pwc-archive/papers-with-abstracts huggingface dataset:
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
Authors: Jinbo Chen, Mahdi Tarkhan, Hui Wu, Fereidoon Hashemi Noshahr, Jie Yang, Mohamad Sawan
Date: 2022-05-27 00:00:00

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.
Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements
Authors: Wenhao Luo, Yee Hui Lee, Lai Fern Ow, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel
Date: 2022-05-27 00:00:00
Tasks: GPR

Tree roots detection is a popular application of the Ground-penetrating radar (GPR). Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes. When the surface of the land is undulating (not flat), these typical hyperbolic fitting methods becomes inaccurate. This is because, the reflection patterns change with the uneven ground surfaces. When the soil surface is not flat, it is inaccurate to use the peak point of an asymmetric reflection pattern to identify the depth and horizontal position of the underground target. The reflection patterns of the complex shapes due to extreme surface variations results in analysis difficulties. Furthermore, when multiple objects are buried under an undulating ground, it is hard to judge their relative positions based on a B-scan that assumes a flat ground. In this paper, a roots fitting method based on electromagnetic waves (EM) travel time analysis is proposed to take into consideration the realistic undulating ground surface. A wheel-based (WB) GPR and an antenna-height-fixed (AHF) GPR System are presented, and their corresponding fitting models are proposed. The effectiveness of the proposed method is demonstrated and validated through numerical examples and field experiments.
Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs
Authors: Felix Schwock, Julien Bloch, Les Atlas, Shima Abadi, Azadeh Yazdan-Shahmorad
Date: 2022-05-27 00:00:00

Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing. The communication is modeled as a flow signals on the edges of a graph and naturally arises from a graph diffusion process. We apply the diffusion model to micro-electrocorticography (ECoG) recordings from sensorimotor cortex of two non-human primates to estimate the neural communication flow during excitatory optogenetics. Comparisons with a baseline model demonstrate that adding the neural flow can improve ECoG predictions. Finally, we demonstrate how the neural flow can be decomposed into a gradient and rotational component and show that the gradient component depends on the location of stimulation. This technique, for the first time, offers the opportunity to study neural communication on an unprecedented spatiotemporal scale.
Secure Performance Analysis of RIS-aided Wireless Communication Systems
Authors: Dinh-Thuan Do, Anh-Tu Le, Shahid Mumtaz
Date: 2022-05-26 00:00:00

Since Internet of Things (IoT) is suggested as the fundamental platform to adapt massive connections and secure transmission, we study physical-layer authentication in the point-to-point wireless systems relying on reconfigurable intelligent surfaces (RIS) technique. Due to lack of direct link from IoT devices (both legal and illegal devices) to the access point, we benefit from RIS by considering two main secure performance metrics. As main goal, we examine the secrecy performance of a RIS-aided wireless communication systems which show secure performance in the presence of an eavesdropping IoT devices. In this circumstance, RIS is placed between the access point and the legitimate devices and is designed to enhance the link security. To specify secure system performance metrics, we firstly present analytical results for the secrecy outage probability. Then, secrecy rate is further examined. Interestingly, we are to control both the average signal-to-noise ratio at the source and the number of metasurface elements of the RIS to achieve improved system performance. We verify derived expressions by matching Monte-Carlo simulation and analytical results.
Ancestral reproductive bias in branching processes
Authors: David Cheek, Samuel G. G. Johnston
Date: 2022-05-27 00:00:00

Consider a branching process with a homogeneous reproduction law. Sampling a single cell uniformly from the population at a time $T > 0$ and looking along the sampled cell's ancestral lineage, we find that the reproduction law is heterogeneous - the expected reproductive output of ancestral cells on the lineage from time $0$ to time $T$ continuously increases. This `inspection paradox' is due to sampling bias, that cells with a larger number of offspring are more likely to have one of their descendants sampled by virtue of their prolificity, and the bias's strength grows with the random population size and/or the sampling time $T$. Our main result explicitly characterises the evolution of reproduction rates and sizes along the sampled ancestral lineage as a mixture of Poisson processes, which simplifies in special cases. The ancestral bias helps to explain recently observed variation in mutation rates along lineages of the developing human embryo.
A typical workflow to simulate cytoskeletal systems with Cytosim
Authors: Carlos A. Lugo, Eashan Saikia, Francois Nedelec
Date: 2022-05-27 00:00:00

