Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Scaling Law Analysis and Phase Shift Optimization of RIS-Aided Massive MIMO Systems With Statistical CSI

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.
A Transparent Distributed Ledger-based Certificate Revocation Scheme for VANETs

Among the available communication systems, vehicular networks are emerging as one of the most promising and yet most challenging instantiations of mobile ad-hoc network technologies. The deployment of such networks in large scale requires the enforcement of stringent security mechanisms that need to abide by the technical, societal, legal, and economical requirements of Intelligent Transportation Systems. Authentication is an effective process for validating user identity in vehicular netoworks. However, it cannot guarantee the network security by itself. Available industrial standards do not consider methods to promptly revoke misbehaving vehicles. The only available protection consists on the \textit{revocation by expiry}, which tolerates the misbehaving vehicle to remain trusted in the system for a long time (e.g. 3 months with certificate pre-loading according to EU security policy). This poses a huge yet dangerous limitation to the security of the vehicular ecosystem. In this work we propose a Distributed Ledger-based Certificate Revocation Scheme for Vehicular Ad-hoc Networks (VANETs) that harnesses the advantages of the underlying Distributed Ledger Technology (DLT) to implement a privacy-aware revocation process that is fully transparent to all participating entities and meets the critical message processing times defined by EU and US standards. An experimental validation and analysis demonstrates the effectiveness and efficiency of the proposed scheme, where the DLT streamlines the revocation operation overhead and delivers an economic solution against cyber-attacks in vehicular systems.
Extend, Push, Pull: Smartphone Mediated Interaction in Spatial Augmented Reality via Intuitive Mode Switching

We investigate how smartphones can be used to mediate the manipulation of smartphone-based content in spatial augmented reality (SAR). A major challenge here is in seamlessly transitioning a phone between its use as a smartphone to its use as a controller for SAR. Most users are familiar with hand extension as a way for using a remote control for SAR. We therefore propose to use hand extension as an intuitive mode switching mechanism for switching back and forth between the mobile interaction mode and the spatial interaction mode. Based on this intuitive mode switch, our technique enables the user to push smartphone content to an external SAR environment, interact with the external content, rotate-scale-translate it, and pull the content back into the smartphone, all the while ensuring no conflict between mobile interaction and spatial interaction. To ensure feasibility of hand extension as mode switch, we evaluate the classification of extended and retracted states of the smartphone based on the phone’s relative 3D position with respect to the user’s head while varying user postures, surface distances, and target locations. Our results show that a random forest classifier can classify the extended and retracted states with a 96% accuracy on average.
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

Conditional text generation often requires lexical constraints, i.e., which words should or shouldn’t be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models – supervised or not – to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.
Dynamic Contextualized Word Embeddings

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.
Automatic facial recognition using VGG16 based transfer learning model

Abstract Detecting and analyzing emotions from human facial movement is a problem defined and developed over many years for the benefits it brings. Facial expression is a crux of the human-computer interaction (HCI) research area. Researchers are exploring its application in security, medical science, and to know the behavior of a person or community. In this paper, we have proposed a deep learning-based framework using transfer learning for facial expression. This approach uses the existing VGG16 model for a modified trained model and concatenates additional layers on it. VGG16 model is already trained on ImageNet, which has 1000 classes. After this, the model has been verified on CK+, JAFFE benchmark datasets. Extended Cohn-Kanade (CK+) and Japanese Female Facial Expression (JAFFE) are popular Facial Expression Dataset. The proposed model has shown 94.8% accuracy on CK+ and 93.7% on the JAFFE dataset and found superior to existing techniques. We have implemented proposed technique on Google Colab-GPU that has helped us to process these data.
Self-Supervised Training For Low-Dose Ct Reconstruction

Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low-dose CT reconstruction has been offered with the availability of compressed sensing based reconstruction methods. Recently, data-driven methods got attention with the rise of deep learning, the availability of high computational power, and big datasets. Deep learning based methods have also been used in low-dose CT reconstruction problem in different manners. Usually, the success of these methods depends on labeled data. However, recent studies showed that training can be achieved successfully with noisy datasets. In this study, we defined a training scheme to use low-dose sinograms as their own training targets. We applied the self-supervision principle in the projection domain where the noise is element-wise independent which is a requirement for self-supervised training methods. Using the self-supervised training, the filtering part of the FBP method and the parameters of a denoiser neural network are optimized. We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods qualitatively and quantitatively in the reconstruction of analytic CT phantoms and real-world CT images in low-dose CT reconstruction task.
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering

Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to high data collection costs, it is not realistic to obtain annotated data for each language one desires to support. We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. We show that the proposed method allows to significantly outperform the baselines trained on English data only. We report a new state-of-the-art on four datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).
Forethought and Hindsight in Credit Assignment

