pwc-archive/papers-with-abstracts huggingface dataset:
Energy-Efficient mm-Wave Backhauling via Frame Aggregation in Wide Area Networks
Authors: Varun Amar Reddy, Gordon L. Stüber, Suhail Al-Dharrab, Wessam Mesbah, Ali Hussein Muqaibel
Date: 2021-05-20 00:00:00

Wide area networks for surveying applications, such as seismic acquisition, have been witnessing a significant increase in node density and area, where large amounts of data have to be transferred in real-time. While cables can meet these requirements, they account for a majority of the equipment weight, maintenance, and labor costs. A novel wireless network architecture, compliant with the IEEE 802.11ad standard, is proposed for establishing scalable, energy-efficient, and gigabit-rate backhaul across very large areas. Statistical path-loss and line-of-sight models are derived using real-world topographic data in well-known seismic regions. Additionally, a cross-layer analytical model is derived for 802.11 systems that can characterize the overall latency and power consumption under the impact of co-channel interference. On the basis of these models, a Frame Aggregation Power-Saving Backhaul (FA-PSB) scheme is proposed for near-optimal power conservation under a latency constraint, through a duty-cycled approach. A performance evaluation with respect to the survey size and data generation rate reveals that the proposed architecture and the FA-PSB scheme can support real-time acquisition in large-scale high-density scenarios while operating with minimal power consumption, thereby enhancing the lifetime of wireless seismic surveys. The FA-PSB scheme can be applied to cellular backhaul and sensor networks as well.
Real-time estimation of phase and amplitude with application to neural data
Authors: Michael Rosenblum, Arkady Pikovsky, Andrea A. Kühn, Johannes L. Busch
Date: 2021-05-20 00:00:00

Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal's past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient's beta-band brain activity.
High Accuracy and Low Complexity Frequency Offset Estimation Based on All-Phase FFT for M-QAM Coherent Optical Systems
Authors: Qian Li
Date: 2021-05-15 00:00:00
Tasks: All

A low complexity frequency offset estimation algorithm based on all-phase FFT for M-QAM is proposed. Compared with two-stage algorithms such as FFT+CZT and FFT+ZoomFFT, our algorithm can lower computational complexity by 73% and 30% respectively, without loss of the estimation accuracy.
Combined Transmission and Distribution State-Estimation for Future Electric Grids
Authors: Amritanshu Pandey, Shimiao Li, Larry Pileggi
Date: 2021-05-14 00:00:00
Tasks: State Estimation

Proliferation of grid resources on the distribution network along with the inability to forecast them accurately will render the existing methodology of grid operation and control untenable in the future. Instead, a more distributed yet coordinated approach for grid operation and control will emerge that models and analyzes the grid with a larger footprint and deeper hierarchy to unify control of disparate T&D grid resources under a common framework. Such approach will require AC state-estimation (ACSE) of joint T&D networks. Today, no practical method for realizing combined T&D ACSE exists. This paper addresses that gap from circuit-theoretic perspective through realizing a combined T&D ACSE solution methodology that is fast, convex and robust against bad-data. To address daunting challenges of problem size (million+ variables) and data-privacy, the approach is distributed both in memory and computing resources. To ensure timely convergence, the approach constructs a distributed circuit model for combined T&D networks and utilizes node-tearing techniques for efficient parallelism. To demonstrate the efficacy of the approach, combined T&D ACSE algorithm is run on large test networks that comprise of multiple T&D feeders. The results reflect the accuracy of the estimates in terms of root mean-square error and algorithm scalability in terms of wall-clock time.
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia
Authors: Roonak Rezvani, Samaneh Kouchaki, Ramin Nilforooshan, David J. Sharp, Payam Barnaghi
Date: 2021-05-14 00:00:00
Tasks: Prognosis, Self-Supervised Learning

