in imaging systems. If it cannot be measured and corrected well, it will affect the accuracy of photogrammetry. To this end, we proposed a general field distortion model based on Fredholm integration, which uses a reconstructed high-resolution reference point spread function (PSF) and two sets of 4-variable polynomials to describe an imaging system. The model includes the point-to-point positional distortion from the object space to the image space and the deformation of the PSF so that we can measure an actual field distortion with arbitrary accuracy. We also derived the formula required for correcting the sampling effect of the image sensor. Through numerical simulation, we verify the effectiveness of the model and reconstruction algorithm. This model will have potential application in high-precision image calibration, photogrammetry and astrometry.
Mathematical Characterization of Private and Public Immune Repertoire Sequences
Authors: Lucas Böttcher, Sascha Wald, Tom Chou
Date: 2022-05-18 00:00:00

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.
DL4DS -- Deep Learning for empirical DownScaling
Authors: Carlos Alberto Gomez Gonzalez
Date: 2022-05-07 00:00:00
Tasks: Deep Learning, Super-Resolution

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents DL4DS, Deep Learning for empirical DownScaling, a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality CAMS data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds
Clustering through pair interactions in swimming zooplankton
Authors: Ron Shnapp, François-Gaël Michalec, Markus Holzner
Date: 2022-05-18 00:00:00
Tasks: Clustering

This work focuses on the formation of mating aggregates in zooplankton. In particular, sexual encounters are behaviourally supported by males actively swimming in search for females, and approaching them for mating once they are found. While the random search leads to a diffusive flux of individuals, the approaching for encounter supports attraction. Thus, we ask whether these competing mechanisms of diffusion and attraction can support aggregation and lead to the formation of mating clusters. To answer our question we formulate a model in which particles performing random walks can briefly make contact with other particles if they are found within a particular distance from each other. Our analysis shows that this model supports clustering in a way analogous to the process of colloid aggregation. Following that, we analyze a dataset of 3D trajectories of swimming copepods and show that the results compare well with our model. These results support the hypothesis that pair-interactions promote mating aggregates in zooplankton and are sufficient to overcome the diffusive nature of their mate searching behavior. Our results are useful for understanding small-scale clustering of zooplankton, which is crucial for predicting encounter rates and reproduction rates in the ocean.
Spatial effects in parasite induced marine diseases of immobile hosts
Authors: Àlex Giménez-Romero, Federico Vazquez, Cristóbal López, Manuel A. Matías
Date: 2022-05-18 00:00:00

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns, calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. An analytical expression for the basic reproduction number of the spatial model is derived as function of the non-spatial counterpart, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system.
Treatment Choice with Nonlinear Regret
Authors: Toru Kitagawa, Sokbae Lee, Chen Qiu
Date: 2022-05-17 00:00:00
Tasks: regression, Sensitivity

The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of regret and show that singleton rules are not essentially complete for nonlinear regret. Focusing on mean square regret, we derive closed-form fractions for finite-sample Bayes and minimax optimal rules. Our approach is grounded in decision theory and extends to limit experiments. The treatment fractions can be viewed as the strength of evidence favoring treatment. We apply our framework to a normal regression model and sample size calculation.
Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context: A model-based analysis of generation, network, and storage investments
Authors: Mario Kendziorski, Leonard Göke, Christian von Hirschhausen, Claudia Kemfert, Elmar Zozmann
Date: 2022-05-18 00:00:00

In this paper, we explore centralized and more decentral approaches to succeed the energiewende in Germany, in the European context. We use the AnyMOD framework to model a future renewable-based European energy system, based on a techno-economic optimization, i.e. cost minimization with given demand, including both investment and the subsequent dispatch of capacity. The model includes 29 regions for European countries, and 38 NUTS-2 regions in Germany. First the entire energy system on the European level is optimized. Based on these results, the electricity system for the German regions is optimized to achieve great regional detail to analyse spatial effects. The model allows a comparison between a stylized central scenario with high amounts of wind offshore deployed, and a decentral scenario using mainly the existing grid, and thus relying more on local capacities. The results reveal that the cost for the second optimization of these two scenarios are about the same: The central scenario is characterized by network expansion in order to transport the electricity from the wind offshore sites, whereas the decentral scenario leads to more photovoltaic and battery deployment closer to the areas with a high demand for energy. A scenarios with higher energy efficiency and lower demand projections lead to a significant reduction of investment requirements, and to different localizations thereof.
Topology-dependence of propagation mechanisms in the production network
Authors: Eszter Molnár, Dénes Csala
Date: 2022-05-18 00:00:00

