pwc-archive/papers-with-abstracts huggingface dataset:
Unlimited Sampling Theorem Based on Fractional Fourier Transform
Authors: Hui Zhao, Bing-Zhao Li
Date: 2021-10-22 00:00:00

The recovery of bandlimited signals with high dynamic range is a hot issue in sampling research. The unlimited sampling theory expands the recordable range of traditional analog-to-digital converters (ADCs) arbitrarily, and the signal is folded back into a low dynamic range measurement, avoiding the saturation problem. We study the unlimited sampling problem of high dynamic non-bandlimited signals in the Fourier domain (FD) based on the fractional Fourier transform (FRFT). First, a mathematical signal model for unlimited sampling is proposed. Then, based on this mathematical model, the annihilation filtering method is used to estimate the arbitrary folding time. Finally, a novel unlimited sampling theorem in the FRFD is obtained. The results show that the non-bandlimited signal can be reconstructed in the FD based on the FRFT, and it is not affected by the ADC threshold.
Cash-subadditive risk measures without quasi-convexity
Authors: Xia Han, Qiuqi Wang, Ruodu Wang, Jianming Xia
Date: 2021-10-23 00:00:00

In the literature on risk measures, cash subadditivity was proposed to replace cash additivity, motivated by the presence of stochastic or ambiguous interest rates and defaultable contingent claims. Cash subadditivity has been traditionally studied together with quasi-convexity, in a way similar to cash additivity with convexity. In this paper, we study cash-subadditive risk measures without quasi-convexity. One of our major results is that a general cash-subadditive risk measure can be represented as the lower envelope of a family of quasi-convex and cash-subadditive risk measures. Representation results of cash-subadditive risk measures with some additional properties are also examined. The notion of quasi-star-shapedness, which is a natural analogue of star-shapedness, is introduced, and we obtain a corresponding representation result via the lower envelope of normalized, quasi-convex and cash-subadditive risk measures.
Rapid and Accurate Detection of SARS-CoV-2 Mutations using a Cas12a-based Sensing Platform
Authors: C He, C Lin, G Mo, B Xi, A Li, D Huang, Y Wan, F Chen, Y Liang, Q Zuo, W Xu, D Feng, G Zhang, L Han, C Ke, H Du, L Huang
Date: 2021-10-25 00:00:00
Tasks: Sensitivity

The increasing prevalence of SARS-CoV-2 variants with spike mutations has raised concerns owing to higher transmission rates, disease severity, and escape from neutralizing antibodies. Rapid and accurate detection of SARS-CoV-2 variants provides crucial information concerning the outbreaks of SARS-CoV-2 variants and possible lines of transmission. This information is vital for infection prevention and control. We used a Cas12a-based RT-PCR combined with CRISPR on-site rapid detection system (RT-CORDS) platform to detect the key mutations in SARS-COV-2 variants, such as 69/70 deletion, N501Y, and D614G. We used type-specific CRISPR RNAs (crRNAs) to identify wild-type (crRNA-W) and mutant (crRNA-M) sequences of SARS-CoV-2. We successfully differentiated mutant variants from wild-type SARS-CoV-2 with a sensitivity of $10^{-17}$ M (approximately 6 copies/$\mu$L). The assay took just 10 min with the Cas12a/crRNA reaction after a simple RT-PCR using a fluorescence reporting system. In addition, a sensitivity of $10^{-16}$ M could be achieved when lateral flow strips were used as readouts. The accuracy of RT-CORDS for SARS-CoV-2 variant detection was 100% consistent with the sequencing data. In conclusion, using the RT-CORDS platform, we accurately, sensitively, specifically, and rapidly detected SARS-CoV-2 variants. This method may be used in clinical diagnosis.
Using Steered Molecular Dynamic Tension for Assessing Quality of Computational Protein Structure Models
Authors: Lyman Monroe, Daisuke Kihara
Date: 2021-10-22 00:00:00

