pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Bias Correction for Cross-section and Panel Data
Authors: Jinyong Hahn, David W. Hughes, Guido Kuersteiner, Whitney K. Newey
Date: 2022-07-20 00:00:00

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher-order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split-sample jackknife, result in larger higher-order variances in the i.i.d. setting we focus on. For both a cross-sectional MLE and a panel model with individual fixed effects, we show that the split-sample jackknife has a higher-order variance term that is twice as large as that of the `leave-one-out' jackknife.
Do school reforms shape study behavior at university? Evidence from an instructional time reform
Authors: Jakob Schwerter, Nicolai Netz, Nicolas Hübner
Date: 2022-07-20 00:00:00

Early-life environments can have long-lasting developmental effects. Interestingly, research on how school reforms affect later-life study behavior has hardly adopted this perspective. Therefore, we investigated a staggered school reform that reduced the number of school years and increased weekly instructional time for secondary school students in most German federal states. We analyzed this quasi-experiment in a difference-in-differences framework using representative large-scale survey data on 71,426 students who attended university between 1998 and 2016. We found negative effects of reform exposure on hours spent attending classes and on self-study, and a larger time gap between school completion and higher education entry. Our results support the view that research should examine unintended long-term effects of school reforms on individual life courses.
Stabilised Inverse Flowline Evolution for Anisotropic Image Sharpening
Authors: Kristina Schaefer, Joachim Weickert
Date: 2022-07-20 00:00:00

The central limit theorem suggests Gaussian convolution as a generic blur model for images. Since Gaussian convolution is equivalent to homogeneous diffusion filtering, one way to deblur such images is to diffuse them backwards in time. However, backward diffusion is highly ill-posed. Thus, it requires stabilisation in the model as well as highly sophisticated numerical algorithms. Moreover, sharpening is often only desired across image edges but not along them, since it may cause very irregular contours. This creates the need to model a stabilised anisotropic backward evolution and to devise an appropriate numerical algorithm for this ill-posed process. We address both challenges. First we introduce stabilised inverse flowline evolution (SIFE) as an anisotropic image sharpening flow. Outside extrema, its partial differential equation (PDE) is backward parabolic in gradient direction. Interestingly, it is sufficient to stabilise it in extrema by imposing a zero flow there. We show that morphological derivatives - which are not common in the numerics of PDEs - are ideal for the numerical approximation of SIFE: They effortlessly approximate directional derivatives in gradient direction. Our scheme adapts one-sided morphological derivatives to the underlying image structure. It allows to progress in subpixel accuracy and enables us to prove stability properties. Our experiments show that SIFE allows nonflat steady states and outperforms other sharpening flows.
Optimized processing order for 3D hole filling in video sequences using frequency selective extrapolation
Authors: Jürgen Seiler, Susanne Schöll, Wolfgang Schnurrer, André Kaup
Date: 2022-07-20 00:00:00

A problem often arising in video communication is the reconstruction of missing or distorted areas in a video sequence. Such holes of unavailable pixels may be caused for example by transmission errors of coded video data or undesired objects like logos. In order to close the holes given neighboring available content, a signal extrapolation has to be performed. The best quality can be achieved, if spatial as well as temporal information is used for the reconstruction. However, the question always is in which order to process the extrapolation to obtain the best result. In this paper, an optimized processing order is introduced for improving the extrapolation quality of Three-dimensional Frequency Selective Extrapolation. Using the proposed optimized order, holes in video sequences can be closed from the outer margin to the center, leading to a higher reconstruction quality, and visually noticeable gains of more than 0.5 dB PSNR are possible.
Spatio-temporal prediction in video coding by non-local means refined motion compensation
Authors: Jürgen Seiler, Thomas Richter, André Kaup
Date: 2022-07-20 00:00:00
Tasks: Motion Compensation, Prediction

The prediction step is a very important part of hybrid video codecs. In this contribution, a novel spatio-temporal prediction algorithm is introduced. For this, the prediction is carried out in two steps. Firstly, a preliminary temporal prediction is conducted by motion compensation. Afterwards, spatial refinement is carried out for incorporating spatial redundancies from already decoded neighboring blocks. Thereby, the spatial refinement is achieved by applying Non-Local Means de-noising to the union of the motion compensated block and the already decoded blocks. Including the spatial refinement into H.264/AVC, a rate reduction of up to 14 % or respectively a gain of up to 0.7 dB PSNR compared to unrefined motion compensated prediction can be achieved.
Spatio-temporal prediction in video coding by best approximation
Authors: Jürgen Seiler, Haricharan Lakshman, André Kaup
Date: 2022-07-20 00:00:00
Tasks: Motion Compensation, Prediction

