pwc-archive/papers-with-abstracts huggingface dataset:
Large Hybrid Time-Varying Parameter VARs
Authors: Joshua C. C. Chan
Date: 2022-01-18 00:00:00

Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs--VARs with time-varying parameters in some equations but constant coefficients in others. Specifically, for each equation, the new method automatically decides whether the VAR coefficients and contemporaneous relations among variables are constant or time-varying. Using US datasets of various dimensions, we find evidence that the parameters in some, but not all, equations are time varying. The large hybrid TVP-VAR also forecasts better than many standard benchmarks.
Accurate smartphone camera simulation using 3D scenes
Authors: Zheng Lyu, Thomas Goossens, Brian Wandell, Joyce Farrell
Date: 2022-01-19 00:00:00

We assess the accuracy of a smartphone camera simulation. The simulation is an end-to-end analysis that begins with a physical description of a high dynamic range 3D scene and includes a specification of the optics and the image sensor. The simulation is compared to measurements of a physical version of the scene. The image system simulation accurately matched measurements of optical blur, depth of field, spectral quantum efficiency, scene inter-reflections, and sensor noise. The results support the use of image systems simulation methods for soft prototyping cameras and for producing synthetic data in machine learning applications.
Real-time X-ray Phase-contrast Imaging Using SPINNet -- A Speckle-based Phase-contrast Imaging Neural Network
Authors: Zhi Qiao, Xianbo Shi, YuDong Yao, Michael J. Wojcik, Luca Rebuffi, Mathew J. Cherukara, Lahsen Assoufid
Date: 2022-01-18 00:00:00
Tasks: Retrieval

X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantages in spatial resolution, phase sensitivity, and implementation flexibility compared with traditional methods. However, their computational cost has hindered their wider adoption. By exploiting the power of deep learning, we developed a new speckle-based phase-contrast imaging neural network (SPINNet) that boosts the phase retrieval speed by at least two orders of magnitude compared to existing methods. To achieve this performance, we combined SPINNet with a novel coded-mask-based technique, an enhanced version of the speckle-based method. Using this scheme, we demonstrate a simultaneous reconstruction of absorption and phase images on the order of 100 ms, where a traditional correlation-based analysis would take several minutes even with a cluster. In addition to significant improvement in speed, our experimental results show that the imaging resolution and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods. Furthermore, we successfully demonstrate its application in 3D X-ray phase-contrast tomography. Our result shows that SPINNet could enable many applications requiring high-resolution and fast data acquisition and processing, such as in-situ and in-operando 2D and 3D phase-contrast imaging and real-time at-wavelength metrology and wavefront sensing.
Rate-Splitting assisted Massive Machine-Type Communications in Cell-Free Massive MIMO
Authors: Anup Mishra, Yijie Mao, Luca Sanguinetti, Bruno Clerckx
Date: 2022-01-19 00:00:00

This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications. Due to the large number of devices, their sporadic access behaviour and limited coherence interval, we assume a random access strategy with all active devices utilizing the same pilot for uplink channel estimation. This gives rise to a highly pilot-contaminated scenario, which inevitably deteriorates channel estimates. Motivated by the robustness of RSMA towards imperfect channel state information, we propose a novel RSMA-assisted downlink transmission framework for cell-free massive MIMO. On the basis of the downlink achievable spectral efficiency of the common and private streams, we devise a heuristic common precoder design and propose a novel max-min power control method for the proposed RSMA-assisted scheme. Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.
Multiway Spherical Clustering via Degree-Corrected Tensor Block Models
Authors: Jiaxin Hu, Miaoyan Wang
Date: 2022-01-19 00:00:00
Tasks: Clustering, Community Detection

