pwc-archive/papers-with-abstracts huggingface dataset:
Equitable 6G Access Service via Cloud-Enabled HAPS for Optimizing Hybrid Air-Ground Networks
Authors: Rawan Alghamdi, Hayssam Dahrouj, Tareq Al-Naffouri, Mohamed-Slim Alouini
Date: 2022-12-05 00:00:00
Tasks: Scheduling

The evolvement of wireless communication services concurs with significant growth in data traffic, thereby inflicting stringent requirements on terrestrial networks. This work invigorates a novel connectivity solution that integrates aerial and terrestrial communications with a cloud-enabled high-altitude platform station (HAPS) to promote an equitable connectivity landscape. Consider a cloud-enabled HAPS connected to both terrestrial base-stations and hot-air balloons via a data-sharing fronthauling strategy. The paper then assumes that both the terrestrial base-stations and the hot-air balloons are grouped into disjoint clusters to serve the aerial and terrestrial users in a coordinated fashion. The work then focuses on finding the user-to-transmitter scheduling and the associated beamforming policies in the downlink direction of cloud-enabled HAPS systems by maximizing two different objectives, namely, the sum-rate and sum-of-log of the long-term average rate, both subject to limited transmit power and finite fronthaul capacity. The paper proposes solving the two non-convex discrete and continuous optimization problems using numerical iterative optimization algorithms. The proposed algorithms rely on well-chosen convexification and approximation steps, namely, fractional programming and sparse beamforming via re-weighted $\ell_0$-norm approximation. The numerical results outline the yielded gain illustrated through equitable access service in crowded and unserved areas, and showcase the numerical benefits stemming from the proposed cloud-enabled HAPS coordination of hot-air balloons and terrestrial base-stations for democratizing connectivity and empowering the digital inclusion framework.
Modulation and Signal Processing for LEO-LEO Optical Inter-satellite Links
Authors: I. P. Vieira, T. C. Pita, D. A. A. Mello
Date: 2022-12-05 00:00:00

We investigate key aspects of coherent optical communications on inter-satellite links (ISLs) for the next-generation ultra-dense low-Earth orbit (LEO) constellations. Initially, the suitability of QPSK, 8-QAM, and 16-QAM modulation formats with different symbol rates (28 GBaud, 60 GBaud, and 120 GBaud) and channel coding schemes (oFEC and staircase codes) for intra- and interorbital connections is evaluated. We provide SNR margins for all investigated sets and determine unfeasible operating points. We show that sets with higher-order modulation formats combined with high symbol rates can prove unfeasible, even for first-neighbor connections. Furthermore, the presence or absence of optical pre-amplification as well as the choice for a more robust channel coding technique, such as the oFEC, can be decisive in certain LEO-LEO links. Next, we characterize the Doppler shift (DS) and its time derivative for first-neighbor interorbital connections in two different topologies and for general connections established between any pairs of satellites. Our results reveal that while the maximum Doppler-generated frequency shift amplitude can be considerably higher than those typically found in fiber-optic communications, the time derivative values are significantly lower. Finally, we address all-digital DS compensation in extreme cases of frequency offset amplitude and derivative where the typical Mth-power algorithm is not sufficient. To this end, we propose a filtered version of an existing two-stage method combining spectral shifts with the Mth-power method. The simulation results indicate that this approach provides an appropriate solution for all examined cases.
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks
Authors: Yinan Zou, Yong Zhou, Xu Chen, Yonina C. Eldar
Date: 2022-12-04 00:00:00
Tasks: Action Detection, Activity Detection, Compressive Sensing

Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a critical issue. Although existing compressive sensing algorithms can be applied for JADCE, they usually fail to simultaneously harvest the following properties: effective sparsity inducing, fast convergence, robust to different pilot sequences, and adaptive to time-varying networks. To this end, we propose an unfolding framework for JADCE based on the proximal gradient method. Specifically, we formulate the JADCE problem as a group-row-sparse matrix recovery problem and leverage a minimax concave penalty rather than the widely-used $\ell_1$-norm to induce sparsity. We then develop a proximal gradient-based unfolding neural network that parameterizes the algorithmic iterations. To improve convergence rate, we incorporate momentum into the unfolding neural network, and prove the accelerated convergence theoretically. Based on the convergence analysis, we further develop an adaptive-tuning algorithm, which adjusts its parameters to different signal-to-noise ratio settings. Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.
Intelligent Reflecting Surface Assisted Secret Key Generation Under Spatially Correlated Channels in Quasi-Static Environments
Authors: Vahid Shahiri, Hamid Behroozi, Ali Kuhestani
Date: 2022-12-03 00:00:00