Many cytoskeletal systems are now sufficiently well known to permit their precise quantitative modelling. Microtubule and actin filaments are well characterized, and the associated proteins are often known, as well as their abundance and the interactions between these elements. Thus, computer simulations can be used to investigate the collective behavior of the system precisely, in a way that is complementary to experiments. Cytosim is an Open Source cytoskeleton simulation suite designed to handle large systems of flexible filaments with associated proteins such as molecular motors. It also offers the possibility to simulate passive crosslinkers, diffusible crosslinkers, nucleators, cutters and discrete versions of the motors that only step on unoccupied lattice sites on a filament. Other objects complement the filaments by offering spherical or more complicated geometry that can be used to represent chromosomes, nucleus or vesicles in the cell. Cytosim offers simple command-line tools for running a simulation and displaying its results, that are versatile and do not require programming skills. In this workflow, step-by-step instructions are given to: i) install the necessary environment on a new computer, ii) configure Cytosim to simulate the contraction of a 2D actomyosin network, iii) produce a visual representation of the system. Next, the system is probed by systematically varying a key parameter: the number of crosslinkers. Finally, the visual representation of the system is complemented by a numerical quantification of contractility to view, in a graph, how contractility depends on the composition of the system. Overall, these different steps constitute a typical workflow that can be applied with few modifications, to tackle many other problems in the cytoskeletal field.
DiMA: Sequence Diversity Dynamics Analyser for Viruses
Authors: Shan Tharanga, Eyyub Selim Unlu, Yongli Hu, Muhammad Farhan Sjaugi, Muhammet A. Celik, Hilal Hekimoglu, Olivo Miotto, Muhammed Miran Oncel, Asif M. Khan
Date: 2022-05-27 00:00:00
Tasks: Diagnostic, Diversity, Position

Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. DiMA provides a quantitative overview of sequence (nucleotide/protein) diversity by use of Shannon's entropy corrected for size bias, applied via a user-defined k-mer sliding window to an input alignment file, and each k-mer position is dissected to various diversity motifs. The motifs are defined based on the probability of distinct sequences at a given k-mer position, whereby an index is the predominant sequence, while all the others are (total) variants to the index. The total variants are sub-classified into the major (most common) variant, minor variants (occurring more than once and of frequency lower than the major), and the unique (singleton) variants. DiMA allows user-defined, sequence metadata enrichment for analyses of the motifs. The application of DiMA was demonstrated for the alignment data of the relatively conserved Spike protein (2,106,985 sequences) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the relatively highly diverse Pol protein (3,874) of human immunodeficiency virus-1 (HIV-1). The tool is publicly available as a web server (https://dima.bezmialem.edu.tr), as a Python library (via PyPi) and as a command line client (Via GitHub).
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding
Authors: Lu Zhang, Xiaowei Yu, Yanjun Lyu, Zhengwang Wu, Haixing Dai, Lin Zhao, Li Wang, Gang Li, Tianming Liu, Dajiang Zhu
Date: 2022-05-26 00:00:00
Tasks: Few-Shot Learning

Effective representation of brain anatomical architecture is fundamental in understanding brain regularity and variability. Despite numerous efforts, it is still difficult to infer reliable anatomical correspondence at finer scale, given the tremendous individual variability in cortical folding patterns. It is even more challenging to disentangle common and individual patterns when comparing brains at different neuro-developmental stages. In this work, we developed a novel learning-based few-shot embedding framework to encode the cortical folding patterns into a latent space represented by a group of anatomically meaningful embedding vectors. Specifically, we adopted 3-hinge (3HG) network as the substrate and designed an autoencoder-based embedding framework to learn a common embedding vector for each 3HG's multi-hop feature: each 3HG can be represented as a combination of these feature embeddings via a set of individual specific coefficients to characterize individualized anatomical information. That is, the regularity of folding patterns is encoded into the embeddings, while the individual variations are preserved by the multi=hop combination coefficients. To effectively learn the embeddings for the population with very limited samples, few-shot learning was adopted. We applied our method on adult HCP and pediatric datasets with 1,000+ brains (from 34 gestational weeks to young adult). Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.
Deep Generators on Commodity Markets; application to Deep Hedging
Authors: Nicolas Boursin, Carl Remlinger, Joseph Mikael, Carol Anne Hargreaves
Date: 2022-05-27 00:00:00
Tasks: Generative Adversarial Network, Time Series, Time Series Analysis

Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry. In this article, we focus on commodity markets and test four state-of-the-art generative methods, namely Time Series Generative Adversarial Network (GAN) Yoon et al. [2019], Causal Optimal Transport GAN Xu et al. [2020], Signature GAN Ni et al. [2020] and the conditional Euler generator Remlinger et al. [2021], are adapted and tested on commodity time series. A first series of experiments deals with the joint generation of historical time series on commodities. A second set deals with deep hedging of commodity options trained on he generated time series. This use case illustrates a purely data-driven approach to risk hedging.
Tsallis Relative entropy from asymmetric distributions as a risk measure for financial portfolios
Authors: Sandhya Devi, Sherman Page
Date: 2022-05-26 00:00:00

In an earlier study, we showed that Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to non-extensive systems, can be used as a possible risk measure in constructing risk optimal portfolios whose returns beat market returns. Over a long term (> 10 years), the risk-return profiles from TRE as the risk measure show a more consistent behavior than those from the commonly used risk measure 'beta' of the Capital Asset Pricing Model (CAPM). In these investigations, the model distributions derived from TRE are symmetric. However, observations show that distributions of the returns of financial markets and equities are in general asymmetric in positive and negative returns. In this work, we generalize TRE for the asymmetric case (ATRE) by considering the data distribution as a linear combination of two independent normalized distributions - one for negative returns and one for positive returns. Each of these two independent distributions are half q-Gaussians with different non-extensivity parameter q and temperature parameter b. The risk-return (in excess of market returns) patterns are investigated using ATRE as the risk measure. The results are compared with those from two other risk measures: TRE and the Tsallis relative entropy S- derived from the negative returns only. Tests on data, which include the dot-com bubble, the 2008 crash, and COVID periods, for both long (20 years) and shorter terms (10 years), show that a linear fit can be obtained for the risk-excess return profiles of all three risk measures. However, the fits for portfolios created during the chaotic market conditions (crashes) using S- as the risk show a much higher slope pointing to higher returns for a given risk value. Further, in this case, the excess returns of even short-term portfolios remain positive irrespective of the market behavior.
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward
Authors: Zhong Ji, Zhenfei Hu, Yaodong Wang, Shengjia Li
Date: 2022-05-21 00:00:00
Tasks: Attribute, Decision Making, Pedestrian Attribute Recognition, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper addresses it as a decision-making task via a reinforcement learning framework. Specifically, PAR is formulated as a Markov decision process (MDP) by designing ingenious states, action space, reward function and state transition. To alleviate the inter-attribute imbalance problem, we apply an Attribute Grouping Strategy (AGS) by dividing all attributes into subgroups according to their region and category information. Then we employ an agent to recognize each group of attributes, which is trained with Deep Q-learning algorithm. We also propose a Group Optimization Reward (GOR) function to alleviate the intra-attribute imbalance problem. Experimental results on the three benchmark datasets of PETA, RAP and PA100K illustrate the effectiveness and competitiveness of the proposed approach and demonstrate that the application of reinforcement learning to PAR is a valuable research direction.
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Authors: Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
Date: 2022-05-28 00:00:00
Tasks: Representation Learning, Visual Reasoning, zero-shot-classification, Zero-Shot Learning

Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior beliefs, we demonstrate that the image and text representations learned via a standard contrastive objective are not interchangeable and can lead to inconsistent downstream predictions. To mitigate this issue, we formalize consistency and propose CyCLIP, a framework for contrastive representation learning that explicitly optimizes for the learned representations to be geometrically consistent in the image and text space. In particular, we show that consistent representations can be learned by explicitly symmetrizing (a) the similarity between the two mismatched image-text pairs (cross-modal consistency); and (b) the similarity between the image-image pair and the text-text pair (in-modal consistency). Empirically, we show that the improved consistency in CyCLIP translates to significant gains over CLIP, with gains ranging from 10%-24% for zero-shot classification accuracy on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%-27% for robustness to various natural distribution shifts. The code is available at https://github.com/goel-shashank/CyCLIP.
MDMLP: Image Classification from Scratch on Small Datasets with MLP
Authors: Tian Lv, Chongyang Bai, Chaojie Wang
Date: 2022-05-28 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification

