pwc-archive/papers-with-abstracts huggingface dataset:
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis
Authors: Chenglong Li, Sibren De Bast, Yang Miao, Emmeric Tanghe, Sofie Pollin, Wout Joseph
Date: 2022-08-23 00:00:00
Tasks: Activity Recognition, Compressive Sensing, Human Activity Recognition

Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with size of 6.5 m$\times$10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves the tracking purposes in real-time, which is promising for practical MTT use cases.
Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery with Manifold Learning
Authors: Ido Cohen, Dan Valsky, Ronen Talmon
Date: 2022-08-23 00:00:00

During Deep Brain Stimulation(DBS) surgery for treating Parkinson's disease, one vital task is to detect a specific brain area called the Subthalamic Nucleus(STN) and a sub-territory within the STN called the Dorsolateral Oscillatory Region(DLOR). Accurate detection of the STN borders is crucial for adequate clinical outcomes. Currently, the detection is based on human experts, guided by supervised machine learning detection algorithms. Consequently, this procedure depends on the knowledge and experience of particular experts and on the amount and quality of the labeled data used for training the machine learning algorithms. In this paper, to circumvent the dependence and bias caused by the training data, we present a data-driven unsupervised method for detecting the STN and the DLOR during DBS surgery. Our method is based on an agnostic modeling approach for general target detection tasks. Given a set of measurements, we extract features and propose a variant of the Mahalanobis distance between these features. We show theoretically that this distance enhances the differences between measurements with different intrinsic characteristics. Then, we incorporate the new features and distances into a manifold learning method, called Diffusion Maps. We show that this method gives rise to a representation that is consistent with the underlying factors that govern the measurements. Since the construction of this representation is carried out without rigid modeling assumptions, it can facilitate a wide range of detection tasks; here, we propose a specification for the STN and DLOR detection tasks. We present detection results on 25 sets of measurements recorded from 16 patients during surgery. Compared to a competing supervised algorithm based on a Hidden Markov Model, our unsupervised method demonstrates similar results in the STN detection task and superior results in the DLOR detection task.
Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems
Authors: Zhendong Peng, Cunhua Pan, Gui Zhou, Hong Ren
Date: 2022-08-23 00:00:00
Tasks: Diversity

In this paper, we propose a novel two-stage based uplink channel estimation strategy with reduced pilot overhead and error propagation for a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. Specifically, in Stage I, with the carefully designed RIS phase shift matrix and introduced matching matrices, all users jointly estimate the correlation factors between different paths of the common RIS-base station (BS) channel, which achieves significant multi-user diversity gain. Then, the inherent scaling ambiguity and angle ambiguity of the mmWave cascaded channel are utilized to construct an ambiguous common RIS-BS channel composed of the estimated correlation factors. In Stage II, with the constructed ambiguous common RIS-BS channel, each user uses reduced pilots to estimate their specific user-RIS channel independently so as to obtain the entire cascaded channel. The theoretical number of pilots required for the proposed method is analyzed and the simulation results are presented to validate the effectiveness of this strategy.
Robust control problems of BSDEs coupled with value functions
Authors: Zhou Yang, Jing Zhang, Chao Zhou
Date: 2022-08-23 00:00:00

A robust control problem is considered in this paper, where the controlled stochastic differential equations (SDEs) include ambiguity parameters and their coefficients satisfy non-Lipschitz continuous and non-linear growth conditions, the objective function is expressed as a backward stochastic differential equation (BSDE) with the generator depending on the value function. We establish the existence and uniqueness of the value function in a proper space and provide a verification theorem. Moreover, we apply the results to solve two typical optimal investment problems in the market with ambiguity, one of which is with Heston stochastic volatility model. In particular, we establish some sharp estimations for Heston model with ambiguity parameters.
A mathematical model integrates diverging PXY and MP interactions in cambium development
Authors: K. S. Bagdassarian, J. P. Etchells, N. S. Savage
Date: 2022-08-23 00:00:00

