pwc-archive/papers-with-abstracts huggingface dataset:
Z3RO Precoder Canceling Nonlinear Power Amplifier Distortion in Large Array Systems
Authors: François Rottenberg, Gilles Callebaut, Liesbet Van der Perre
Date: 2021-10-15 00:00:00

Large array-based transmission uses the combination of a massive number of antenna elements and clever precoder designs to achieve array gain and spatially multiplex different users. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs). However, this reduces energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders, such as \gls{mrt}, induces a coherent combining of distortion at the user location, degrading the signal quality. In this work, a linear precoder is proposed that allows working close to saturation while canceling the coherent combining of the third order nonlinear \gls{pa} distortion at the user location. In contrast to other solutions, the zero third-order distortion (Z3RO) precoder does not require prior knowledge of the signal statistics and the PA model. The design consists of saturating a single or a few antennas on purpose together with an opposite phase shift to compensate for the distortion of all other antennas. The resulting array gain penalty becomes negligible as the number of base station antennas grows large.
Linear Channel Estimation Based on a Low-Bandwidth Observation Channel with Unknown Response
Authors: Juan I. Bonetti, James Kunst, Damián A. Morero, Mario R. Hueda
Date: 2021-10-14 00:00:00

We propose a novel system identification technique, based on a least-mean square algorithm, allowing for the estimation of a linear channel by using an unknown-response measurement channel. The key of the technique is a memoryless nonlinear function working as uncoupling block between the estimated and observation channels, conforming a Wiener-Hammerstein scheme. We prove that this estimation, only differing from the actual channel response by a scaling factor and a temporal shift, does not depend on the observation channel bandwidth. As a consequence, this technique enables the usage of low-cost measurement devices as feedback channel. We present numerical examples of the method, supporting the proposal and displaying excellent results.
A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data
Authors: Fankun Bu, Rui Cheng, Zhaoyu Wang
Date: 2021-10-14 00:00:00
Tasks: Time Series, Time Series Analysis

As the cost of the residential solar system decreases, rooftop photovoltaic (PV) has been widely integrated into distribution systems. Most rooftop PV systems are installed behind-the-meter (BTM), i.e., only the net demand is metered, while the native demand and PV generation are not separately recorded. Under this condition, the PV generation and native demand are invisible to utilities, which brings challenges for optimal distribution system operation and expansion. In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from the known hourly net demand data recorded by smart meters: 1) At the aggregate level, the proposed approach separates the total PV generation and native demand time series from the total net demand time series for customers with PVs. 2) At the customer level, the separated aggregate-level PV generation is allocated to individual PVs. These two layers leverage the spatial correlations of native demand and PV generation, respectively. One primary advantage of our proposed approach is that it is more independent and practical compared to previous works because it does not require PV array parameters, meteorological data and previously recorded solar power exemplars. We have verified our proposed approach using real native demand and PV generation data.
cellanneal: A User-Friendly Deconvolution Software for Omics Data
Authors: Lisa Buchauer, Shalev Itzkovitz
Date: 2021-10-15 00:00:00

We introduce cellanneal, a python-based software for deconvolving bulk RNA sequencing data. cellanneal relies on the optimization of Spearman's rank correlation coefficient between experimental and computational mixture gene expression vectors using simulated annealing. cellanneal can be used as a python package or via a command line interface, but importantly also provides a simple graphical user interface which is distributed as a single executable file for user convenience. The python package is available at https://github.com/LiBuchauer/cellanneal , the graphical software can be downloaded at http://shalevlab.weizmann.ac.il/resources .
Analysis and visualization of spatial transcriptomic data
Authors: Boxiang Liu, Yanjun Li, Liang Zhang
Date: 2021-10-15 00:00:00

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.
Phase transitions in when feedback is useful
Authors: Lokesh Boominathan, Xaq Pitkow
Date: 2021-10-15 00:00:00

Sensory observations about the world are invariably ambiguous. Inference about the world's latent variables is thus an important computation for the brain. However, computational constraints limit the performance of these computations. These constraints include energetic costs for neural activity and noise on every channel. Efficient coding is one prominent theory that describes how such limited resources can best be used. In one incarnation, this leads to a theory of predictive coding, where predictions are subtracted from signals, reducing the cost of sending something that is already known. This theory does not, however, account for the costs or noise associated with those predictions. Here we offer a theory that accounts for both feedforward and feedback costs, and noise in all computations. We formulate this inference problem as message-passing on a graph whereby feedback serves as an internal control signal aiming to maximize how well an inference tracks a target state while minimizing the costs of computation. We apply this novel formulation of inference as control to the canonical problem of inferring the hidden scalar state of a linear dynamical system with Gaussian variability. The best solution depends on architectural constraints, such as Dale's law, the ubiquitous law that each neuron makes solely excitatory or inhibitory postsynaptic connections. This biological structure can create asymmetric costs for feedforward and feedback channels. Under such conditions, our theory predicts the gain of optimal predictive feedback and how it is incorporated into the inference computation. We show that there is a non-monotonic dependence of optimal feedback gain as a function of both the computational parameters and the world dynamics, leading to phase transitions in whether feedback provides any utility in optimal inference under computational constraints.
Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2
Authors: Anyou Wang
Date: 2021-10-14 00:00:00
Tasks: Time Series Analysis