The native structures of proteins, except for notable exceptions of intrinsically disordered proteins, in general take their most stable conformation in the physiological condition to maintain their structural framework so that their biological function can be properly carried out. Experimentally, the stability of a protein can be measured by several means, among which the pulling experiment using the atomic force microscope (AFM) stands as a unique method. AFM directly measures the resistance from unfolding, which can be quantified from the observed force-extension profile. It has been shown that key features observed in an AFM pulling experiment can be well reproduced by computational molecular dynamics simulations. Here, we applied computational pulling for estimating the accuracy of computational protein structure models under the hypothesis that the structural stability would positively correlated with the accuracy, i.e. the closeness to the native, of a model. We used in total 4,929 structure models for 24 target proteins from the Critical Assessment of Techniques of Structure Prediction (CASP) and investigated if the magnitude of the break force, i.e., the force required to rearrange the model structure, from the force profile was sufficient information for selecting near-native models. We found that near-native models can be successfully selected by examining their break forces suggesting that high break force indeed indicates high stability of models. On the other hand, there were also near-native models that had relatively low peak forces. The mechanisms of the stability exhibited by the break forces were explored and discussed.
RZiMM-scRNA: A regularized zero-inflated mixture model framework for single-cell RNA-seq data
Authors: Xinlei Mi, William Bekerman, Peter A. Sims, Peter D. Canoll, Jianhua Hu
Date: 2021-10-25 00:00:00
Tasks: Clustering, scientific discovery

Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However, unique characteristics of scRNA-seq data, including large dimensionality, high dropout rates, and possibly batch effects, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score, accounting for both dropouts and batch effects. We conduct extensive simulation studies in which we evaluate the performance of RZiMM-scRNA and compare it with several popular methods, including Seurat, SC3, K-Means, and Hierarchical Clustering. Simulation results show that RZiMM-scRNA demonstrates superior clustering performance and enhanced biomarker detection accuracy compared to alternative methods, especially when cell subgroups are less distinct, verifying the robustness of our method. Our empirical investigations focus on two brain tumor studies dealing with astrocytoma of various grades, including the most malignant of all brain tumors, glioblastoma multiforme (GBM). Our goal is to delineate cell heterogeneity and identify driving biomarkers associated with these tumors. Notably, RZiMM-scNRA successfully identifies a small group of oligodendrocyte cells which has drawn much attention in biomedical literature on brain cancers.
A data-informed mean-field approach to mapping of cortical parameter landscapes
Authors: Zhuo-Cheng Xiao, Kevin K. Lin, Lai-Sang Young
Date: 2021-10-23 00:00:00

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.
Concurrent generative models inform prediction error in the human auditory pathway
Authors: Alejandro Tabas, Katharina von Kriegstein
Date: 2021-10-23 00:00:00
Tasks: Prediction

Predictive coding is the leading algorithmic framework to understand how expectations shape our experience of reality. Its main tenet is that sensory neurons encode prediction error: the residuals between a generative model of the sensory world and the actual sensory input. However, it is yet unclear how this scheme generalises to the multi-level hierarchical architecture of sensory processing. Theoretical accounts of predictive coding agree that neurons computing prediction error and the generative model exist at all levels of the processing hierarchy. However, there is not a current consensus of how predictions from independent models at different stages are integrated during the computation of prediction error. Here we investigated predictive processing with respect to two independent concurrent generative models in the auditory pathway using functional magnetic resonance imaging. We used two paradigms where human participants listened to sequences of either pure tones or FM-sweeps while we recorded BOLD responses in inferior colliculus (IC), medial geniculate body (MGB), and auditory cortex (AC). Each paradigm included the induction of two generative models: one based on local stimulus statistics; and another model based on the subjective expectations induced by task instruction. We used Bayesian model comparison to test whether neural responses in IC, MGB, and AC encoded prediction error with respect to either of the two generative models, or a combination of both. Results showed that neural populations in bilateral IC, MGB, and AC encode prediction error with respect to a combination of the two generative models, suggesting that the predictive architecture of predictive coding might be more complex than previously hypothesised.
Auditory verbal learning disabilities in patients with mild cog impairment and mild Alzheimer's disease: A clinical study
Authors: Mahmoud Kargar, Alireza Mohammadi
Date: 2021-10-22 00:00:00