The cambium is a meristematic tissue in plant stems. Here, cell divisions occur that are required for radial growth of plant stems. Daughters of cell divisions within the cambium differentiate into woody xylem cells towards the inside of the stem, or phloem towards the outside. As such, a pattern of xylem-cambium-phloem is present along the radial axis of the stem. A ligand-receptor pair, TDIF-PXY promotes cell division in the cambium, as do the phytohormones, cytokinin and auxin. An auxin response factor, MP, has been proposed to initiate cambial cell divisions by promoting PXY expression, however, MP has also been reported to repress cambial cell divisions later in development where TDIF-PXY complexes are also reported to suppress MP activity. Here, we used a mathematical modelling approach to investigate how MP cell division-promoting activity and cell division-repressing activity might be integrated into the same network as a negative feedback loop. In our model, this feedback loop improved the ability of the cambium to pattern correctly and was found to be required for normal patterning when MP was stable. The implications of this model in early and late cambium development are discussed.
Therapeutic algebra of immunomodulatory drug responses at single-cell resolution
Authors: Jialong Jiang, Sisi Chen, Tiffany Tsou, Christopher S. McGinnis, Tahmineh Khazaei, Qin Zhu, Jong H. Park, Paul Rivaud, Inna-Marie Strazhnik, Eric D. Chow, David A. Sivak, Zev J. Gartner, Matt Thomson
Date: 2022-08-23 00:00:00

Therapeutic modulation of immune states is central to the treatment of human disease. However, how drugs and drug combinations impact the diverse cell types in the human immune system remains poorly understood at the transcriptome scale. Here, we apply single-cell mRNA-seq to profile the response of human immune cells to 502 immunomodulatory drugs alone and in combination. We develop a unified mathematical model that quantitatively describes the transcriptome scale response of myeloid and lymphoid cell types to individual drugs and drug combinations through a single inferred regulatory network. The mathematical model reveals how drug combinations generate novel, macrophage and T-cell states by recruiting combinations of gene expression programs through both additive and non-additive drug interactions. A simplified drug response algebra allows us to predict the continuous modulation of immune cell populations between activated, resting and hyper-inhibited states through combinatorial drug dose titrations. Our results suggest that transcriptome-scale mathematical models could enable the design of therapeutic strategies for programming the human immune system using combinations of therapeutics.
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals
Authors: Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik
Date: 2022-08-23 00:00:00
Tasks: regression

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals. Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.
Information-theoretical analysis of the neural code for decoupled face representation
Authors: Miguel Ibáñez-Berganza, Carlo Lucibello, Luca Mariani, Giovanni Pezzulo
Date: 2022-08-22 00:00:00

Processing faces accurately and efficiently is a key capability of humans and other animals that engage in sophisticated social tasks. Recent studies reported a decoupled coding for faces in the primate inferotemporal cortex, with two separate neural populations coding for the geometric position of (texture-free) facial landmarks and for the image texture at fixed landmark positions, respectively. Here, we formally assess the efficiency of this decoupled coding by appealing to the information-theoretic notion of description length, which quantifies the amount of information that is saved when encoding novel facial images, with a given precision. We show that despite decoupled coding describes the facial images in terms of two sets of principal components (of landmark shape and image texture), it is more efficient (i.e., yields more information compression) than the encoding in terms of the image principal components only, which corresponds to the widely used eigenface method. The advantage of decoupled coding over eigenface coding increases with image resolution and is especially prominent when coding variants of training set images that only differ in facial expressions. Moreover, we demonstrate that decoupled coding entails better performance in three different tasks: the representation of facial images, the (daydream) sampling of novel facial images, and the recognition of facial identities and gender. In summary, our study provides a first principle perspective on the efficiency and accuracy of the decoupled coding of facial stimuli reported in the primate inferotemporal cortex.
An end-to-end neural network framework for SOH estimation and RUL prediction of lithium battery
Authors: Penghua Li a, Zijian Zhang a, Radu Grosu b, Zhongwei Deng c, ∗, Jie Hou a, Yujun Rong d, Rui Wu e
Date: 2022-05-01 00:00:00
Proceeding: Renewable and Sustainable Energy Reviews 2022 5
Tasks: Bayesian Optimization

