pwc-archive/papers-with-abstracts huggingface dataset:
Spatial Loss for Unsupervised Multi-channel Source Separation
Authors: Kohei Saijo, Robin Scheibler
Date: 2022-04-01 00:00:00
Tasks: blind source separation

We propose a spatial loss for unsupervised multi-channel source separation. The proposed loss exploits the duality of direction of arrival (DOA) and beamforming: the steering and beamforming vectors should be aligned for the target source, but orthogonal for interfering ones. The spatial loss encourages consistency between the mixing and demixing systems from a classic DOA estimator and a neural separator, respectively. With the proposed loss, we train the neural separators based on minimum variance distortionless response (MVDR) beamforming and independent vector analysis (IVA). We also investigate the effectiveness of combining our spatial loss and a signal loss, which uses the outputs of blind source separation as the reference. We evaluate our proposed method on synthetic and recorded (LibriCSS) mixtures. We find that the spatial loss is most effective to train IVA-based separators. For the neural MVDR beamformer, it performs best when combined with a signal loss. On synthetic mixtures, the proposed unsupervised loss leads to the same performance as a supervised loss in terms of word error rate. On LibriCSS, we obtain close to state-of-the-art performance without any labeled training data.
4Weed Dataset: Annotated Imagery Weeds Dataset
Authors: Varun Aggarwal, Aanis Ahmad, Aaron Etienne, Dharmendra Saraswat
Date: 2022-03-29 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, object-detection, Object Detection

Weeds are a major threat to crops and are responsible for reducing crop yield worldwide. To mitigate their negative effect, it is advantageous to accurately identify them early in the season to prevent their spread throughout the field. Traditionally, farmers rely on manually scouting fields and applying herbicides for different weeds. However, it is easy to confuse between crops with weeds during the early growth stages. Recently, deep learning-based weed identification has become popular as deep learning relies on convolutional neural networks that are capable of learning important distinguishable features between weeds and crops. However, training robust deep learning models requires access to large imagery datasets. Therefore, an early-season weeds dataset was acquired under field conditions. The dataset consists of 159 Cocklebur images, 139 Foxtail images, 170 Redroot Pigweed images and 150 Giant Ragweed images corresponding to four common weed species found in corn and soybean production systems.. Bounding box annotations were created for each image to prepare the dataset for training both image classification and object detection deep learning networks capable of accurately locating and identifying weeds within corn and soybean fields. (https://osf.io/w9v3j/)
An Approximate MSE Expression for Maximum Likelihood and Other Implicitly Defined Estimators of Non-Random Parameters (extended version)
Authors: Erdal Mehmetcik, Umut Orguner, Çağatay Candan
Date: 2022-04-01 00:00:00
Tasks: parameter estimation

An approximate mean square error (MSE) expression for the performance analysis of implicitly defined estimators of non-random parameters is proposed. An implicitly defined estimator (IDE) declares the minimizer/maximizer of a selected cost/reward function as the parameter estimate. The maximum likelihood (ML) and the least squares estimators are among the well known examples of this class. In this paper, an exact MSE expression for implicitly defined estimators with a symmetric and unimodal objective function is given. It is shown that the expression reduces to the Cramer-Rao lower bound (CRLB) and misspecified CRLB in the large sample size regime for ML and misspecified ML estimation, respectively. The expression is shown to yield the Ziv-Zakai bound (without the valley filling function) for the maximum a posteriori (MAP) estimator when it is used in a Bayesian setting, that is, when an a-priori distribution is assigned to the unknown parameter. In addition, extension of the suggested expression to the case of nuisance parameters is studied and some approximations are given to ease the computations for this case. Numerical results indicate that the suggested MSE expression not only predicts the estimator performance in the asymptotic region; but it is also applicable for the threshold region analysis, even for IDEs whose objective functions do not satisfy the symmetry and unimodality assumptions. Advantages of the suggested MSE expression are its conceptual simplicity and its relatively straightforward numerical calculation due to the reduction of the estimation problem to a binary hypothesis testing problem, similar to the usage of Ziv-Zakai bounds in random parameter estimation problems.
A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure
Authors: Ping Liu, Hai Zhang
Date: 2022-04-01 00:00:00
Tasks: subspace methods

