pwc-archive/papers-with-abstracts huggingface dataset:
IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces
Authors: Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
Date: 2021-10-25 00:00:00
Tasks: parameter estimation

The intelligent reflecting surface (IRS) technology has recently attracted a lot of interest in wireless communications research. An IRS consists of passive reflective elements capable of tuning the phase, amplitude, frequency and polarization of the impinging waveforms. Given such desirable properties, the wireless channel characteristics can be controlled and optimized for specific signal design and processing needs -- thus promising significant potential in radar applications. In this paper, we establish the theoretical foundations for introducing IRS into a radar system and study the potential to improve target parameter estimation. More specifically, we will investigate the deployment of IRS in cases where the line-of-sight (LOS) link is weak or blocked by obstructions. We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance. The effectiveness of such an IRS-provided virtual link in estimating the moving target parameters is illustrated under both optimized and non-optimized IRS scenarios. Numerical simulations indicate that the IRS can enhance the target parameter estimation when the LOS link is weaker than $\sim 10^{-1}$ in relative strength in comparison with the NLOS link.
Does TMS increase BOLD activity at the site of stimulation?
Authors: Farshad Rafiei, Dobromir Rahnev
Date: 2021-10-26 00:00:00

Transcranial magnetic stimulation (TMS) is widely used for understanding brain function in neurologically intact subjects and for the treatment of various disorders. However, the precise neurophysiological effects of TMS at the site of stimulation remain poorly understood. The local effects of TMS can be studied using concurrent TMS-fMRI, a technique where TMS is delivered during fMRI scanning. However, although concurrent TMS-fMRI was developed over 20 years ago and dozens of studies have used this technique, there is still no consensus on whether TMS increases blood-oxygen-level-dependent (BOLD) activity at the site of stimulation. To address this question, here we review all previous concurrent TMS-fMRI studies that reported analyses of BOLD activity at the target location. We find evidence that TMS increases local BOLD activity when stimulating the primary motor and visual cortices but that these effects are likely driven by the downstream consequences of TMS (finger twitches and phosphenes). However, TMS does not appear to increase BOLD activity at the site of stimulation for areas outside of the primary motor and visual cortices when conducted at rest. We examine the possible reasons for such lack of BOLD signal increase based on recent work in non-human animals. We argue that the current evidence points to TMS inducing periods of increased and decreased neuronal firing that mostly cancel each other out and therefore lead to no change in the overall BOLD signal.
Frequency and frequency modulation share the same predictive encoding mechanisms in human auditory cortex
Authors: Jasmin Stein, Katharina von Kriegstein, Alejandro Tabas
Date: 2021-10-26 00:00:00
Tasks: Prediction

Expectations can substantially influence perception. Predictive coding is a theory of sensory processing that aims to explain the neural mechanisms underlying the effect of expectations in sensory processing. Its main assumption is that sensory neurons encode prediction error with respect to expected sensory input. Neural populations encoding prediction error have been previously reported in the human auditory cortex (AC); however, most studies focused on the encoding of pure tones and induced expectations by stimulus repetition, potentially confounding prediction error with effects of neural habituation. Here, we systematically studied prediction error to pure tones and fast frequency modulated (FM) sweeps across different auditory cortical fields in humans. We conducted two fMRI experiments, each using one type of stimulus. We measured BOLD responses across the bilateral auditory cortical fields Te1.0, Te1.1, Te1.2, and Te3 while participants listened to sequences of sounds. We induced subjective expectations on the incoming sounds independently of stimulus repetition using abstract rules. Our results indicate that pure tones and FM-sweeps are encoded as prediction error with respect to the participants' expectations across auditory cortical fields. The topographical distribution of neural populations encoding prediction error to pure tones and FM-sweeps was highly correlated in left Te1.1 and Te1.2, and in bilateral Te3, suggesting that predictive coding is the general encoding mechanism in AC.
Scalable Bayesian divergence time estimation with ratio transformations
Authors: Xiang Ji, Alexander A. Fisher, Shuo Su, Jeffrey L. Thorne, Barney Potter, Philippe Lemey, Guy Baele, Marc A. Suchard
Date: 2021-10-25 00:00:00

Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly-correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original N - 1 internal node heights into a space of one height parameter and N - 2 ratio parameters. To make analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in four pathogenic virus phylogenies: West Nile virus, rabies virus, Lassa virus and Ebola virus. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples. Our method also makes it now computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study and reveals clearer multimodal distributions of the divergence times of some clades of interest.
Efficient ISDA Initial Margin Calculations Using Least Squares Monte-Carlo
Authors: Asif Lakhany, Amber Zhang
Date: 2021-10-25 00:00:00

Non-cleared bilateral OTC derivatives between two financial firms or systemically important non-financial entities are subject to regulations that require the posting of initial and variation margin. The ISDA standard approach (SIMM) provides a way for computing the initial margin. It involves computing sensitivities of the contracts with respect to several market factors. In this paper, the authors extend the well known LSMC technique to efficiently estimate the sensitivities required in the ISDA SIMM methodology.
Large Platonic Markets with Delays
Authors: Yannick Limmer, Thilo Meyer-Brandis
Date: 2021-10-26 00:00:00