Interpreting the environmental, behavioural and psychological data from in-home sensory observations and measurements can provide valuable insights into the health and well-being of individuals. Presents of neuropsychiatric and psychological symptoms in people with dementia have a significant impact on their well-being and disease prognosis. Agitation in people with dementia can be due to many reasons such as pain or discomfort, medical reasons such as side effects of a medicine, communication problems and environment. This paper discusses a model for analysing the risk of agitation in people with dementia and how in-home monitoring data can support them. We proposed a semi-supervised model which combines a self-supervised learning model and a Bayesian ensemble classification. We train and test the proposed model on a dataset from a clinical study. The dataset was collected from sensors deployed in 96 homes of patients with dementia. The proposed model outperforms the state-of-the-art models in recall and f1-score values by 20%. The model also indicates better generalisability compared to the baseline models.
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Authors: Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Charles Tapley Hoyt, William L Hamilton
Date: 2021-05-17 00:00:00
Tasks: Drug Discovery, Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting.
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
Authors: Jenna Bilbrey, Logan Ward, Sutanay Choudhury, Neeraj Kumar, Ganesh Sivaraman
Date: 2021-05-07 00:00:00
Tasks: Drug Design, Drug Discovery, reinforcement-learning, Reinforcement Learning (RL)

We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on \icfifty. This generative framework\footnote{https://github.com/exalearn/covid-drug-design} will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates.
High rate of SARS-CoV2 nonsense spike genomes coding for prematurely truncated proteins
Authors: Alessio D'Alessandro
Date: 2021-05-21 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Replication of SARS-CoV2 virions is an error-prone process which may eventually generate a percentage of impaired protein copies with complete lack of functionality. For instance, after RNA mis-replication, a very premature stop codon (UAG, UAA, UGA) coding for a prematurely truncated (nonsense-mutated) spike protein may occur. In the natural virus replication process via cell infection, the nonsense genomes are corrected by the proofreading enzymes of the virus, strongly penalized by natural selection and condemned to a very short life by the host cell's mRNA watching mechanisms. However, for the very long spike genome of 1273 codons, a truncated non-functional spike protein may potentially still occur with a high frequency, even in presence of a low mutation rate per single nucleotide. With this paper, a hi-fidelity post-processing of SARS-CoV2 spike sequences is provided: in ex-vivo samples from patients, an impressively high rate of 26\% of prematurely-stopped (nonsense-mutated) spike genomes sequences due to insertions/deletions is found, compared with a 9.7\% obtained from in-vitro cell culture. A general warning on the possible high rate of prematurely-stopped spike protein sequences is also raised for "artificial" de novo DNA synthesis processes of SARS-CoV2 spike genomes with no associated natural proofreading/selection, possibly including vaccine preparations. Finally, a metric based on the ratio between prematurely stopped and "normal" genomes is proposed as a potential host-independent variant-watching tool, able to classify the infectivity of new spike mutations.
Correlation-invariant synaptic plasticity
Authors: Carlos Stein N. Brito, Wulfram Gerstner
Date: 2021-05-21 00:00:00
Tasks: Sensitivity

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. While existing synaptic plasticity models reproduce some of the observed receptive-field properties, a major obstacle is the sensitivity of Hebbian learning to omnipresent spurious correlations in cortical networks which can overshadow relevant latent input features. Here we develop a theory for synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, we show how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations, so that receptive fields become aligned with features hidden in higher-order statistics. Our simulations demonstrate how correlation-invariance enables biologically realistic models to develop sparse population codes, despite diverse levels of variability and heterogeneity. The theory advances our understanding of local unsupervised learning in cortical circuits and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.
Antibiotic resistance: Insights from evolution experiments and mathematical modeling
Authors: Gabriela Petrungaro, Yuval Mulla, Tobias Bollenbach
Date: 2021-05-21 00:00:00

Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.
Distribuicao e diversidade de herbaceas de sub-bosque em uma floresta de terra firme da amazonia meridional
Authors: Eliana Celestino da Paixao do Rodrigues dos Santos
Date: 2021-05-20 00:00:00
Tasks: Management, Sand