The attention mechanism has become a go-to technique for natural language processing and computer vision tasks. Recently, the MLP-Mixer and other MLP-based architectures, based simply on multi-layer perceptrons (MLPs), are also powerful compared to CNNs and attention techniques and raises a new research direction. However, the high capability of the MLP-based networks severely relies on large volume of training data, and lacks of explanation ability compared to the Vision Transformer (ViT) or ConvNets. When trained on small datasets, they usually achieved inferior results than ConvNets. To resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple and lightweight MLP-based architecture yet achieves SOTA when training from scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool (MDAttnTool), a novel and efficient attention mechanism based on MLPs. Even without strong data augmentation, MDMLP achieves 90.90% accuracy on CIFAR10 with only 0.3M parameters, while the well-known MLP-Mixer achieves 85.45% with 17.1M parameters. In addition, the lightweight MDAttnTool highlights objects in images, indicating its explanation power. Our code is available at https://github.com/Amoza-Theodore/MDMLP.
PSNet: Fast Data Structuring for Hierarchical Deep Learning on Point Cloud
Authors: Luyang Li, Ligang He, Jinjin Gao, Xie Han
Date: 2022-05-30 00:00:00

In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently results in poor scalability. This paper proposes a fast data structuring method called PSNet (Point Structuring Net). PSNet transforms the spatial features of the points and matches them to the features of local areas in a point cloud. PSNet achieves grouping and sampling at the same time while the existing methods process sampling and grouping in two separate steps (such as using FPS plus kNN). PSNet performs feature transformation pointwise while the existing methods uses the spatial relationship among the points as the reference for grouping. Thanks to these features, PSNet has two important advantages: 1) the grouping and sampling results obtained by PSNet is stable and permutation invariant; and 2) PSNet can be easily parallelized. PSNet can replace the data structuring methods in the mainstream point cloud deep learning models in a plug-and-play manner. We have conducted extensive experiments. The results show that PSNet can improve the training and inference speed significantly while maintaining the model accuracy.
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Authors: Yinghao Li, Le Song, Chao Zhang
Date: 2022-05-27 00:00:00
Tasks: Named Entity Recognition, Named Entity Recognition (NER), Weakly-Supervised Named Entity Recognition

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels. To work well, the label model needs to contextually identify and emphasize well-performed LFs while down-weighting the under-performers. However, evaluating the LFs is challenging due to the lack of ground truths. To address this issue, we propose the sparse conditional hidden Markov model (Sparse-CHMM). Instead of predicting the entire emission matrix as other HMM-based methods, Sparse-CHMM focuses on estimating its diagonal elements, which are considered as the reliability scores of the LFs. The sparse scores are then expanded to the full-fledged emission matrix with pre-defined expansion functions. We also augment the emission with weighted XOR scores, which track the probabilities of an LF observing incorrect entities. Sparse-CHMM is optimized through unsupervised learning with a three-stage training pipeline that reduces the training difficulty and prevents the model from falling into local optima. Compared with the baselines in the Wrench benchmark, Sparse-CHMM achieves a 3.01 average F1 score improvement on five comprehensive datasets. Experiments show that each component of Sparse-CHMM is effective, and the estimated LF reliabilities strongly correlate with true LF F1 scores.
Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models
Authors: Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
Date: 2022-05-28 00:00:00
Tasks: Method name prediction

Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input programs to improve the transparency of CI models. However, this approach is syntax-unaware and does not consider the grammar of the programming language. In this paper, we apply a syntax-guided program reduction technique that considers the grammar of the input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique is faster and provides smaller sets of key tokens in reduced programs. We also show that the key tokens could be used in generating adversarial examples for up to 65% of the input programs.