pwc-archive/papers-with-abstracts huggingface dataset:
High-Dimensional Granger Causality for Climatic Attribution
Authors: Marina Friedrich, Luca Margaritella, Stephan Smeekes
Date: 2023-02-08 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global temperatures. By allowing for high dimensionality in the model, we can enrich the information set with relevant natural and anthropogenic forcing variables to obtain reliable causal relations. This provides a step forward from existing climatology literature, which has mostly treated these variables in isolation in small models. Additionally, our framework allows to disregard the order of integration of the variables by directly estimating the VAR in levels, thus avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are well known to contain stochastic trends and long memory. We are thus able to establish causal networks linking radiative forcings to global temperatures and to connect radiative forcings among themselves, thereby allowing for tracing the path of dynamic causal effects through the system.
Labor Market Effects of the Venezuelan Refugee Crisis in Brazil
Authors: Hugo Sant'Anna, Samyam Shrestha
Date: 2023-02-08 00:00:00

We use administrative panel data on the universe of Brazilian formal workers to investigate the labor market effects of the Venezuelan crisis in Brazil, focusing on the border state of Roraima. The results using difference-in-differences show that the monthly wages of Brazilians in Roraima increased by around 2 percent, which was mostly driven by those working in sectors and occupations with no refugee involvement. The study finds negligible job displacement for Brazilians but finds evidence of native workers moving to occupations without immigrants. We also find that immigrants in the informal market offset the substitution effects in the formal market.
The qualitative accuracy of the Becker-DeGroot-Marshak method
Authors: Maximilian Späth
Date: 2023-02-08 00:00:00
Tasks: Object

The Becker DeGroot Marshak method is widely used to elicit the valuation that an individual assigns to an object. Theoretically, the second-price structure of the method gives individuals the incentive to state their true valuation. Yet, the elicitation methods empirical accuracy is subject to debate. With this paper, I provide a clear verification of the qualitative accuracy of the method. Participants of an incentivized laboratory experiment can sell a virtual object. The value of the object is publicly known and experimentally varied in a between-subjects design. Replicating previous findings on the low quantitative accuracy, I observe a very small share of individuals placing a payoff-optimal stated valuation. However, the analysis shows that the stated valuation increases with the value of the object. This result shows the qualitative accuracy of the BDM method and suggests that the method can be applied in comparative studies.
Axiomatization of Random Utility Model with Unobservable Alternatives
Authors: Haruki Kono, Kota Saito, Alec Sandroni
Date: 2023-02-08 00:00:00
Tasks: model

The random utility model is one of the most fundamental models in economics. Falmagne (1978) provides an axiomatization but his axioms can be applied only when choice frequencies of all alternatives from all subsets are observable. In reality, however, it is often the case that we do not observe choice frequencies of some alternatives. For such a dataset, we obtain a finite system of linear inequalities that is necessary and sufficient for the dataset to be rationalized by a random utility model. Moreover, the necessary and sufficient condition is tight in the sense that none of the inequalities is implied by the other inequalities, and dropping any one of the inequalities makes the condition not sufficient.
Simplified markerless stride detection pipeline (sMaSDP) for surface EMG segmentation
Authors: Rafael Castro Aguiar, Edward Jero, Samit Chakrabarty
Date: 2023-02-08 00:00:00
Tasks: Electromyography (EMG), Event Detection