Environmental heterogeneity is a determining factor of the structure of biological communities. Thus, understanding the distribution of species along environmental gradients provides assistance to conservation. The goal of this study was to determine the distribution pattern of the herbaceous community in three areas of the Southern Amazon. Sampling was conducted in three modules totaling 39 permanent plots according to the protocol of collection of the Program for Research in Biodiversity. All herbaceous and ground hemiepiphyte subjects above 5 cm were recorded. Multivariate analyses were used to summarize the species composition, multiple regression models were used to determine if environmental variables and disturbance caused by logging influenced the composition of the herbaceous community. We recorded 7.965 individuals representing 70 species. The distance of the watercourse was the main factor associated with the distribution of the species, interactions between variables showed that canopy openness and sand content also influence the species composition, and there was no effect on the number of trees cut. Species richness increased in areas where canopy cover was higher and it decreases as it becomes more distant from the watercourse. The occurrences of preferred habitats for some species have, in addition to an ecological interest, a practical significance for the conservation and management of these species. Currently, the area of preservation of streams provided by the Forest Code in effect is 30 m for rivers up to 10 m wide. However, this study shows that the range of protection should be extended to at least 100 m wide.
On the risk of infection by infectious aerosols in large indoor spaces
Authors: Bardia Hejazi, Oliver Schlenczek, Birte Thiede, Gholamhossein Bagheri, Eberhard Bodenschatz
Date: 2021-05-20 00:00:00

Airborne diseases can be transmitted by infectious aerosols in the near field, i.e., in close proximity, or in the far field, i.e., by infectious aerosols that are well mixed within the indoor air. Is it possible to say which mode of disease transmission is predominant in large indoor spaces? We addressed this question by measuring the transport of aerosols equivalent to the size of human respiratory particles in two large hardware stores (V>10000 m$^3$). We found that aerosol concentrations in both stores decreased rapidly and almost independently of aerosol size, despite the different ventilation systems. A persistent and directional airflow on the order of a few cm/s was observed in both stores. Consequently, aerosol dynamics in such open settings can be expected to be dominated by turbulent dispersion and sweeping, and the accumulation of infectious aerosols in the indoor air is unlikely to contribute significantly to the risk of infection as long as the occupancy of the store is not too high. Under these conditions, well-fitting face masks are an excellent means of preventing disease transmission by human aerosols.
Dynamical models for metabolomics data integration
Authors: Polina Lakrisenko, Daniel Weindl
Date: 2021-05-21 00:00:00
Tasks: Data Integration

As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heterogeneous data and the analysis of dynamical phenotypes. Here, we review recent efforts in using dynamical metabolic models for data integration, focusing on approaches that are not restricted to steady-state measurements or that require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and that, although challenges remain, dynamical modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.
Cerium (III) and (IV) containing mesoporous glasses/alginate beads for bone regeneration: bioactivity, biocompatibility and reactive oxygen species activity
Authors: E. Varini, S. Sanchez-Salcedo, G. Malavasi, G. Lusvardi, M. Vallet-Regi, A. J. Salinas
Date: 2021-05-21 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A very small number of biomaterials investigated for bone regeneration was reported as able to prevent the oxidative stress. In this study beads based on alginate hydrogel and mesoporous glasses (MG) containing different amounts of cerium oxides (Ce3+/Ce4+) exhibiting antioxidant properties were investigated as a good approach to mimic the action of antioxidant enzymes in our organism. The effect of cerium contents on the bioactivity and biocompatibility of beads were investigated. Moreover, the potential capability of Ce-containing MG to prevent the oxidative stress caused by the activity of reactive oxygen species (ROS) was here investigated for the first time. The increment of cerium oxide from 1.2, to 3.6 and 5.3 mol-% decreases the surface area and porosity of MG and increases the catalase mimetic activity after 168 h. Swelling tests in different cell culture media (D- and {\alpha}-MEM) demonstrated the rehydration capability of beads. The presence of beads with the highest Ce-contents (3.6 and 5.3 %) improved the proliferation of pre-osteoblastic cells MC3T3-C1 cells. However, the cell differentiation decreased when increased the cerium content. Lactate dehydrogenase assays showed beads are cytocompatible materials. Moreover, oxidative stress tests with H2O2 showed a better response related to cell viability and the elimination of oxidant species when increased cerium content. Beads of glasses with 1.2 and 3.6 % of CeO2 are excellent candidates as bioactive scaffolds for bone regeneration capable of counteract the oxidative stress.
Mesoporous core-shell silica nanoparticles with anti-fouling properties for ovarian cancer therapy
Authors: Sandra Sanchez-Salcedo, Maria Vallet-Regi, Sophia Allaf Shanin, Carlotta A. Glackin, Jeffrey I. Zink
Date: 2021-05-21 00:00:00