This study proposes an end-to-end prognostic framework for state-of-health (SOH) estimation and remaining
useful life (RUL) prediction. In such a framework, a hybrid neural network (NN), i.e., the concatenation of onedimensional convolutional NN and active-state-tracking long–short-term memory NN, is designed to capture
the hierarchical features between several variables affecting battery degeneration, as well as the temporal
dependencies embedded in those features. The prior distribution over hyperparameters, specified to the popular
NNs applied in SOH or RUL tasks, is built through the Kolmogorov–Smirnov test. Such prior distribution is
regarded as a surrogate to investigate the degeneration data’s impact on modeling such NNs. Based on such
a surrogate, a Bayesian optimization algorithm is proposed to build SOH and RUL models, selecting the most
promising configuration automatically in the sequential evolution progress of hyperparameters. Compared with
the existing NNs, the experiments indicate that our method hits a lower average RMSE 0.0072 and global
average RMSE 0.0269 for SOH and RUL tasks. Code and models are available at https://github.com/LipenghuaCQ/CNN-ASTLSTM.
Secondary Protein Structure Prediction Using Neural Networks
Authors: Sidharth Malhotra, Robin Walters
Date: 2022-08-24 00:00:00
Tasks: Prediction, Protein Structure Prediction

In this paper we experiment with using neural network structures to predict a protein's secondary structure ({\alpha} helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested on mouse and human datasets. Secondly, we test the impact of varying the length of protein sequence we input into the model. Thirdly, we compare custom error functions designed to focus on the center of the input window. At the end of paper we propose a alternative, recurrent neural network model which can be applied to the problem.
A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spaces
Authors: Kyler Larsen, Arghya Pal, Yogesh Rathi
Date: 2022-08-24 00:00:00
Tasks: MRI Reconstruction

Magnetic Resonance Imaging (MRI) scans are time consuming and precarious, since the patients remain still in a confined space for extended periods of time. To reduce scanning time, some experts have experimented with undersampled k spaces, trying to use deep learning to predict the fully sampled result. These studies report that as many as 20 to 30 minutes could be saved off a scan that takes an hour or more. However, none of these studies have explored the possibility of using masked image modeling (MIM) to predict the missing parts of MRI k spaces. This study makes use of 11161 reconstructed MRI and k spaces of knee MRI images from Facebook's fastmri dataset. This tests a modified version of an existing model using baseline shifted window (Swin) and vision transformer architectures that makes use of MIM on undersampled k spaces to predict the full k space and consequently the full MRI image. Modifications were made using pytorch and numpy libraries, and were published to a github repository. After the model reconstructed the k space images, the basic Fourier transform was applied to determine the actual MRI image. Once the model reached a steady state, experimentation with hyperparameters helped to achieve pinpoint accuracy for the reconstructed images. The model was evaluated through L1 loss, gradient normalization, and structural similarity values. The model produced reconstructed images with L1 loss values averaging to <0.01 and gradient normalization values <0.1 after training finished. The reconstructed k spaces yielded structural similarity values of over 99% for both training and validation with the fully sampled k spaces, while validation loss continually decreased under 0.01. These data strongly support the idea that the algorithm works for MRI reconstruction, as they indicate the model's reconstructed image aligns extremely well with the original, fully sampled k space.
Efficient Heterogeneous Video Segmentation at the Edge
Authors: Jamie Menjay Lin, Siargey Pisarchyk, Juhyun Lee, David Tian, Tingbo Hou, Karthik Raveendran, Raman Sarokin, George Sung, Trent Tolley, Matthias Grundmann
Date: 2022-08-24 00:00:00
Tasks: CPU, GPU, Video Segmentation, Video Semantic Segmentation

We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across multiple dimensions of specifications for the neural architectures and operations on top of already light-weight backbones, targeting commercially available edge inference engines. We further analyze and optimize the heterogeneous data flows in our systems across the CPU, the GPU and the NPU. Our approach has empirically factored well into our real-time AR system, enabling remarkably higher accuracy with quadrupled effective resolutions, yet at much shorter end-to-end latency, much higher frame rate, and even lower power consumption on edge platforms.
Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion
Authors: Yugeng Huang, Haitao Liu, Tian Huang
Date: 2022-08-24 00:00:00