The objective is to develop a general stochastic approach to delays on financial markets. We suggest such a concept in the context of large platonic markets, which allow infinitely many assets and incorporate a restricted information setting. The discussion is divided into information delays and order execution delays. The former enables modelling of markets where the observed information is delayed, while the latter provides the opportunity to defer the indexed time of a received asset price. Both delays may be designed randomly and inhomogeneously over time. We show that delayed markets are equipped with a fundamental theorem of asset pricing and our main result is inheritance of the no asymptotic Lp-free lunch condition under both delay types. Eventually, we suggest an approach to verify absence of Lp-free lunch on markets with multiple brokers endowed with deviating trading speeds.
Zero-Liquidation Loans: A Structured Product Approach to DeFi Lending
Authors: Aetienne Sardon
Date: 2021-10-26 00:00:00

Zero-Liquidation loans allow DeFi users to borrow USDC against their ETH holdings, but without the risk of being liquidated in case of LTV shortfalls. This is achieved by giving users the option to repay their loans, either in USDC or through their previously pledged ETH (the concept can be generalized to other currency pairs as well). Liquidity providers, on the other hand side, are compensated with a higher yield for bearing the ETH downside risk. A positive side effect of zero-liquidation loans is that they are more robust against flash crashes and have a lower financial contagion effect than current lending and borrowing protocols.
Wealth heterogeneity in a closed pooled annuity fund
Authors: Thomas Bernhardt, Ge Qu
Date: 2021-10-26 00:00:00

The stability of income payments in a pooled annuity fund is studied. In those funds, members receive a fluctuating income depending on their experienced mortality in exchange for their pension savings. The focus is on describing the influence of different initial savings on the ability of the fund to provide a stable income in retirement. Because of this, members coincide in their characteristics except for their initial savings. We identify a term, which we dub ``implied number of homogeneous members'', that directly links the initial savings to the size of the income fluctuations. Our main contribution is the analysis of this term and the development of a criterion to answer the question of whether or not a given group of same-aged people should pool their funds together.
Heterogenous criticality in high frequency finance: a phase transition in flash crashes
Authors: Jeremy Turiel, Tomaso Aste
Date: 2021-10-26 00:00:00

Flash crashes in financial markets have become increasingly important attracting attention from financial regulators, market makers as well as from the media and the broader audience. Systemic risk and propagation of shocks in financial markets is also a topic of great relevance that attracted increasing attention in recent years. In the present work we bridge the gap between these two topics with an in-depth investigation of the systemic risk structure of co-crashes in high frequency trading. We find that large co-crashes are systemic in their nature and differ from small crashes. We demonstrate that there is a phase transition between co-crashes of small and large sizes, where the former involves mostly illiquid stocks while large and liquid stocks are the most represented and central in the latter. This suggests that systemic effects and shock propagation might be triggered by simultaneous withdrawals or movement of liquidity by HFTs, arbitrageurs and market makers with cross-asset exposures.
Active Learning for Deep Visual Tracking
Authors: Di Yuan, Xiaojun Chang, Yi Yang, Qiao Liu, Dehua Wang, Zhenyu He
Date: 2021-10-17 00:00:00
Tasks: Active Learning, Diversity, Visual Tracking

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this paper, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNNs model. Under the guidance of active learning, the tracker based on the trained deep CNNs model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multi-frame collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest neighbor discrimination method based on the average nearest neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks.
Semantic Segmentation for Urban-Scene Images
Authors: Shorya Sharma
Date: 2021-10-20 00:00:00
Tasks: Autonomous Driving, Image Segmentation, Segmentation, Semantic Segmentation

Urban-scene Image segmentation is an important and trending topic in computer vision with wide use cases like autonomous driving [1]. Starting with the breakthrough work of Long et al. [2] that introduces Fully Convolutional Networks (FCNs), the development of novel architectures and practical uses of neural networks in semantic segmentation has been expedited in the recent 5 years. Aside from seeking solutions in general model design for information shrinkage due to pooling, urban-scene image itself has intrinsic features like positional patterns [3]. Our project seeks an advanced and integrated solution that specifically targets urban-scene image semantic segmentation among the most novel approaches in the current field. We re-implement the cutting edge model DeepLabv3+ [4] with ResNet-101 [5] backbone as our strong baseline model. Based upon DeepLabv3+, we incorporate HANet [3] to account for the vertical spatial priors in urban-scene image tasks. To boost up model efficiency and performance, we further explore the Atrous Spatial Pooling (ASP) layer in DeepLabv3+ and infuse a computational efficient variation called "Waterfall" Atrous Spatial Pooling (WASP) [6] architecture in our model. We find that our two-step integrated model improves the mean Intersection-Over-Union (mIoU) score gradually from the baseline model. In particular, HANet successfully identifies height-driven patterns and improves per-class IoU of common class labels in urban scenario like fence and bus. We also demonstrate the improvement of model efficiency with help of WASP in terms of computational times during training and parameter reduction from the original ASPP module.
Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
Authors: Shuo Jiang, Jianxi Luo
Date: 2021-10-21 00:00:00

Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks
Authors: Leonardos Pantiskas, Kees Verstoep, Henri Bal
Date: 2021-01-01 00:00:00
Proceeding: IEEE Symposium Series on Computational Intelligence (SSCI) 2021 1
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach.
On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements
Authors: Suchuan Dong, Jielin Yang
Date: 2021-10-27 00:00:00

We consider the use of extreme learning machines (ELM) for computational partial differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are assigned to random values generated on $[-R_m,R_m]$ and fixed, where $R_m$ is a user-provided constant, and the output-layer coefficients are trained by a linear or nonlinear least squares computation. We present a method for computing the optimal value of $R_m$ based on the differential evolution algorithm. The presented method enables us to illuminate the characteristics of the optimal $R_m$ for two types of ELM configurations: (i) Single-Rm-ELM, in which a single $R_m$ is used for generating the random coefficients in all the hidden layers, and (ii) Multi-Rm-ELM, in which multiple $R_m$ constants are involved with each used for generating the random coefficients of a different hidden layer. We adopt the optimal $R_m$ from this method and also incorporate other improvements into the ELM implementation. In particular, here we compute all the differential operators involving the output fields of the last hidden layer by a forward-mode auto-differentiation, as opposed to the reverse-mode auto-differentiation in a previous work. These improvements significantly reduce the network training time and enhance the ELM performance. We systematically compare the computational performance of the current improved ELM with that of the finite element method (FEM), both the classical second-order FEM and the high-order FEM with Lagrange elements of higher degrees, for solving a number of linear and nonlinear PDEs. It is shown that the current improved ELM far outperforms the classical FEM. Its computational performance is comparable to that of the high-order FEM for smaller problem sizes, and for larger problem sizes the ELM markedly outperforms the high-order FEM.
Does the Data Induce Capacity Control in Deep Learning?
Authors: Rubing Yang, Jialin Mao, Pratik Chaudhari
Date: 2021-10-27 00:00:00
Tasks: Deep Learning, Generalization Bounds

We show that the input correlation matrix of typical classification datasets has an eigenspectrum where, after a sharp initial drop, a large number of small eigenvalues are distributed uniformly over an exponentially large range. This structure is mirrored in a network trained on this data: we show that the Hessian and the Fisher Information Matrix (FIM) have eigenvalues that are spread uniformly over exponentially large ranges. We call such eigenspectra "sloppy" because sets of weights corresponding to small eigenvalues can be changed by large magnitudes without affecting the loss. Networks trained on atypical datasets with non-sloppy inputs do not share these traits and deep networks trained on such datasets generalize poorly. Inspired by this, we study the hypothesis that sloppiness of inputs aids generalization in deep networks. We show that if the Hessian is sloppy, we can compute non-vacuous PAC-Bayes generalization bounds analytically. By exploiting our empirical observation that training predominantly takes place in the non-sloppy subspace of the FIM, we develop data-distribution dependent PAC-Bayes priors that lead to accurate generalization bounds using numerical optimization.
Temporal-attentive Covariance Pooling Networks for Video Recognition
Authors: Zilin Gao, Qilong Wang, Bingbing Zhang, QinGhua Hu, Peihua Li
Date: 2021-10-27 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Video Recognition

For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video architectures usually generate it by using a simple, global average pooling (GAP) method, which has limited ability to capture complex dynamics of videos. For image recognition task, there exist evidences showing that covariance pooling has stronger representation ability than GAP. Unfortunately, such plain covariance pooling used in image recognition is an orderless representative, which cannot model spatio-temporal structure inherent in videos. Therefore, this paper proposes a Temporal-attentive Covariance Pooling(TCP), inserted at the end of deep architectures, to produce powerful video representations. Specifically, our TCP first develops a temporal attention module to adaptively calibrate spatio-temporal features for the succeeding covariance pooling, approximatively producing attentive covariance representations. Then, a temporal covariance pooling performs temporal pooling of the attentive covariance representations to characterize both intra-frame correlations and inter-frame cross-correlations of the calibrated features. As such, the proposed TCP can capture complex temporal dynamics. Finally, a fast matrix power normalization is introduced to exploit geometry of covariance representations. Note that our TCP is model-agnostic and can be flexibly integrated into any video architectures, resulting in TCPNet for effective video recognition. The extensive experiments on six benchmarks (e.g., Kinetics, Something-Something V1 and Charades) using various video architectures show our TCPNet is clearly superior to its counterparts, while having strong generalization ability. The source code is publicly available.
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Authors: Siyuan Zhang, Nan Jiang
Date: 2021-10-26 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Off-policy evaluation, Open-Ended Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product.