We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain. Classical subspace methods (e.g., MUSIC algorithm, Matrix Pencil method, etc.) show great superiority in resolving closely spaced sources, but their computational cost is usually heavy. This is especially the case for point sources with a multi-cluster structure which requires processing large-sized data matrix resulted from highly sampled measurements. To address this issue, we propose a fast algorithm termed D-MUSIC, based on a measurement decoupling strategy. We demonstrate theoretically that for point sources with a known cluster structure, their measurement can be decoupled into local measurements of each of the clusters by solving a system of linear equations that are obtained by using a multipole basis. We further develop a subsampled MUSIC algorithm to detect the cluster structure and utilize it to decouple the global measurement. In the end, the MUSIC algorithm was applied to each local measurement to resolve point sources therein. Compared to the standard MUSIC algorithm, the proposed algorithm has comparable super-resolving capability while having a much lower computational complexity.
Atomic Filter: a Weak Form of Shift Operator for Graph Signals
Authors: Lihua Yang, Qing Zhang, Qian Zhang, Chao Huang
Date: 2022-04-01 00:00:00
Tasks: Form

The shift operation plays a crucial role in the classical signal processing. It is the generator of all the filters and the basic operation for time-frequency analysis, such as windowed Fourier transform and wavelet transform. With the rapid development of internet technology and big data science, a large amount of data are expressed as signals defined on graphs. In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals. It is a fundamental problem since the vertex set of a graph is usually not a vector space and the addition operation cannot be defined on the vertex set of the graph. In this paper, based on our understanding on the core role of shift operation in classical signal processing we propose the concept of atomic filters, which can be viewed as a weak form of the shift operator for graph signals. Then, we study the conditions such that an atomic filter is norm-preserving, periodic, or real-preserving. The property of real-preserving holds naturally in the classical signal processing, but no the research has been reported on this topic in the graph signal setting. With these conditions we propose the concept of normal atomic filters for graph signals, which degenerates into the classical shift operator under mild conditions if the graph is circulant. Typical examples of graphs that have or have not normal atomic filters are given. Finally, as an application, atomic filters are utilized to construct time-frequency atoms which constitute a frame of the graph signal space.
Optimizing Pilot Spacing in MU-MIMO Systems Operating Over Aging Channels
Authors: Sebastian Fodor, Gábor Fodor, Doğa Gürgünoğlu, Miklós Telek
Date: 2022-04-01 00:00:00

In the uplink of multiuser multiple input multiple output (MU-MIMO) systems operating over aging channels, pilot spacing is crucial for acquiring channel state information and achieving high signal-to-interference-plus-noise ratio (SINR). Somewhat surprisingly, very few works examine the impact of pilot spacing on the correlation structure of subsequent channel estimates and the resulting quality of channel state information considering channel aging. In this paper, we consider a fast-fading environment characterized by its exponentially decaying autocorrelation function, and model pilot spacing as a sampling problem to capture the inherent trade-off between the quality of channel state information and the number of symbols available for information carrying data symbols. We first establish a quasi-closed form for the achievable asymptotic deterministic equivalent SINR when the channel estimation algorithm utilizes multiple pilot signals. Next, we establish upper bounds on the achievable SINR and spectral efficiency, as a function of pilot spacing, which helps to find the optimum pilot spacing within a limited search space. Our key insight is that to maximize the achievable SINR and the spectral efficiency of MU-MIMO systems, proper pilot spacing must be applied to control the impact of the aging channel and to tune the trade-off between pilot and data symbols.
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Authors: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, YiXuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li
Date: 2022-04-01 00:00:00
Tasks: Self-Supervised Learning

Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.
Low-fat diets and testosterone in men: Systematic review and meta-analysis of intervention studies
Authors: Joseph Whittaker, Kexin Wu
Date: 2022-03-31 00:00:00