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fr\'echet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic. Full text and detailed on https://combai.org/ai/covidgenome/
Control of diffusion-driven pattern formation behind a wave of competency
Authors: Yue Liu, Philip K. Maini, Ruth E. Baker
Date: 2021-10-15 00:00:00

In certain biological contexts, such as the plumage patterns of birds and stripes on certain species of fishes, pattern formation takes place behind a so-called "wave of competency". Currently, the effects of a wave of competency on the patterning outcome is not well-understood. In this study, we use Turing's diffusion-driven instability model to study pattern formation behind a wave of competency, under a range of wave speeds. Numerical simulations show that in one spatial dimension a slower wave speed drives a sequence of peak splittings in the pattern, whereas a higher wave speed leads to peak insertions. In two spatial dimensions, we observe stripes that are either perpendicular or parallel to the moving boundary under slow or fast wave speeds, respectively. We argue that there is a correspondence between the one- and two-dimensional phenomena, and that pattern formation behind a wave of competency can account for the pattern organization observed in many biological systems.
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification
Authors: Chris McKennan, Zhe Sang, Yi Shi
Date: 2021-10-15 00:00:00
Tasks: Model Selection

Nanobodies are small antibody fragments derived from camelids that selectively bind to antigens. These proteins have marked physicochemical properties that support advanced therapeutics, including treatments for SARS-CoV-2. To realize their potential, bottom-up proteomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been proposed to identify antigen-specific nanobodies at the proteome scale, where a critical component of this pipeline is matching nanobody peptides to their begotten tandem mass spectra. While peptide-spectrum matching is a well-studied problem, we show the sequence similarity between nanobody peptides violates key assumptions necessary to infer nanobody peptide-spectrum matches (PSMs) with the standard target-decoy paradigm, and prove these violations beget inflated error rates. To address these issues, we then develop a novel framework and method that treats peptide-spectrum matching as a Bayesian model selection problem with an incomplete model space, which are, to our knowledge, the first to account for all sources of PSM error without relying on the aforementioned assumptions. In addition to illustrating our method's improved performance on simulated and real nanobody data, our work demonstrates how to leverage novel retention time and spectrum prediction tools to develop accurate and discriminating data-generating models, and, to our knowledge, provides the first rigorous description of MS/MS spectrum noise.
Convergence of regularized particle filters for stochastic reaction networks
Authors: Zhou Fang, Ankit Gupta, Mustafa Khammash
Date: 2021-10-14 00:00:00

Filtering for stochastic reaction networks (SRNs) is an important problem in systems/synthetic biology aiming to estimate the state of unobserved chemical species. A good solution to it can provide scientists valuable information about the hidden dynamic state and enable optimal feedback control. Usually, the model parameters need to be inferred simultaneously with state variables, and a conventional particle filter can fail to solve this problem accurately due to sample degeneracy. In this case, the regularized particle filter (RPF) is preferred to the conventional ones, as the RPF can mitigate sample degeneracy by perturbing particles with artificial noise. However, the artificial noise introduces an additional bias to the estimate, and, thus, it is questionable whether the RPF can provide reliable results for SRNs. In this paper, we aim to identify conditions under which the RPF converges to the exact filter in the filtering problem determined by a bimolecular network. First, we establish computationally efficient RPFs for SRNs on different scales using different dynamical models, including the continuous-time Markov process, tau-leaping model, and piecewise deterministic process. Then, by parameter sensitivity analyses, we show that the established RPFs converge to the exact filters if all reactions leading to an increase of the molecular population have linearly growing propensities and some other mild conditions are satisfied simultaneously. This ensures the performance of the RPF for a large class of SRNs, and several numerical examples are presented to illustrate our results.
Application of Sequence Embedding in Protein Sequence-Based Predictions
Authors: Nabil Ibtehaz, Daisuke Kihara
Date: 2021-10-14 00:00:00
Tasks: Multiple Sequence Alignment, Position, Prediction

In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix. Recently, inspired by the development in natural language processing, several applications of sequence embedding have been observed. Here, we review different approaches of protein sequence embeddings and their applications including protein contact prediction, secondary structure, prediction, and function prediction.
Effective reduction of biofilm through photothermal therapy by gold core@shell based mesoporous silica nanoparticles
Authors: Ana Garcia, Blanca Gonzalez, Catherine Harvey, Isabel Izquierdo-Barba, Maria Vallet-Regi
Date: 2021-10-15 00:00:00