We consider the problem of multiway clustering in the presence of unknown degree heterogeneity. Such data problems arise commonly in applications such as recommendation system, neuroimaging, community detection, and hypergraph partitions in social networks. The allowance of degree heterogeneity provides great flexibility in clustering models, but the extra complexity poses significant challenges in both statistics and computation. Here, we develop a degree-corrected tensor block model with estimation accuracy guarantees. We present the phase transition of clustering performance based on the notion of angle separability, and we characterize three signal-to-noise regimes corresponding to different statistical-computational behaviors. In particular, we demonstrate that an intrinsic statistical-to-computational gap emerges only for tensors of order three or greater. Further, we develop an efficient polynomial-time algorithm that provably achieves exact clustering under mild signal conditions. The efficacy of our procedure is demonstrated through two data applications, one on human brain connectome project, and another on Peru Legislation network dataset.
Controllable Protein Design with Language Models
Authors: Noelia Ferruz, Birte Höcker
Date: 2022-01-18 00:00:00
Tasks: Protein Design, Text Generation

The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences are inherently similar to natural languages: Amino acids arrange in a multitude of combinations to form structures that carry function, the same way as letters form words and sentences that carry meaning. Therefore, it is not surprising that throughout the history of Natural Language Processing (NLP), many of its techniques have been applied to protein research problems. In the last few years, we have witnessed revolutionary breakthroughs in the field of NLP. The implementation of Transformer pre-trained models has enabled text generation with human-like capabilities, including texts with specific properties such as style or subject. Motivated by its considerable success in NLP tasks, we expect dedicated Transformers to dominate custom protein sequence generation in the near future. Finetuning pre-trained models on protein families will enable the extension of their repertoires with novel sequences that could be highly divergent but still potentially functional. The combination of control tags such as cellular compartment or function will further enable the controllable design of novel protein functions. Moreover, recent model interpretability methods will allow us to open the 'black box' and thus enhance our understanding of folding principles. While early initiatives show the enormous potential of generative language models to design functional sequences, the field is still in its infancy. We believe that protein language models are a promising and largely unexplored field and discuss their foreseeable impact on protein design.
Critical periods and Autism Spectrum Disorders, a role for sleep
Authors: Elizabeth Medina, Sarah Peterson, Kristan Singletary, Lucia Peixoto
Date: 2022-01-18 00:00:00

Brain development relies on both experience and genetically defined programs. Time windows where certain brain circuits are particularly receptive to external stimuli, resulting in heightened plasticity, are referred to as critical periods. Sleep is thought to be essential for normal brain development. Importantly, studies have shown that sleep enhances critical period plasticity and promotes experience-dependent synaptic pruning in the developing mammalian brain. Therefore, normal plasticity during critical periods depends on proper sleep. Problems falling and staying asleep occur at a higher rate in Autism Spectrum Disorder (ASD) relative to typical development. In this review, we explore the potential link between sleep, critical period plasticity, and ASD. First, we review the importance of critical period plasticity in typical development and the role of sleep in this process. Next, we summarize the evidence linking ASD with deficits in synaptic plasticity in rodent models of high-confident ASD gene candidates. We then show that almost all the high-confidence rodent models of ASD that show sleep deficits also display plasticity deficits. Given how important sleep is for critical period plasticity, it is essential to understand the connections between synaptic plasticity, sleep, and brain development in ASD. However, studies investigating sleep or plasticity during critical periods in ASD mouse models are lacking. Therefore, we highlight an urgent need to consider developmental trajectory in studies of sleep and plasticity in neurodevelopmental disorders.
Meta-analysis for Discovering Which Genes are Differentially Expressed in Neuroinflammation
Authors: Berk C. Ugurdag, Serena Aktürk, Michelle Adams
Date: 2022-01-18 00:00:00

Neuroinflammation is a significant aspect of many neurological diseases of Homo sapiens, and the genes that are differentially expressed in this process should be well understood to gather the nature of such diseases. We have conducted a meta-analysis (based on a combined adjusted P value and logFC scheme) of 6 multi-species (Homo sapiens, Mus musculus) datasets (available on GEO, short for Gene Expression Omnibus) obtained through microarray technology. Our analysis shows that the genes coding pleckstrin homology domain and galectin-9 proteins take part in neuroinflammation in microglia.
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case
Authors: Geir Storvik, Alfonso Diz-Louis Palomares, Solveig Engebretsen, Gunnar Øyvind Isaksson Rø, Kenth Engø-Monsen, Aja Bråthen Kristoffersen, Birgitte Freiesleben de Blasio, Arnoldo Frigessi
Date: 2022-01-19 00:00:00
Tasks: Management, Time Series, Time Series Analysis