Within the scope of this contribution we propose a novel efficient spatio-temporal prediction algorithm for video coding. The algorithm operates in two stages. First, motion compensation is performed on the block to be predicted in order to exploit temporal correlations. Afterwards, in order to exploit spatial correlations, this preliminary estimate is spatially refined by forming a joint model of the motion compensated block and spatially adjacent already decoded blocks. Compared to an earlier refinement algorithm, the novel one only needs very little iteration, leading to a speedup of factor 17. The implementation of this new algorithm into the H.264/AVC leads to a maximum reduction in data rate of up to nearly 13% for the considered sequences.
Orthogonality Deficiency Compensation for Improved Frequency Selective Image Extrapolation
Authors: Jürgen Seiler, Katrin Meisinger, André Kaup
Date: 2022-07-20 00:00:00

This paper describes a very efficient algorithm for image signal extrapolation. It can be used for various applications in image and video communication, e.g. the concealment of data corrupted by transmission errors or prediction in video coding. The extrapolation is performed on a limited number of known samples and extends the signal beyond these samples. Therefore the signal from the known samples is iteratively projected onto different basis functions in order to generate a model of the signal. As the basis functions are not orthogonal with respect to the area of the known samples we propose a new extension, the orthogonality deficiency compensation, to cope with the non-orthogonality. Using this extension, very good extrapolation results for structured as well as for smooth areas are achievable. This algorithm improves PSNR up to 2 dB and gives a better visual quality for concealment of block losses compared to extrapolation algorithms existent so far.
Lesion detection in contrast enhanced spectral mammography
Authors: Clément Jailin, Pablo Milioni, Zhijin Li, Răzvan Iordache, Serge Muller
Date: 2022-07-20 00:00:00
Tasks: Cancer Classification, Diagnostic, Lesion Detection, Management

Background \& purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases. Material \& methods: A large CESM diagnostic dataset with biopsy-proven lesions was collected from various hospitals and different acquisition systems. The annotated data were split on a patient level for the training (55%), validation (15%) and test (30%) of a deep neural network with a state-of-the-art detection architecture. Free Receiver Operating Characteristic (FROC) was used to evaluate the model for the detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC curve was used to evaluate breast cancer classification. The metrics were finally compared to clinical results. Results: For the evaluation of the malignant lesion detection, at high sensitivity (Se>0.95), the false positive rate was at 0.61 per image. For the classification of malignant cases, the model reached an Area Under the Curve (AUC) in the range of clinical CESM diagnostic results. Conclusion: This CAD is the first development of a lesion detection and classification model for CESM images. Trained on a large dataset, it has the potential to be used for helping the management of biopsy decision and for helping the radiologist detecting complex lesions that could modify the clinical treatment.
Decentralized Bandits with Feedback for Cognitive Radar Networks
Authors: William Howard, R. Michael Buehrer
Date: 2022-07-20 00:00:00

Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks. Rather than model each radar node as an independent learner, entirely unable to swap information with other nodes in a network, in this work we construct a "central coordinator" to facilitate the exchange of information between radar nodes. We show that in interference-limited spectrum, where the signal to interference plus noise (SINR) ratio for the available bands may vary by location, a cognitive radar network (CRN) is able to use information from a central coordinator to reduce the number of time steps required to attain a given localization error. Importantly, each node is still able to learn separately. We provide a description of a network which has hybrid cognition in both a central coordinator and in each of the cognitive radar nodes, and examine the online machine learning algorithms which can be implemented in this structure.
Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges
Authors: Fuxin Zhang, Chunbo Luo, Jialang Xu, Yang Luo, FuChun Zheng
Date: 2022-07-20 00:00:00
Tasks: Automatic Modulation Recognition, Deep Learning

Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information, and provides the essential function when such information is missing. Recent breakthroughs in deep learning (DL) have laid the foundation for developing high-performance DL-AMR approaches for communications systems. Comparing with traditional modulation detection methods, DL-AMR approaches have achieved promising performance including high recognition accuracy and low false alarms due to the strong feature extraction and classification abilities of deep neural networks. Despite the promising potential, DL-AMR approaches also bring concerns to complexity and explainability, which affect the practical deployment in wireless communications systems. This paper aims to present a review of the current DL-AMR research, with a focus on appropriate DL models and benchmark datasets. We further provide comprehensive experiments to compare the state of the art models for single-input-single-output (SISO) systems from both accuracy and complexity perspectives, and propose to apply DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with precoding. Finally, existing challenges and possible future research directions are discussed.
Lens Antenna Arrays-Assisted mmWave MU-MIMO Uplink Transmission: Joint Beam Selection and Phase-Only Beamforming Design
Authors: Chongjun Ouyang, Hao Xu, Xujie Zang, Hongwen Yang
Date: 2022-07-20 00:00:00

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem, a penalty dual decomposition (PDD)-based iterative algorithm is developed via capitalizing on the weighted minimum mean square error (WMMSE), block coordinate descent (BCD), and minorization-maximization (MM) techniques. Moreover, a low-complexity sequential optimization (SO)-based algorithm is proposed at the cost of a slight sum rate performance loss. Numerical results demonstrate that the proposed methods can achieve higher sum rates than state-of-the-art methods.
Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion
Authors: Xuan Chen, Fei Ji, Miaowen Wen, Yu Huang, Yuankun Tang, Andrew W. Eckford
Date: 2022-07-19 00:00:00