Physical layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by enabling them to change their secret keys significantly faster and more efficient. However, due to the reliance of PLKG techniques on channel medium, reaching a high secret key rate is challenging in static environments. Recently, exploiting intelligent reflecting surface (IRS) as a means to induce randomness in static wireless channels has received significant research interest. However, the impact of spatial correlation between the IRS elements is rarely studied. To be specific, for the first time, in this contribution, we take into account a spatially correlated IRS which intends to enhance the secret key generation (SKG) rate in a static medium. Closed form analytical expressions for SKG rate are derived for the two cases of random phase shift and equal random phase shift for all the IRS elements. We also analyze the temporal correlation between the channel samples to ensure the randomness of the generated secret key sequence. We further formulate an optimization problem in which we determine the optimal portion of time within a coherence interval dedicated for the direct and indirect channel estimation. We show the accuracy and the fast convergence of our proposed sequential convex programming (SCP) based algorithm and discuss the various parameters affecting spatially correlated IRS assisted PLKG.
High-resolution and reliable automatic target recognition based on photonic ISAR imaging system with explainable deep learning
Authors: Xiuting Zou, Anyi Deng, Yiheng Hu, Shiyu Hua, Linbo Zhang, Shaofu Xu, Weiwen Zou
Date: 2022-12-03 00:00:00

Automatic target recognition (ATR) based on inverse synthetic aperture radar (ISAR) images, which is extensively utilized to surveil environment in military and civil fields, must be high-precision and reliable. Photonic technologies' advantage of broad bandwidth enables ISAR systems to realize high-resolution imaging, which is in favor of achieving high-performance ATR. Deep learning (DL) algorithms have achieved excellent recognition accuracies. However, the lack of interpretability of DL algorithms causes the head-scratching problem of credibility. In this paper, we exploit the inner relationship between a photonic ISAR imaging system and behaviors of a convolutional neural network (CNN) to deeply comprehend the intelligent recognition. Specifically, we manipulate imaging physical process and analyze network outputs, the relevance between the ISAR image and network output, and the visualization of features in the network output layer. Consequently, the broader imaging bandwidths and appropriate imaging angles lead to more detailed structural and contour features and the bigger discrepancy among ISAR images of different targets, which contributes to the CNN recognizing and distinguishing objects according to physical laws. Then, based on the photonic ISAR imaging system and the explainable CNN, we accomplish a high-accuracy and reliable ATR. To the best of our knowledge, there is no precedent of explaining the DL algorithms by exploring the influence of the physical process of data generation on network behaviors. It is anticipated that this work can not only inspire the accomplishment of a high-performance ATR but also bring new insights to explore network behaviors and thus achieve better intelligent abilities.
Weak error estimates for rough volatility models
Authors: Peter K. Friz, William Salkeld, Thomas Wagenhofer
Date: 2022-12-03 00:00:00

We consider a class of stochastic processes with rough stochastic volatility, examples of which include the rough Bergomi and rough Stein-Stein model, that have gained considerable importance in quantitative finance. A basic question for such (non-Markovian) models concerns efficient numerical schemes. While strong rates are well understood (order $H$), we tackle here the intricate question of weak rates. Our main result asserts that the weak rate, for a reasonably large class of test function, is essentially of order $\min \{ 3H+\tfrac12, 1 \}$ where $H \in (0,1/2]$ is the Hurst parameter of the fractional Brownian motion that underlies the rough volatility process. Interestingly, the phase transition at $H=1/6$ is related to the correlation between the two driving factors, and thus gives additional meaning to a quantity already of central importance in stochastic volatility modelling.Our results are complemented by a lower bound which show that the obtained weak rate is indeed optimal.
TIDAL: Topology-Inferred Drug Addiction Learning
Authors: Zhu Zailiang, Dou Bozheng, Cao Yukang, Jiang Jian, Zhu Yueying, Chen Dong, Feng Hongsong, Liu Jie, Zhang Bengong, Zhou Tianshou, Wei Guowei
Date: 2022-12-03 00:00:00