People with mobility impairments are often recommended for gait assessment studies to diagnose their condition and to select appropriate physiotherapy to improve their mobility. These studies are often conducted in clinical or lab settings, where subjects are assessed in a foreign environment, which may influence their motivation, coordination and overall mobility. Alternatively, if the subject's gait could be assessed in their daily-lives, in unconstrained settings, a more naturalistic gait assessment could be performed. Kinematic analysis of a gait pattern on its own may not be sufficient to characterise a subject's mobility. To better diagnose gait deficiencies, analysis of the patient's muscle activity should be conducted as well. To do so, gait studies should collect, synchronously, Electromyography (EMG) and kinematic data. This method introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals, via synchronously recorded Inertial Measurement Unit (IMU) data. In an unconstrained walking experiment, healthy subjects walk through a designed course with their kinematic and EMG data recorded. This course comprises 5 different walking modalities (level walking, ramp up/down, staircase up/down), mimicking everyday walking. Through timepoint matching, segmentation and filtering, we generate an algorithm that detects heel-strike (HS) events using a single IMU, and isolates EMG activity of gait cycles, in the different walking modalities. This gait event detection algorithm can be adapted to different datasets, and was tested in both healthy and Parkinson's Disease (PD) gait. Results demonstrate the extracted muscle activity levels in a healthy subject's level ground walking, and the extracted HS events of a PD patient. Adjustments to algorithm parameters are possible (e.g., expected velocity, cadence) and can further increase the detection accuracy.
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems
Authors: Zhibo Zhang, Sani Umar, Ahmed Y. Al Hammadi, Sangyoung Yoon, Ernesto Damiani, Chan Yeob Yeun
Date: 2023-02-08 00:00:00
Tasks: Data Poisoning, EEG, Electroencephalogram (EEG)

Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention. However, EEG signal-based risk assessment systems, which could evaluate the emotional states of humans, have shown several vulnerabilities to data poison attacks. In this paper, from the attackers' perspective, data poison attacks involving label-flipping occurring in the training stages of different machine learning models intrude on the EEG signal-based risk assessment systems using these machine learning models. This paper aims to propose two categories of label-flipping methods to attack different machine learning classifiers including Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP), Random Forest, and K-Nearest Neighbors (KNN) dedicated to the classification of 4 different human emotions using EEG signals. This aims to degrade the performance of the aforementioned machine learning models concerning the classification task. The experimental results show that the proposed data poison attacks are model-agnostically effective whereas different models have different resilience to the data poison attacks.
Cooperative Beamforming and RISs Association for Multi-RISs Aided Multi-Users MmWave MIMO Systems through Graph Neural Networks
Authors: Mengbing Liu, Chongwen Huang, Marco Di Renzo, Merouane Debbah, Chau Yuen
Date: 2023-02-08 00:00:00
Tasks: Blocking, Graph Neural Network

Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e.g., customizing the communication environment. Therefore, deploying multiple RISs helps overcome severe signal blocking between the base station (BS) and users, which is also a practical and effective solution to achieve better service coverage. However, reaping the full benefits of a multi-RISs aided communication system requires solving a non-convex, infinite-dimensional optimization problem, which motivates the use of learning-based methods to configure the optimal policy. This paper adopts a novel heterogeneous graph neural network (GNN) to effectively exploit the graph topology in the wireless communication optimization problem. First, we characterize all communication link features and interference relations in our system with a heterogeneous graph structure. Then, we endeavor to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active beamforming at the BS, the passive beamforming vector of the RIS elements, as well as the RISs association strategy. Unlike most existing work, we consider a more general scenario where the cascaded link for each user is not fixed but dynamically selected by maximizing the WSR. Simulation results show that our proposed heterogeneous GNNs perform about 10 times better than other benchmarks, and a suitable RISs association strategy is also validated to be effective in improving the quality services of users by 30%.
Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings
Authors: Hamed Masoumi, Michel Verhaegen, Nitin Jonathan Myers
Date: 2023-02-08 00:00:00
Tasks: compressed sensing

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.
Cluster Index Modulation for Reconfigurable Intelligent Surface-Assisted mmWave Massive MIMO
Authors: Mahmoud Raeisi, Asil Koc, Ibrahim Yildirim, Ertugrul Basar, Tho Le-Ngoc
Date: 2023-02-08 00:00:00
Tasks: Diversity

In this paper, we propose a transmission mechanism for a reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) system based on cluster index modulation (CIM), named best-gain optimized cluster selection CIM (BGCS-CIM). The proposed BGCS-CIM scheme considers effective cluster power gain and spatial diversity gain obtained by the additional paths within the indexed cluster to construct an efficient codebook. We also integrate the proposed scheme into a practical system model to create a virtual path between transmitter and receiver where the direct link has been blocked. Thanks to the designed whitening filter, a closed-form expression for the upper bound on the average bit error rate (ABER) is derived and used to validate the simulation results. It has been shown that the proposed BGCS-CIM scheme outperforms the existing benchmarks thanks to its higher effective cluster gain, spatial diversity of indexed clusters, and lower inter-cluster interference.
Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers
Authors: Samuel R. Shebert, Benjamin H. Kirk, R. Michael Buehrer
Date: 2023-02-07 00:00:00
Tasks: Deep Learning