Learning and memory impairments are common characteristics of individuals with mild cognitive impairment (MCI) and mild Alzheimer's disease (miAD). Early diagnosis of MCI is necessary to prevent recurrence of the disease and developing of miAD. For this purpose, we investigated the components of the Rey Auditory Verbal Learning Test (RAVLT) to explore the auditory-verbal learning (AVL) disabilities in these patients. The AVL of 20 patients with miAD and 30 patients with MCI were compared with 30 cognitively normal controls (CN) using the RAVLT. General cognitive performance assessment was carried out based on the Mini-Mental State Examination (MMSE) score. Finally, Pearson's correlation coefficients were used to evaluate the correlation between the MMSE scores and immediate and delayed recalls, verbal learning and forgetting, and memory recognition. We found that both miAD and MCI subjects were significantly impaired in all components of the RAVLT. Compared to the MCI subjects, miAD patients performed worse on all components of the test. The MCI subjects had significantly lower scores than the CN group. The AVL analysis showed that there were significant differences between the CN and other groups, but the difference between MCI and miAD subjects was not significant. However, there was no difference among the groups in their verbal forgetting scores. It can be concluded that both patients with miAD and MCI were impaired in AVL and our findings confirm that the RAVLT can take a part in the prediction of probable miAD and early evaluation of individuals with subjective memory complaints.
Godot is not coming: when we will let innovations enter psychiatry?
Authors: Milena B. Čukić
Date: 2021-10-16 00:00:00
Tasks: Bayesian Inference, Diagnostic

Current diagnostic practice in psychiatry is not relying on objective biophysical evidence. Recent pandemic emphasized the need to address the rising number of mood disorders (in particular, depression) cases in a more efficient way. We are proposing several already developed practices that can help improve that diagnostic process: detection based on electrophysiological signals (both electroencephalogram and electrocardiogram based) that were shown to be accurate for clinical practice and several modalities of electromagnetic stimulation that were proven to ameliorate symptoms of depression. In this work, we are connecting the two with explanations coming from physiological complexity studies (and our own work) as well as advanced statistical methods like machine learning and the Bayesian inference approach. It is shown that fractal and nonlinear measures can adequately quantify previously undetected changes in intrinsic dynamics of physiological systems, providing the basis for early detection of depression. We are also advocating for early screening of cardiovascular risks in depression which is in connection to previously described decomplexification of the autonomous nervous system resulting in symptoms recognized clinically. All that said, additional information about the level of complexity can help clinicians make a better decisions in the therapeutic process, increase the overall effectiveness of the treatment, and finally increase the quality of life of the patient.
Predictive factors associated with survival rate of cervical cancer patients in Brunei Darussalam
Authors: Fadhliah Madli, Elvynna Leong, Sok King Ong, Edwin Lim, Khairul Amilin Tengah
Date: 2021-10-21 00:00:00
Tasks: Management, Survival Analysis

Introduction: Cervical cancer is the third most prevalent cancer among women in Brunei Darussalam. This study aims to report the overall survival rates and associated factors of patients diagnosed with malignant cervical cancer in Brunei Darussalam. Methods: A retrospective study of patients diagnosed with cervical cancer from 2007 to 2017 in Brunei Darussalam. The data were obtained from the population-based cancer registry in Brunei Darussalam. Kaplan- Meier survival analysis was used to estimate the overall survival rates at 1-, 3- and 5-year intervals while the log-rank test was used to assess differences in survival between groups. Cox Proportional Hazard (PH) regression analysis was used to examine the association of demographic and clinical factors on the survival of cervical cancer patients. Results: A total of 329 registered malignant cervical cancer cases were analyzed. The mean age at diagnosis of patients with cervical cancer was 46.7 years. There were 28.6% deaths and the overall survival rates at 1, 3 and 5 years were 85.4%, 72.6% and 68.6% respectively. Age at diagnosis, cancer stage and histology types were significant predictive factors for overall survival of the patients diagnosed with cervical cancers when analysed on both log rank tests and Cox PH model. Conclusion: Age at diagnosis, cancer stage and histology types were significantly associated with the overall survival rates of cervical cancer patients in Brunei Darussalam. Early detection and management of cervical cancer at early stages should be prioritized to improve the survival rate and quality of cancer care.
Encounter networks from collective mitochondrial dynamics support the emergence of effective mtDNA genomes in plant cells
Authors: Konstantinos Giannakis, Joanna M. Chustecki, Iain G. Johnston
Date: 2021-10-25 00:00:00