In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration.
Estimating generation time of SARS-CoV-2 variants from the daily incidence rate
Authors: Eugenio Lippiello, Giuseppe Petrillo, Lucilla de Arcangelis
Date: 2022-07-20 00:00:00

The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy we find an average generation time $z = 3.2 \pm 0.8$ days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value $z = 6.2 \pm 1.1$ days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter $z$, was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore indicate that the novel approach can indicate the existence of virus variants resulting particularly useful in situations when information about genomic sequencing is not yet available.
A mechanistic model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities
Authors: Julian Heidecke, Jan Fuhrmann, Maria Vittoria Barbarossa
Date: 2022-07-19 00:00:00
Tasks: Diagnostic

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts - often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy - is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks requires us to be aware about the effectiveness of such containment strategies. This can be evaluated using mathematical models. We present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates a detailed mechanistic description of the state-dependent dynamics induced by limited TTIQ capacities. In addition, we account for transmission during the early phase of SARS-CoV-2 infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Numerical experiments, inspired by the early spread of COVID-19 in Germany, reveal the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent - representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses emphasize factors, partially related to the specific disease, which impede or enhance the success of TTIQ. Studying the diminishing effectiveness of TTIQ along simulations of an epidemic wave we highlight consequences for intervention strategies.
Deep learning-based identification of sub-nuclear structures in FIB-SEM images
Authors: Niraj Gupta, Eric J. Roberts, Song Pang, C. Shan Xu, Harald F. Hess, Fan Wu, Abby Dernburg, Danielle Jorgens, Petrus H. Zwart, Vignesh Kasinath
Date: 2022-07-19 00:00:00
Tasks: Electron Tomography, Morphological Analysis

Three-dimensional volumetric imaging of cells allows for in situ visualization, thus preserving contextual insights into cellular processes. Despite recent advances in machine learning methods, morphological analysis of sub-nuclear structures have proven challenging due to both the shallow contrast profile and the technical limitation in feature detection. Here, we present a convolutional neural network, supervised deep learning-based approach which can identify sub-nuclear structures with 90% accuracy. We develop and apply this model to C. elegans gonads imaged using focused ion beam milling combined with scanning electron microscopy resulting in the accurate identification and segmentation of all sub-nuclear structures including entire chromosomes. We discuss in depth the architecture, parameterization, and optimization of the deep learning model, as well as provide evaluation metrics to assess the quality of the network prediction. Lastly, we highlight specific aspects of the model that can be optimized for its broad application to other volumetric imaging data as well as in situ cryo-electron tomography.
DDPG based on multi-scale strokes for financial time series trading strategy
Authors: Jun-Cheng Chen, Cong-Xiao Chen, Li-Juan Duan, Zhi Cai
Date: 2022-06-05 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Time Series, Time Series Analysis

With the development of artificial intelligence,more and more financial practitioners apply deep reinforcement learning to financial trading strategies.However,It is difficult to extract accurate features due to the characteristics of considerable noise,highly non-stationary,and non-linearity of single-scale time series,which makes it hard to obtain high returns.In this paper,we extract a multi-scale feature matrix on multiple time scales of financial time series,according to the classic financial theory-Chan Theory,and put forward to an approach of multi-scale stroke deep deterministic policy gradient reinforcement learning model(MSSDDPG)to search for the optimal trading strategy.We carried out experiments on the datasets of the Dow Jones,S&P 500 of U.S. stocks, and China's CSI 300,SSE Composite,evaluate the performance of our approach compared with turtle trading strategy, Deep Q-learning(DQN)reinforcement learning strategy,and deep deterministic policy gradient (DDPG) reinforcement learning strategy.The result shows that our approach gets the best performance in China CSI 300,SSE Composite,and get an outstanding result in Dow Jones,S&P 500 of U.S.
Predicting Word Learning in Children from the Performance of Computer Vision Systems
Authors: Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths
Date: 2022-07-07 00:00:00
Tasks: Image Captioning

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.
Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals
Authors: Jamell Dacon, Harry Shomer, Shaylynn Crum-Dacon, Jiliang Tang
Date: 2022-06-15 00:00:00

Online discussions, panels, talk page edits, etc., often contain harmful conversational content i.e., hate speech, death threats and offensive language, especially towards certain demographic groups. For example, individuals who identify as members of the LGBTQIA+ community and/or BIPOC (Black, Indigenous, People of Color) are at higher risk for abuse and harassment online. In this work, we first introduce a real-world dataset that will enable us to study and understand harmful online conversational content. Then, we conduct several exploratory data analysis experiments to gain deeper insights from the dataset. We later describe our approach for detecting harmful online Anti-LGBTQIA+ conversational content, and finally, we implement two baseline machine learning models (i.e., Support Vector Machine and Logistic Regression), and fine-tune 3 pre-trained large language models (BERT, RoBERTa, and HateBERT). Our findings verify that large language models can achieve very promising performance on detecting online Anti-LGBTQIA+ conversational content detection tasks.