The topology of production networks determines the propagation mechanisms of local shocks and thus the co-movement of industries. As a result, we need a more precisely defined production network to model economic growth accurately. In this study, we analyse Leontief's input-output model from a network theory perspective, aiming to construct a production network in such a way that it allows the most accurate modelling of the propagation mechanisms of changes that generate industry growth. We do this by revisiting a prevalent threshold in the literature that determines industry-industry interdependence. Our hypothesis is that changing the threshold changes the topological structure of the network and the core industries to a large extent. This is significant, because if the production network topology is not precisely defined, the resulting internal propagation mechanisms will be distorted, and thus industry growth modelling will not be accurate. We prove our hypothesis by examining the network topology, and centrality metrics under different thresholds on a network derived from the US input-output accounts data for 2007 and 2012.
Revealed Incomplete Preferences
Authors: Kirby Nielsen, Luca Rigotti
Date: 2022-05-17 00:00:00

We elicit incomplete preferences over monetary gambles with subjective uncertainty. Subjects rank gambles, and these rankings are used to estimate preferences; payments are based on estimated preferences. About 40\% of subjects express incompleteness, but they do so infrequently. Incompleteness is similar for individuals with precise and imprecise beliefs, and in an environment with objective uncertainty, suggesting that it results from imprecise tastes more than imprecise beliefs. When we force subjects to choose, we observe more inconsistencies and preference reversals. Evidence suggests there is incompleteness that is indirectly revealed -- in up to 98\% of subjects -- in addition to what we directly measure.
Marx after Okishio: Falling Rate of Profit with Constant Rate of Exploitation
Authors: Deepankar Basu, Oscar Orellana
Date: 2022-05-18 00:00:00

Can cost-reducing technical change lead to a fall in the long run rate of profit if class struggle manages to keep the rate of exploitation constant? In a general circulating capital model, we derive sufficient conditions for cost-reducing technical change to both keep the rate of exploitation constant and lead to a fall in the equilibrium rate of profit. Further, if the real wage bundle is such that the maximum price-value ratio is larger than 1 plus the rate of exploitation, then starting from any configuration of technology and real wage, we can always find a viable, CU-LS technical change that satisfies the sufficient conditions for the previous result. Taken together, these results vindicate Marx's claim in Volume III of Capital, that if the rate of exploitation remains unchanged then viable, CU-LS technical change in capitalist economies can lead to a fall in the long run rate of profit.
Coherence-Based Frequency Subset Selection For Binaural RTF-Vector-Based Direction of Arrival Estimation for Multiple Speakers
Authors: Daniel Fejgin, Simon Doclo
Date: 2022-05-18 00:00:00
Tasks: Direction of Arrival Estimation

Recently, a method has been proposed to estimate the direction of arrival (DOA) of a single speaker by minimizing the frequency-averaged Hermitian angle between an estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors. In this paper, we extend this method to multi-speaker localization by introducing the frequency-averaged Hermitian angle spectrum and selecting peaks of this spatial spectrum. To construct the Hermitian angle spectrum, we consider only a subset of frequencies, where it is likely that one speaker is dominant. We compare the effectiveness of the generalized magnitude squared coherence and two coherent-to-diffuse ratio (CDR) estimators as frequency selection criteria. Simulation results for estimating the DOAs of two speakers in a reverberant environment with diffuse-like babble noise using binaural hearing devices show that using the binaural effective-coherence-based CDR estimate as a frequency selection criterion yields the best performance.