Bacterial biofilms can initiate chronic infections that become difficult to eradicate. There is an unmet need for effective therapeutic strategies that control and inhibit the growth of these biofilms. Herein, light sensitive mesoporous silica nanoparticles (MSNs) with photothermal (PTT) and antimicrobial combined capabilities have been developed. These nanosystems have high therapeutic potential to affect the bacterial biofilm architecture and subsequently inhibit its growth. Nucleation of gold nanorods followed by the growth of a silica shell leads to a core@shell design (AuNR@MSN) with PTT properties. Incorporation of nitrosothiol groups (-SNO) with a heat liable linker, enables an enhanced nitric oxide release upon photothermal stimulation with near infrared radiation. Further loading of an antimicrobial molecule such as the levofloxacin (LEVO) antibiotic creates a unique nanoassembly with potential therapeutic efficacy against Staphylococcus aureus bacterial biofilms. A dispersion rate of the bacterial biofilm was evident when light stimuli is applied because impregnation of the nitrosothiol functionalized nanosystem with the antibiotic LEVO led to ca. 30% reduction but its illumination with near infrared (NIR) irradiation showed a biofilm reduction of ca. 90%, indicating that localized antimicrobial exposure and PTT improves the therapeutic efficacy. These findings envision the conception of near-infraredactivated nanoparticle carriers capable of combined therapy upon NIR irradiation, which enables photothermal therapy, together with the release of levofloxacin and nitric oxide to disrupt the integrity of bacterial biofilms and achieve a potent antimicrobial therapy.
$β$-Carotene bioavailability and conversion efficiency are significantly affected by sex in rats. First observation suggesting a possible hormetic regulation of vitamin A metabolism in female rats
Authors: Patrick Borel, Romane Troadec, Morgane Damiani, Charlotte Halimi, Marion Nowicki, Philippe Guichard, Marielle Margier, Julien Astier, Michel Grino, Emmanuelle Reboul, Jean-françois Landrier
Date: 2021-10-14 00:00:00

Scope: To study the effect of variation in dietary vitamin A (VA) content on its hepatic and intestinal metabolism. Methods and results: Adult female and male rats were fed with diets containing 400, 2300, or 9858 IU/kg VA for 31-33 weeks. VA concentrations were measured in plasma and liver. Bioavailability and intestinal conversion efficiency of $\beta$-carotene to VA were assessed by measuring postprandial plasma $\beta$-carotene and retinyl palmitate concentrations after force-feeding rats with $\beta$-carotene. Expression of genes involved in VA metabolism, together with concentrations of RBP4, BCO1 and SR-BI proteins, were measured in the intestine and liver of female rats. Plasma retinol concentrations were lower and hepatic free retinol concentrations were higher in females than in males. There was no effect of dietary VA content on $\beta$-carotene bioavailability and its conversion efficiency, but bioavailability was higher and conversion efficiency was lower in females than in males. The expression of most genes exhibited a U-shaped dose response curve depending on VA intake. Main conclusions: $\beta$-Carotene bioavailability and conversion efficiency to VA are affected by the sex of rats. Results of gene expression suggest a hormetic regulation of VA metabolism in female rats.
Chaotic Fitness Dependent Optimizer for Planning and Engineering Design
Authors: Hardi M. Mohammed, Tarik A. Rashid
Date: 2021-08-21 00:00:00

Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.
An Independent Study of Reinforcement Learning and Autonomous Driving
Authors: Hanzhi Yang
Date: 2021-08-20 00:00:00
Tasks: Autonomous Driving, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in autonomous driving and its combination with safety check constraints (safety controllers). We trained a rough autonomous driving agent using highway-gym environment and explored the effects of various environment configurations like reward functions on the agent training performance.
Tensor-to-Image: Image-to-Image Translation with Vision Transformers
Authors: Yiğit Gündüç
Date: 2021-10-06 00:00:00
Tasks: Image-to-Image Translation, Translation

Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformer-based custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification.
Simplest Streaming Trees
Authors: Haoyin Xu, Jayanta Dey, Sambit Panda, Joshua T. Vogelstein
Date: 2021-10-16 00:00:00
Tasks: Continual Learning, Transfer Learning

Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, especially on tabular data. However, most of the current implementations only operate in batch mode, and therefore cannot incrementally update when more data arrive. Several previous works developed streaming trees and ensembles to overcome this limitation. Nonetheless, we found that those state-of-the-art algorithms suffer from a number of drawbacks, including low accuracy on some problems and high memory usage on others. We therefore developed the simplest possible extension of decision trees: given new data, simply update existing trees by continuing to grow them, and replace some old trees with new ones to control the total number of trees. In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations. On those datasets, we also demonstrate that our approach often performs as well, and sometimes even better, than conventional batch decision forest algorithm. Thus, SDFs establish a simple standard for streaming trees and forests that could readily be applied to many real-world problems.