Mitochondria in plant cells form strikingly dynamic populations of largely individual organelles. Each mitochondrion contains on average less than a full copy of the mitochondrial DNA (mtDNA) genome. Here, we asked whether mitochondrial dynamics may allow individual mitochondria to `collect' a full copy of the mtDNA genome over time, by facilitating exchange between individuals. Akin to trade on a social network, exchange of mtDNA fragments across organelles may lead to the emergence of full `effective' genomes in individuals over time. We characterise the collective dynamics of mitochondria in \emph{Arabidopsis thaliana} hypocotyl cells using a recent approach combining single-cell timelapse microscopy, video analysis, and network science. We then use a quantitative model to predict the capacity for the sharing and accumulation of genetic information through the networks of encounters between mitochondria. We find that biological encounter networks are strikingly well predisposed to support the collection of full genomes over time, outperforming a range of other networks generated from theory and simulation. Using results from the coupon collector's problem, we show that the upper tail of the degree distribution is a key determinant of an encounter network's performance at this task and discuss how features of mitochondrial dynamics observed in biology facilitate the emergence of full effective genomes.
MAD Risk Parity Portfolios
Authors: Çağın Ararat, Francesco Cesarone, Mustafa Çelebi Pınar, Jacopo Maria Ricci
Date: 2021-10-23 00:00:00

In this paper, we investigate the features and the performance of the Risk Parity (RP) portfolios using the Mean Absolute Deviation (MAD) as a risk measure. The RP model is a recent strategy for asset allocation that aims at equally sharing the global portfolio risk among all the assets of an investment universe. We discuss here some existing and new results about the properties of MAD that are useful for the RP approach. We propose several formulations for finding MAD-RP portfolios computationally, and compare them in terms of accuracy and efficiency. Furthermore, we provide extensive empirical analysis based on three real-world datasets, showing that the performances of the RP approaches generally tend to place both in terms of risk and profitability between those obtained from the minimum risk and the Equally Weighted strategies.
Interpolating commodity futures prices with Kriging
Authors: Andrea Maran, Andrea Pallavicini
Date: 2021-10-25 00:00:00

The shape of the futures term structure is essential to commodity hedgers and speculators as futures prices serve as a forecast of future spot prices. Commodity markets quotes futures prices on a selection of maturities and delivery periods. In this note, we investigate a Bayesian technique known as Kriging to build a term structure of futures prices by embedding trends and seasonalities and by taking into account bid-ask spreads of market quotations on different delivery periods.
Bank transactions embeddings help to uncover current macroeconomics
Authors: Maria Begicheva, Alexey Zaytsev
Date: 2021-10-14 00:00:00

Macroeconomic indexes are of high importance for banks: many risk-control decisions utilize these indexes. A typical workflow of these indexes evaluation is costly and protracted, with a lag between the actual date and available index being a couple of months. Banks predict such indexes now using autoregressive models to make decisions in a rapidly changing environment. However, autoregressive models fail in complex scenarios related to appearances of crises. We propose to use clients' financial transactions data from a large Russian bank to get such indexes. Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions. The approach uses a neural networks paradigm and a smart sampling scheme. The results show that our neural network approach outperforms the baseline method on hand-crafted features based on transactions. Calculated embeddings show the correlation between the client's transaction activity and bank macroeconomic indexes over time.
Machine Learning in Finance-Emerging Trends and Challenges
Authors: Jaydip Sen, Rajdeep Sen, Abhishek Dutta
Date: 2021-10-08 00:00:00
Tasks: BIG-bench Machine Learning

The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the emergence of smarter algorithms for solving complex and challenging problems, organizations have found it possible to harness a humongous volume of data in realizing solutions that have far-reaching business values. This introductory chapter highlights some of the challenges and barriers that organizations in the financial services sector at the present encounter in adopting machine learning and artificial intelligence-based models and applications in their day-to-day operations.
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Authors: Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami, Negar Kiyavash
Date: 2021-10-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Selection bias

We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of the main approaches for solving this problem, but the existing methods are either computationally impractical when dealing with large graphs or lacking completeness guarantees. We propose a novel computationally efficient recursive constraint-based method that is sound and complete. The key idea of our approach is that at each iteration a specific type of variable is identified and removed. This allows us to learn the structure efficiently and recursively, as this technique reduces both the number of required conditional independence (CI) tests and the size of the conditioning sets. The former substantially reduces the computational complexity, while the latter results in more reliable CI tests. We provide an upper bound on the number of required CI tests in the worst case. To the best of our knowledge, this is the tightest bound in the literature. We further provide a lower bound on the number of CI tests required by any constraint-based method. The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case. We provide experimental results to compare the proposed approach with the state of the art on both synthetic and real-world structures.