During the first months, the Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state space formalisation of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalisation and positive test incidences. The method is in regular use in Norway and is a powerful instrument for epidemic monitoring and management.
Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings
Authors: Runpeng Li, Prativa Sahoo, Dongrui Wang, Qixuan Wang, Christine E. Brown, Russell C. Rockne, Heyrim Cho
Date: 2022-01-18 00:00:00

Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13Ra2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13Ra2 targeting CAR T-cells for treating glioma. We focus on extending the work of Kuznetsov et al. (1994) by considering binding of multiple CAR T-cells to a single glioma cell, and the dynamics of these multi-cellular conjugates. Our model more accurately describes experimentally observed CAR T-cell killing assay data than a model which does not consider cell binding. Moreover, we derive conditions in the CAR T-cell expansion rate that determines treatment success or failure. Finally, we show that our model captures distinct CAR T-cell killing dynamics at low, medium, and high antigen receptor densities in patient-derived brain tumor cells.
Evaluating and predicting the Efficiency Index for Stereotactic Radiosurgery Plans using RapidMiner GO(JAVA) Based Artificial Intelligence Algorithms
Authors: Hossam Donya, Sheikh Othman, Alexis Dimitriadis
Date: 2022-01-19 00:00:00
Tasks: BIG-bench Machine Learning, Parameter Prediction

Evaluation the prediction of Efficiency index by DVH parameter for SRS treatment plans using Supervised Machine learning and the performance of predictive model algorithms of RapidMiner GO in the parameter prediction are investigated. Dose volume histogram (DVH) based Efficiency index was calculated for 100 clinical SRS plans generated by Leksell Gamma plan, and the results were compared to predicted values produced by machine learning toolbox of RapidMiner Go, algorithms are namely, Generalized linear model (GLR), Decision Tree Model, Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Random Forest (RF) and Deep learning Model (DL). Root mean square error (RMSE), Average absolute error, Absolute relative error, squared correlation and model building time were determined to evaluate the performance of each algorithm. The GLR algorithm model had square correlation of 0.974 with the smallest RMSE of 0.01, relatively high prediction speed, and fast model building time with 2.812 s, according to the results. The RMSE values for all models were between 0.01 upto 0.021, all algorithms performed well. The RMSE of the Gradient Boosted Tree, Random Forest, and Decision Tree regression algorithms was found to be greater than 0.01, suggesting that they are not appropriate for predicting EI in this analysis. RapidMiner GO machine learning models can be used to predict DVH parameters like EI in SRS treatment planning QA. To effectively evaluate the parameter, it is necessary to choose a suitable machine learning algorithm.
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization
Authors: Camille Gontier, Simone Carlo Surace, Igor Delvendahl, Martin Müller, Jean-Pascal Pfister
Date: 2022-01-19 00:00:00
Tasks: Active Learning

Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time: current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology.
The influence of spatial configuration in collective transitions: the importance of being sorted
Authors: Daniel Galvis, David J. Hodson, Kyle C. A. Wedgwood
Date: 2022-01-17 00:00:00

We studied the effects of spatial configuration on collective dynamics in a nearest-neighbour and diffusively coupled lattice of heterogeneous nodes. The networks contained nodes from two populations, which differed in their intrinsic excitability. Initially, these populations were uniformly and randomly distributed throughout the lattice. We then developed an iterative algorithm for perturbing the arrangement of the network such that nodes from the same population were increasingly likely to be adjacent to one another. We found that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity was decreased as network sortedness was increased. Moreover, for weak coupling, we found that regimes of partial synchronisation exist (i.e., 2:1 resonance in the activity of the two populations), which were dependent both on network drive (sometimes in a non-monotonic fashion) and network sortedness.
Forecasting the distribution of long-horizon returns with time-varying volatility
Authors: Hwai-Chung Ho
Date: 2022-01-19 00:00:00
Tasks: Management