Drug addiction or drug overdose is a global public health crisis, and the design of anti-addiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating topological Laplacian, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). The topological Laplacian is a novel algebraic topology tool that embeds molecular topological invariants and algebraic invariants into its harmonic spectra and non-harmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT datasets, showing that TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and identify their repurposing potentials, revealing drugmediated linear and bilinear target correlations. Finally, TIDAL is applied to shed light on relative efficacy, repurposing potential, and potential side effects of 12 existing anti-addiction medications. Our results suggest that TIDAL provides a new computational strategy for pressingly-needed anti-substance addiction drug development.
Detection of brain activations induced by naturalistic stimuli in a pseudo model-driven way
Authors: Jiangcong Liu, Hao Ma, Yun Guan, Fan Wu, Le Xu, Yang Zhang, Lixia Tian
Date: 2022-12-03 00:00:00

Naturalistic fMRI has been suggested to be a powerful alternative for investigations of human brain function. Stimulus-induced activation has been playing an essential role in fMRI-based brain function analyses. Due to the complexity of the stimuli, however, detection of activations induced by naturalistic stimuli (AINSs) has been a tricky problem, as AINS cannot be detected simply in a model-driven way. In this study, we proposed a method to detect AINS in a pseudo model-driven way. Inspired by the strategy of utilizing the commonalities among the brains exposed to the same stimuli for inter-subject correlation analysis, we established response models for one subject by averaging the fMRI signals across several other subjects, and then detected AINSs of the subject using general linear model. We evaluated the effectiveness of AINS with both statistical and predictive analyses on individual differences in sex and intelligence quotient (IQ), based on the four movie fMRI runs included in the Human Connectome Project dataset. The results indicate that AINS is not only sensitive to sex- and IQ-related differences, but also specific enough to decode individuals' sex and IQ. Specifically, activations in brain regions associated with visual-spatial processing were observed to be consistently stronger in the males, and individuals with higher IQ exhibited consistently stronger activations in regions within the visual and the default mode networks. Predictions of individuals' sex and IQ were significantly better than those based on random labels (P < 0.005). Taken together, AINS advanced in this study can be an effective evaluation of human brain function. The conceptual simplicity and easy application of its detection may make AINS a favorable choice for future brain function analyses and personalized medicine based on naturalistic fMRI.
Prototype matching: children's preference for forming scientific concepts
Authors: Zhong Wang, Yi Zhang, Yi Jiang
Date: 2022-12-03 00:00:00

Inspired by a sample lesson, this paper studies and discusses children's preferences in learning scientific concepts. In a "Dissolution" lesson, one of the students took the demonstration experiment of "carmine dissolves in Water" demonstrated by the teacher as the prototype to judge whether a new phenomenon belongs to dissolution, instead of analyzing and judging the phenomenon by using the dissolution definition. Therefore, we propose a conjecture that "prototype matching" may be a more preferred way for children to learn concepts than thinking through inquiry experiment, analysis, deduction, etc. To this end, we conducted a targeted test on 160 fifth grade students (all of whom had learned this lesson) from a primary school in Beijing, and used goodness of fit test to statistically analyze the results. The results showed that: 1. the Chi square of the general result is 73.865, P<0.001, indicating that children did have obvious prototype preference; 2. We "tampered" some of the prototypes, that is, they looked like the prototypes that the teacher had told students, but they were actually wrong. However, the results showed that children still preferred these so-called "prototypes" (chi square is 21.823, P<0.001). Conclusion: 1. Children have an obvious preference for "prototype matching" in scientific concept learning, which is not only obviously deviated from the current general understanding of science education that emphasizes discovery/inquiry construction, but also points out that there may be a priority relationship among various ways of concept organization (such as definition theory, prototype theory, schema theory, etc.). 2. Children's preference for prototypes seems to be unthinking, and they will not identify the authenticity of prototypes, which is particularly noteworthy in front-line teaching.
Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus
Authors: Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Guangyao Zhou, Miguel Lázaro-Gredilla, Dileep George
Date: 2022-12-03 00:00:00
Tasks: Hippocampus