In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isolation steps. To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers. The hybrid classifier reduces the computational complexity by 2 to 7 times on average compared to the expert feature classifiers, while achieving an accuracy of 95% at 15 dB SNR for known signal classes. The hybrid classifier manages to detect unknown classes at nearly 100% accuracy, due to the robustness of the expert feature classifiers.
On the redundancy of birth and death rates in homogenous epidemic SIR models
Authors: Florian Nill
Date: 2023-02-08 00:00:00

The dynamics of fractional population sizes y_i=Y_i/N in homogeneous compartment models with time dependent total population N is analyzed. Assuming constant per capita birth and death rates the vector field Y_i'=V_i(Y) naturally projects to a vector field F_i(Y) tangent to the leaves of constant population N. A universal formula for the projected field F_i is given. In this way, in many SIR-type models with standard incidence all demographic parameters become redundant for the dynamical system y_i'=F_i(y). They may be put to zero by shifting remaining parameters appropriately. Normalizing eight examples from the literature this way, they unexpectedly become isomorphic for corresponding parameter ranges. Thus, some recently published results turn out to be already covered by papers 20 years ago.
Early Risk Prediction of Chronic Myeloid Leukemia with Protein Sequences using Machine Learning-based Meta-Ensemble
Authors: Madiha Hameed, Muhammad Bilal, Tuba Majid, Abdul Majid, Asifullah Khan
Date: 2023-02-08 00:00:00

Leukemia, the cancer of blood cells, originates in the blood-forming cells of the bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cells partially become mature that look like normal white blood cells but do not resist infection effectively. Early detection of CML is important for effective treatment, but there is a lack of routine screening tests. Regular check-ups and monitoring of symptoms are the best way to detect CML in the early stages. In the study, we developed a multi-layer-perception-based meta-ensemble system using protein amino acid sequences for early risk prediction of CML. The deleterious mutation analysis of protein sequences provides 7discriminant information in amino acid sequences causing CML. The protein sequences are expressed into molecular descriptors using the values of hydrophobicity and hydrophilicity of the amino acids. 9 These descriptors are transformed in various statistical and correlation-based feature spaces. These 10 features information is given to several diverse types of base learners. The preliminary predictions of 11 base-learners are employed to develop Multi-Layered Perceptron (MLP) based meta-ensemble. The 12 proposed learning approach effectively utilizes the discriminant information to classify CML/non- 13 CML protein sequences. The proposed prediction system has given improved results and it can be 14 employed as a potential biomarker for early diagnosis of CML.
Dynamic Visualization of Gyral and Sulcal Stereoelectroencephalographic contacts in Humans
Authors: Markus Adamek, Alexander P Rockhill, Peter Brunner, Dora Hermes
Date: 2023-02-07 00:00:00
Tasks: MORPH, Survey

Stereoelectroencephalography (SEEG) is a neurosurgical method to survey electrophysiological activity within the brain to treat disorders such as Epilepsy. In this stereotactic approach, leads are implanted through straight trajectories to survey both cortical and sub-cortical activity. Visualizing the recorded locations covering sulcal and gyral activity while staying true to the cortical architecture is challenging due to the folded, three-dimensional nature of the human cortex. To overcome this challenge, we developed a novel visualization concept, allowing investigators to dynamically morph between the subjects' cortical reconstruction and an inflated cortex representation. This inflated view, in which gyri and sulci are viewed on a smooth surface, allows better visualization of electrodes buried within the sulcus while staying true to the underlying cortical architecture.
Evolution of Two Membrane Protein Sequences and Functions
Authors: J. C. Phillips
Date: 2023-02-07 00:00:00
Tasks: Vocal Bursts Valence Prediction