Mesoporous silica nanoparticles (MSNPs) have many potential applications in biomedical fields. However, when MSNPs are exposed to plasma, protein adsorption leads to opsonization and decreases blood circulation time. A new multifunctional nanodevice based on polyethylenimine (PEI) coated core-shell Fe3O4@SiO2 MSNPs with a zwitterionic 2-methacryloyloxyethyl phosphorylcholine (MPC) surface was designed to minimize unspecific protein adhesion. Particle size measurements demonstrated an excellent non-fouling capacity in solutions containing Bovine Serum Albumin (BSA) and Fetal Bovine Serum (FBS) plasma proteins. The system was used in this study to co-deliver two different cargos: siRNA and daunorubicin. Anti-TWIST siRNA plays critical role in modulating knockdown of TWIST and sensitizing cells to chemotherapeutics such as daunorubicin for ovarian cancer therapy. The drug was released in response to externally controlled oscillating magnetic fields (OMF). siRNA (siGFP) silenced expression of green fluorescence protein (GFP) in Ovcar8 cancer cells, demonstrating the incorporation of core shell MSNPs into cells and siGFP delivery. The synergistic effect of the co-release of anti-TWIST-siRNA loaded in the PEI and daunorubicin loaded in NPs pores c aused increased cytotoxicity in Ovcar8 of up to 50% from both zwitteronic and non-zwitteronic NPs. The system is the first example of silencing by anti-TWITS-siRNA /daunorubicin co-delivered using zwitterionic core-shell nanoparticles with low-fouling adsorption. This engineered multifunctional approach may provide therapeutic potential for the treatment of currently incurable ovarian cancer.
A note on the CAPM with endogenously consistent market returns
Authors: Andreas Krause
Date: 2021-05-21 00:00:00

I demonstrate that with the market return determined by the equilibrium returns of the CAPM, expected returns of an asset are affected by the risks of all assets jointly. Another implication is that the range of feasible market returns will be limited and dependent on the distribution of weights in the market portfolio. A large and well diversified market with no dominating asset will only return zero while a market dominated by a small number of assets will only return the risk-free rate. In the limiting case of atomistic assets, we recover the properties of the standard CAPM.
Turnover-Adjusted Information Ratio
Authors: Feng Zhang, Xi Wang, Honggao Cao
Date: 2021-05-19 00:00:00
Tasks: Management

In this paper, we study the behavior of information ratio (IR) as determined by the fundamental law of active investment management. We extend the classic relationship between IR and its two determinants (i.e., information coefficient and investment "breadth") by explicitly and simultaneously taking into account the volatility of IC and the cost from portfolio turnover. Through mathematical derivations and simulations, we show that - for both mean-variance and quintile portfolios - a turnover-adjusted IR is always lower than an IR that ignores the cost from turnover; more importantly, we find that, contrary to the implication from the fundamental low but consistent with available empirical evidence, investment managers may improve their investment performance or IR by limiting/optimizing trade or portfolio turnover.
Data-driven Weight Initialization with Sylvester Solvers
Authors: Debasmit Das, Yash Bhalgat, Fatih Porikli
Date: 2021-05-02 00:00:00

In this work, we propose a data-driven scheme to initialize the parameters of a deep neural network. This is in contrast to traditional approaches which randomly initialize parameters by sampling from transformed standard distributions. Such methods do not use the training data to produce a more informed initialization. Our method uses a sequential layer-wise approach where each layer is initialized using its input activations. The initialization is cast as an optimization problem where we minimize a combination of encoding and decoding losses of the input activations, which is further constrained by a user-defined latent code. The optimization problem is then restructured into the well-known Sylvester equation, which has fast and efficient gradient-free solutions. Our data-driven method achieves a boost in performance compared to random initialization methods, both before start of training and after training is over. We show that our proposed method is especially effective in few-shot and fine-tuning settings. We conclude this paper with analyses on time complexity and the effect of different latent codes on the recognition performance.