Background: Higher endogenous testosterone levels are associated with reduced chronic disease risk and mortality. Since the mid-20th century, there have been significant changes in dietary patterns, and men's testosterone levels have declined in western countries. Cross-sectional studies show inconsistent associations between fat intake and testosterone in men. Methods: Studies eligible for inclusion were intervention studies, with minimal confounding variables, comparing the effect of low-fat vs high-fat diets on men's sex hormones. 9 databases were searched from their inception to October 2020, yielding 6 eligible studies, with a total of 206 participants. Random effects meta-analyses were performed using Cochrane's Review Manager software. Cochrane's risk of bias tool was used for quality assessment. Results: There were significant decreases in sex hormones on low-fat vs high-fat diets. Standardised mean differences with 95% confidence intervals (CI) for outcomes were: total testosterone [-0.38 (95% CI -0.75 to -0.01) P = 0.04]; free testosterone [-0.37 (95% CI -0.63 to -0.11) P = 0.005]; urinary testosterone [-0.38 (CI 95% -0.66 to -0.09) P = 0.009], and dihydrotestosterone [-0.3 (CI 95% -0.56 to -0.03) P = 0.03]. There were no significant differences for luteinising hormone or sex hormone binding globulin. Subgroup analysis for total testosterone, European and American men, showed a stronger effect [-0.52 (95% CI -0.75 to -0.3) P < 0.001]. Conclusions: Low-fat diets appear to decrease testosterone levels in men, but further randomised controlled trials are needed to confirm this effect. Men with European ancestry may experience a greater decrease in testosterone, in response to a low-fat diet.
The return of (I)DeFiX
Authors: Florentina Şoiman, Guillaume Dumas, Sonia Jimenez-Garces
Date: 2022-04-01 00:00:00

Decentralized Finance (DeFi) is a nascent set of financial services, using tokens, smart contracts, and blockchain technology as financial instruments. We investigate four possible drivers of DeFi returns: exposure to cryptocurrency market, the network effect, the investor's attention, and the valuation ratio. As DeFi tokens are distinct from classical cryptocurrencies, we design a new dedicated market index, denoted DeFiX. First, we show that DeFi tokens returns are driven by the investor's attention on technical terms such as "decentralized finance" or "DeFi", and are exposed to their own network variables and cryptocurrency market. We construct a valuation ratio for the DeFi market by dividing the Total Value Locked (TVL) by the Market Capitalization (MC). Our findings do not support the TVL/MC predictive power assumption. Overall, our empirical study shows that the impact of the cryptocurrency market on DeFi returns is stronger than any other considered driver and provides superior explanatory power.
No arbitrage global parametrization for the eSSVI volatility surface
Authors: Arianna Mingone
Date: 2022-04-01 00:00:00

The article describes a global and arbitrage-free parametrization of the eSSVI surfaces introduced by Hendriks and Martini in 2019. A robust calibration of such surfaces has already been proposed by the quantitative research team at Zeliade in 2019, but it is sequential in expiries and lacks of a global view on the surface. The alternative calibration suggested in this article is faster and always guarantees an arbitrage-free fit of market data.
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness
Authors: Souvik Kundu, Sairam Sundaresan, Massoud Pedram, Peter A. Beerel
Date: 2022-03-28 00:00:00
Tasks: image-classification, Image Classification

Existing models that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on convolution operations conditioned with feature-wise linear modulation (FiLM) layers. These layers require many new parameters and are hyperparameter sensitive. They significantly increase training time, memory cost, and potential latency which can prove costly for resource-limited or real-time applications. In this paper, we present a fast learnable once-for-all adversarial training (FLOAT) algorithm, which instead of the existing FiLM-based conditioning, presents a unique weight conditioned learning that requires no additional layer, thereby incurring no significant increase in parameter count, training time, or network latency compared to standard adversarial training. In particular, we add configurable scaled noise to the weight tensors that enables a trade-off between clean and adversarial performance. Extensive experiments show that FLOAT can yield SOTA performance improving both clean and perturbed image classification by up to ~6% and ~10%, respectively. Moreover, real hardware measurement shows that FLOAT can reduce the training time by up to 1.43x with fewer model parameters of up to 1.47x on iso-hyperparameter settings compared to the FiLM-based alternatives. Additionally, to further improve memory efficiency we introduce FLOAT sparse (FLOATS), a form of non-iterative model pruning and provide detailed empirical analysis to provide a three way accuracy-robustness-complexity trade-off for these new class of pruned conditionally trained models.
Small-World Propensity in Weighted, Real-World Networks
Authors: Sarah Feldt Muldoon, Eric W. Bridgeford, Danielle S. Bassett
Date: 2015-05-08 00:00:00

Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems. Small-world structure, commonly diagnosed by high local clustering yet short average path length between any two nodes, directly enables information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-world structure are dependent on nuisance variables such as density and agnostic to critical variables such as the strengths of connections between nodes, thereby hampering accurate and comparable assessments of small-world structure in different networks. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a stringent and generalizable method for mapping real-world data onto the theoretical model. In applying these techniques to real world brain networks, we uncover the surprising fact that the canonical example of a biological small-world network, the C. elegans neuronal network, has strikingly low SWP in comparison to other examined brain networks. These metrics, models, and maps form a coherent toolbox for the assessment of architectural properties in real-world networks and their statistical comparison across conditions.
Mesenchymal stem cells as carrier cells to enable effective intratumoral delivery of oncolytic virus for oncolytic virotherapy: a systematic review
Authors: Chottiwatt Jittprasong
Date: 2022-04-04 00:00:00

Oncolytic viruses, which may be naturally occurring or genetically engineered, are a type of virus that infects and destroy cancer cells preferentially. Owing to their selectivity, they outperform conventional chemotherapy and radiotherapy, which both have a tendency to impact non-target cells and cause unwanted adverse side effects. Oncolytic virotherapy is a type of cancer treatment in which oncolytic viruses are deliberately introduced into patients affected with cancers in order for them to infect and destroy cancer cells locally or systemically, in a manner analogous to chemotherapy but with a greater degree of selectivity. Multiple studies indicate that oncolytic virotherapy is effective in vitro but in vivo findings remain ambiguous due to the approach's primary limitation: inefficient therapeutic agent delivery to its target, which is heavily influenced by the immune system. Here, we propose overcoming this limitation by exploiting a recent discovery in cancer research: a carrier cell. By exploiting their tumor-promoting activities, mesenchymal stem cells may be employed for cancer therapy by serving as a carrier for the oncolytic viruses toward their target. This approach directly addresses the limitation of conventional oncolytic virotherapy, where oncolytic viruses are often poorly delivered after systemic administration.
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation
Authors: Manthan Thakker, Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang
Date: 2022-04-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Knowledge Distillation, Multi-Task Learning, Speech Enhancement, speech-recognition, Speech Recognition

This paper investigates how to improve the runtime speed of personalized speech enhancement (PSE) networks while maintaining the model quality. Our approach includes two aspects: architecture and knowledge distillation (KD). We propose an end-to-end enhancement (E3Net) model architecture, which is $3\times$ faster than a baseline STFT-based model. Besides, we use KD techniques to develop compressed student models without significantly degrading quality. In addition, we investigate using noisy data without reference clean signals for training the student models, where we combine KD with multi-task learning (MTL) using automatic speech recognition (ASR) loss. Our results show that E3Net provides better speech and transcription quality with a lower target speaker over-suppression (TSOS) rate than the baseline model. Furthermore, we show that the KD methods can yield student models that are $2-4\times$ faster than the teacher and provides reasonable quality. Combining KD and MTL improves the ASR and TSOS metrics without degrading the speech quality.
Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation
Authors: Mohammed A. M. Elhassan, Chenhui Yang, Chenxi Huang, Tewodros Legesse Munea
Date: 2022-04-04 00:00:00
Tasks: Segmentation, Semantic Segmentation

The following is a technical report to test the validity of the proposed Subspace Pyramid Fusion Module (SPFM) to capture multi-scale feature representations, which is more useful for semantic segmentation. In this investigation, we have proposed the Efficient Shuffle Attention Module(ESAM) to reconstruct the skip-connections paths by fusing multi-level global context features. Experimental results on two well-known semantic segmentation datasets, including Camvid and Cityscapes, show the effectiveness of our proposed method.
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
Authors: Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths
Date: 2022-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Inductive Bias, Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building "task metamers" that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.