TRPC(3,6) are two ~ 930 amino acid membrane proteins that form calcium permeant cation channels. Here we examine the differences between mammals and oviparous species. Our method is based on the concept of evolution towards criticality, a general concept we have previously applied to many proteins, especially in describing the evolution of pandemic sequences through natural selection.
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
Authors: Geng Zhan, Dongang Wang, Mariano Cabezas, Lei Bai, Kain Kyle, Wanli Ouyang, Michael Barnett, Chenyu Wang
Date: 2023-02-08 00:00:00

Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications. This paper presents a deep learning based method, DeepBVC, for longitudinal brain volume change measurement using 3D T1-weighted MRI scans. Trained with the intermediate outputs from SIENA, DeepBVC is designed to take into account the variance caused by different scanners and acquisition protocols. Compared with SIENA, DeepBVC demonstrates higher consistency in terms of volume change estimation across multiple time points in MS subjects; and greater stability and superior performance in scan-rescan experiments. Moreover, the results also show that DeepBVC is insensitive to acquisition variance in terms of imaging contrast, voxel resolution, random bias field and signal-to-noise ratio. Measurement robustness, automation and processing speed suggest a broad potential of DeepBVC in both research and clinical quantitative neuroimaging applications.
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective
Authors: Johann Lussange, Boris Gutkin
Date: 2023-02-08 00:00:00
Tasks: Multi-agent Reinforcement Learning

Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments. In parallel, the ongoing machine learning revolution and its multiple applications to trading can now be used to design a next generation of financial models, and thereby explore the systemic complexity of financial stock markets in new ways. We here follow on a previous groundwork, where we designed and calibrated a novel agent-based model stock market simulator, where each agent autonomously learns to trade by reinforcement learning. In this Paper, we now study the predictions of this model from a regulator's perspective. In particular, we focus on how the market quality is impacted by smaller order book tick sizes, increasingly larger metaorders, and higher trading frequencies, respectively. Under our model assumptions, we find that the market quality benefits from the latter, but not from the other two trends.
Risk sharing, measuring variability, and distortion riskmetrics
Authors: Jean-Gabriel Lauzier, Liyuan Lin, Ruodu Wang
Date: 2023-02-08 00:00:00
Tasks: Portfolio Optimization

We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance. The set of Pareto-optimal allocations is characterized under various settings of general or comonotonic risk sharing problems. We solve explicitly Pareto-optimal allocations among agents using the Gini deviation, the mean-median deviation, or the inter-quantile difference as the relevant variability measures. The latter is of particular interest, as optimal allocations are not comonotonic in the presence of inter-quantile difference agents; instead, the optimal allocation features a mixture of pairwise counter-monotonic structures, showing some patterns of extremal negative dependence.
Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training
Authors: Dianxin Luan, John Thompson
Date: 2023-02-08 00:00:00
Tasks: Decoder

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input features before processing them in the decoder. In particular, we implement multi-head attention in the encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a customized weight-level pruning to slim the trained neural network with a fine-tuning process, which reduces the computational complexity significantly to realize a low complexity and low latency solution. This enables reductions of up to 70\% in the parameters, while maintaining an almost identical performance compared with the complete Channelformer. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems, which only needs the available information at the receiver for online training. Using industrial standard channel models, the simulations of attention-based solutions show superior estimation performance compared with other candidate neural network methods for channel estimation.
Reversible Vision Transformers
Authors: Karttikeya Mangalam, Haoqi Fan, Yanghao Li, Chao-yuan Wu, Bo Xiong, Christoph Feichtenhofer, Jitendra Malik
Date: 2023-02-09 00:00:00
Proceeding: CVPR 2022 1
Tasks: GPU, image-classification, Image Classification, object-detection, Object Detection, Video Classification

We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage. We adapt two popular models, namely Vision Transformer and Multiscale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5x at roughly identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for hardware resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 2.3x over their non-reversible counterparts. Full code and trained models are available at https://github.com/facebookresearch/slowfast. A simpler, easy to understand and modify version is also available at https://github.com/karttikeya/minREV