Fascinating and puzzling phenomena, such as landmark vector cells, splitter cells, and event-specific representations to name a few, are regularly discovered in the hippocampus. Without a unifying principle that can explain these divergent observations, each experiment seemingly discovers a new anomaly or coding type. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves myriad phenomena, and suggests that the place-field mapping methodology where sequential neuron responses are interpreted in spatial and Euclidean terms might itself be a source of anomalies. Our model, called Clone-structured Causal Graph (CSCG), uses a specific higher-order graph scaffolding to learn latent representations by mapping sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs result in the emergence of cognitive maps - mental representations of spatial and conceptual relationships in an environment that are suited for planning, introspection, consolidation, and abstraction. We demonstrate that over a dozen different hippocampal phenomena, ranging from those reported in classic experiments to the most recent ones, are succinctly and mechanistically explained by our model.
Inferring latent neural sources via deep transcoding of simultaneously acquired EEG and fMRI
Authors: Xueqing Liu, Tao Tu, Paul Sajda
Date: 2022-11-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution. Challenging has been developing principled and interpretable approaches for fusing the modalities, specifically approaches enabling inference of latent source spaces representative of neural activity. In this paper, we address this inference problem within the framework of transcoding -- mapping from a specific encoding (modality) to a decoding (the latent source space) and then encoding the latent source space to the other modality. Specifically, we develop a symmetric method consisting of a cyclic convolutional transcoder that transcodes EEG to fMRI and vice versa. Without any prior knowledge of either the hemodynamic response function or lead field matrix, the complete data-driven method exploits the temporal and spatial relationships between the modalities and latent source spaces to learn these mappings. We quantify, for both the simulated and real EEG-fMRI data, how well the modalities can be transcoded from one to another as well as the source spaces that are recovered, all evaluated on unseen data. In addition to enabling a new way to symmetrically infer a latent source space, the method can also be seen as low-cost computational neuroimaging -- i.e. generating an 'expensive' fMRI BOLD image from 'low cost' EEG data.
A Network Theory Investigation into the Altered Resting State Functional Connectivity in Attention-Deficit Hyperactivity Disorder
Authors: Sadi Md. Redwan, Md Palash Uddin, Muhammad Imran Sharif, Anwaar Ulhaq
Date: 2022-11-23 00:00:00
Tasks: Functional Connectivity

In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain. fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Beyond task-related activations, the human brain has some intrinsic activity at a task-negative (resting) state that surprisingly consumes a lot of energy to support communication among neurons. Recent neuroimaging research has also seen an increase in modeling and analyzing brain activity in terms of a graph or network. Since graph models facilitate a systems-theoretic explanation of the brain, they have become increasingly relevant with advances in network science and the popularization of complex systems theory. The purpose of this study is to look into the abnormalities in resting brain functions in adults with Attention Deficit Hyperactivity Disorder (ADHD). The primary goal is to investigate resting-state functional connectivity (FC), which can be construed as a significant temporal coincidence in blood-oxygen-level dependent (BOLD) signals between functionally related brain regions in the absence of any stimulus or task. When compared to healthy controls, ADHD patients have lower average connectivity in the Supramarginal Gyrus and Superior Parietal Lobule, but higher connectivity in the Lateral Occipital Cortex and Inferior Temporal Gyrus. We also hypothesize that the network organization of default mode and dorsal attention regions is abnormal in ADHD patients.
Describing the evolution and perturbations to biodiversity using a simple dynamical model
Authors: L. G. Barão, L. M. de Sá, A. Bernardo, J. E. Horvath
Date: 2022-12-03 00:00:00

In this work, we outline a mathematical description of biodiversity evolution throughout the Phanerozoic based on a simple coupled system of two differential equations and on the division of genera in two classes - Short-lived and Long-lived types, as used by Rohde and Muller. We show that while the division in only two classes cannot capture the complexity of biodiversity evolution in short scales, broad trends can be described well. We also compute the systems' Green functions as a way to qualitatively describe the possible effects of intense perturbations of astrophyisical origin on the subsequent biodiversity evolution.
KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description
Authors: Chunyu Ma, Zhihan Zhou, Han Liu, David Koslicki
Date: 2022-11-30 00:00:00
Tasks: Drug Discovery

Background: Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings. Results: In this work, we propose KGML-xDTD: a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a two-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable mechanisms of action (MOAs). We leverage knowledge-and-publication based information to extract biologically meaningful "demonstration paths" as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths. Conclusions: KGML-xDTD is the first model framework that can offer KG-path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce "black-box" concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations, and further accelerate the process of drug discovery for emerging diseases.