As an efficient algorithm to solve the multi-view registration problem,the motion averaging (MA) algorithm has been extensively studied and many MA-based algorithms have been introduced. They aim at recovering global motions from relative motions and exploiting information redundancy to average accumulative errors. However, one property of these methods is that they use Guass-Newton method to solve a least squares problem for the increment of global motions, which may lead to low efficiency and poor robustness to outliers. In this paper, we propose a novel motion averaging framework for the multi-view registration with Laplacian kernel-based maximum correntropy criterion (LMCC). Utilizing the Lie algebra motion framework and the correntropy measure, we propose a new cost function that takes all constraints supplied by relative motions into account. Obtaining the increment used to correct the global motions, can further be formulated as an optimization problem aimed at maximizing the cost function. By virtue of the quadratic technique, the optimization problem can be solved by dividing into two subproblems, i.e., computing the weight for each relative motion according to the current residuals and solving a second-order cone program problem (SOCP) for the increment in the next iteration. We also provide a novel strategy for determining the kernel width which ensures that our method can efficiently exploit information redundancy supplied by relative motions in the presence of many outliers. Finally, we compare the proposed method with other MA-based multi-view registration methods to verify its performance. Experimental tests on synthetic and real data demonstrate that our method achieves superior performance in terms of efficiency, accuracy and robustness.
Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction
Authors: Xinyu Zhu, Yongliang Shen, Weiming Lu
Date: 2022-08-24 00:00:00

Concomitant administration of drugs can cause drug-drug interactions (DDIs). Some drug combinations are beneficial, but other ones may cause negative effects which are previously unrecorded. Previous works on DDI prediction usually rely on hand-engineered domain knowledge, which is laborious to obtain. In this work, we propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs. We adopt a Transformer-like substructure extraction module to acquire a fixed number of representative vectors that are associated with various substructure patterns of the drug molecule. Then, interaction strength between the two drugs' substructures will be captured by a similarity-based interaction module. We also perform a substructure dropping augmentation before graph encoding to alleviate overfitting. Experimental results from a real-world dataset reveal that our proposed model achieves the state-of-the-art performance. We also show that the predictions of our model are highly interpretable through a case study.
Preprocessing Source Code Comments for Linguistic Models
Authors: Sergey Matskevich, Colin S. Gordon
Date: 2022-08-23 00:00:00

Comments are an important part of the source code and are a primary source of documentation. This has driven interest in using large bodies of comments to train or evaluate tools that consume or produce them -- such as generating oracles or even code from comments, or automatically generating code summaries. Most of this work makes strong assumptions about the structure and quality of comments, such as assuming they consist mostly of proper English sentences. However, we know little about the actual quality of existing comments for these use cases. Comments often contain unique structures and elements that are not seen in other types of text, and filtering or extracting information from them requires some extra care. This paper explores the contents and quality of Python comments drawn from 840 most popular open source projects from GitHub and 8422 projects from SriLab dataset, and the impact of na\"ive vs. in-depth filtering can have on the use of existing comments for training and evaluation of systems that generate comments.
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network
Authors: Liang Du, Xiaoqing Ye, Xiao Tan, Edward Johns, Bo Chen, Errui Ding, xiangyang xue, Jianfeng Feng
Date: 2022-08-24 00:00:00
Tasks: 3D Object Detection, Domain Adaptation, object-detection, Object Detection

The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detecting occluded and distant objects: the point cloud appearance varies greatly due to occlusion, and has inherent variance in point densities along the distance to sensors. Therefore, designing feature representations robust to such point clouds is critical. Inspired by human associative recognition, we propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We bridge the gap between the perceptual domain, where features are derived from real scenes with sub-optimal representations, and the conceptual domain, where features are extracted from augmented scenes that consist of non-occlusion objects with rich detailed information. A feasible method is investigated to construct conceptual scenes without external datasets. We further introduce an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions. The network's feature enhancement ability is exploited without introducing extra cost during inference, which is plug-and-play in various 3D detection frameworks. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed. Experiments on nuScenes and Waymo datasets also validate the versatility of our method.