The study of long-horizon returns has received a great deal of attention in recent years (see, for example, Boudoukh, Richardson, and Whitelaw (2008), Neuberger (2012) and Lee (2013), Fama and French (2018)). While most of the discussions are concerned with some practical issues in investment, few have touched the important aspect on risk management. The approach adopted in this article is to predict the future distribution of the returns of a fixed long-horizon by which the risk measures of interest that come in the form of a distributional functional such as the value at risk (VaR) and the conditional tail expectation (CTE) can be easily derived. The characteristic feature of our approach which requires no specification of the volatility dynamics nor parametric assumptions of the shock distribution extends the work by Ho et al. (2016) and Ho ( 2017) to a more general volatility dynamics that includes both the widely-used SV model and the GARCH model (Bollerslev, 1986) as special cases.
Exploring Kervolutional Neural Networks
Authors: Nicolas Perez
Date: 2022-01-06 00:00:00

A paper published in the CVPR 2019 conference outlines a new technique called 'kervolution' used in a new type of augmented convolutional neural network (CNN) called a 'kervolutional neural network' (KNN). The paper asserts that KNNs achieve faster convergence and higher accuracies than CNNs. This "mini paper" will further examine the findings in the original paper and perform a more in depth analysis of the KNN architecture. This will be done by analyzing the impact of hyper parameters (specifically the learning rate) on KNNs versus CNNs, experimenting with other types of kervolution operations not tested in the original paper, a more rigourous statistical analysis of accuracies and convergence times and additional theoretical analysis. The accompanying code is publicly available.
Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Clouds
Authors: Chao Qi, Jianqin Yin
Date: 2021-12-05 00:00:00
Tasks: Model Optimization, Semantic Segmentation

Uncertainty-aware semantic segmentation of the point clouds includes the predictive uncertainty estimation and the uncertainty-guided model optimization. One key challenge in the task is the efficiency of point-wise predictive distribution establishment. The widely-used MC dropout establishes the distribution by computing the standard deviation of samples using multiple stochastic forward propagations, which is time-consuming for tasks based on point clouds containing massive points. Hence, a framework embedded with NSA-MC dropout, a variant of MC dropout, is proposed to establish distributions in just one forward pass. Specifically, the NSA-MC dropout samples the model many times through a space-dependent way, outputting point-wise distribution by aggregating stochastic inference results of neighbors. Based on this, aleatoric and predictive uncertainties acquire from the predictive distribution. The aleatoric uncertainty is integrated into the loss function to penalize noisy points, avoiding the over-fitting of the model to some degree. Besides, the predictive uncertainty quantifies the confidence degree of predictions. Experimental results show that our framework obtains better segmentation results of real-world point clouds and efficiently quantifies the credibility of results. Our NSA-MC dropout is several times faster than MC dropout, and the inference time does not establish a coupling relation with the sampling times. The code will be available if the paper is accepted.
Malware Classification Using Static Disassembly and Machine Learning
Authors: Zhenshuo Chen, Eoin Brophy, Tomas Ward
Date: 2021-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Malware Classification

Network and system security are incredibly critical issues now. Due to the rapid proliferation of malware, traditional analysis methods struggle with enormous samples. In this paper, we propose four easy-to-extract and small-scale features, including sizes and permissions of Windows PE sections, content complexity, and import libraries, to classify malware families, and use automatic machine learning to search for the best model and hyper-parameters for each feature and their combinations. Compared with detailed behavior-related features like API sequences, proposed features provide macroscopic information about malware. The analysis is based on static disassembly scripts and hexadecimal machine code. Unlike dynamic behavior analysis, static analysis is resource-efficient and offers complete code coverage, but is vulnerable to code obfuscation and encryption. The results demonstrate that features which work well in dynamic analysis are not necessarily effective when applied to static analysis. For instance, API 4-grams only achieve 57.96% accuracy and involve a relatively high dimensional feature set (5000 dimensions). In contrast, the novel proposed features together with a classical machine learning algorithm (Random Forest) presents very good accuracy at 99.40% and the feature vector is of much smaller dimension (40 dimensions). We demonstrate the effectiveness of this approach through integration in IDA Pro, which also facilitates the collection of new training samples and subsequent model retraining.