We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, prior works focus on the models trained over the Euclidean space, e.g., images and texts, while how to extract a GNN model that contains a graph structure and node features is yet to be explored. In this paper, for the first time, we comprehensively investigate and develop model extraction attacks against GNN models. We first systematically formalise the threat modelling in the context of GNN model extraction and classify the adversarial threats into seven categories by considering different background knowledge of the attacker, e.g., attributes and/or neighbour connections of the nodes obtained by the attacker. Then we present detailed methods which utilise the accessible knowledge in each threat to implement the attacks. By evaluating over three real-world datasets, our attacks are shown to extract duplicated models effectively, i.e., 84% - 89% of the inputs in the target domain have the same output predictions as the victim model.
Stable ResNet

Deep ResNet architectures have achieved state of the art performance on many tasks. While they solve the problem of gradient vanishing, they might suffer from gradient exploding as the depth becomes large (Yang et al. 2017). Moreover, recent results have shown that ResNet might lose expressivity as the depth goes to infinity (Yang et al. 2017, Hayou et al. 2019). To resolve these issues, we introduce a new class of ResNet architectures, called Stable ResNet, that have the property of stabilizing the gradient while ensuring expressivity in the infinite depth limit.
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians

Hyperparameter optimization of neural networks can be elegantly formulated as a bilevel optimization problem. While research on bilevel optimization of neural networks has been dominated by implicit differentiation and unrolling, hypernetworks such as Self-Tuning Networks (STNs) have recently gained traction due to their ability to amortize the optimization of the inner objective. In this paper, we diagnose several subtle pathologies in the training of STNs. Based on these observations, we propose the $\Delta$-STN, an improved hypernetwork architecture which stabilizes training and optimizes hyperparameters much more efficiently than STNs. The key idea is to focus on accurately approximating the best-response Jacobian rather than the full best-response function; we achieve this by reparameterizing the hypernetwork and linearizing the network around the current parameters. We demonstrate empirically that our $\Delta$-STN can tune regularization hyperparameters (e.g. weight decay, dropout, number of cutout holes) with higher accuracy, faster convergence, and improved stability compared to existing approaches.
A tale of two actions a variational principle for two-dimensional causal sets

In this paper we will explore two different proposals for the action for causal sets: the Benincasa–Dowker action [], and a modified version of the chain action []. We propose a variational principle for two-dimensional causal sets and use it for both actions to determine which causal sets at least on average satisfy a discrete version of the Einstein equation. Specifically, we test this method on causal sets embedded in 2d Minkowski, de Sitter, and anti-de Sitter spacetimes and compare the results with those obtained for the most prominent nonmanifoldlike causal sets, the Kleitman–Rothschild causal sets [].
Investigation of the Ribosome Independent mRNA Localization to the Endoplasmic Reticulum

Localization of mRNA to various subcellular compartments is a widespread phenomenon in both prokaryotes and eukaryotes. One group of mRNAs that exhibit this organelle specific localization are mRNAs encoding secreted, organellar and membrane-bound proteins. These mRNAs are localized on the surface of the plasma membrane in prokaryotes and on the endoplasmic reticulum (ER) in eukaryotes. Previously, these mRNAs were thought to target and later be maintained on the surface of the ER through information that is present in the encoded polypeptide. However, here I provide direct visual evidence that a subset of these mRNAs can be targeted and also later maintained on the ER independently of translation. I also identified an mRNA receptor on the ER, p180, which anchors mRNAs on the ER via a highly positively charged lysine-rich region. Subsequently I identified a region in the placental alkaline phosphatase (ALPP) mRNA, the region that encodes the transmembrane domain (TMD), that is required and sufficient to anchor mRNAs to the ER in a p180-dependent manner. Finally, I demonstrate that certain mRNAs encoding tailed anchored (TA) proteins are also anchored to the ER in a translation-independent manner. Overall my work provides the first mechanistic insight into RNA-mediated targeting of mRNAs to the ER, suggesting a more complex role for the ER in the post-transcriptional control of genes encoding secretory proteins.
The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = −1.29, P < 0.001) and ADC (SMD = −1.21, P < 0.001) than benign tumors. The MK (SMD = −1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.
The role of hypoxia-induced modulation of alveolar epithelial Na+- transport in hypoxemia at high altitude

Reabsorption of excess alveolar fluid is driven by vectorial Na+-transport across alveolar epithelium, which protects from alveolar flooding and facilitates gas exchange. Hypoxia inhibits Na+-reabsorption in cultured cells and in-vivo by decreasing activity of epithelial Na+-channels (ENaC), which impairs alveolar fluid clearance. Inhibition also occurs during in-vivo hypoxia in humans and laboratory animals. Signaling mechanisms that inhibit alveolar reabsorption are poorly understood. Because cellular adaptation to hypoxia is regulated by hypoxia-inducible transcription factors (HIF), we tested whether HIFs are involved in decreasing Na+-transport in hypoxic alveolar epithelium. Expression of HIFs was suppressed in cultured rat primary alveolar epithelial cells (AEC) with shRNAs. Hypoxia (1.5% O2, 24 h) decreased amiloride-sensitive transepithelial Na+-transport, decreased the mRNA expression of α-, β-, and γ-ENaC subunits, and reduced the amount of αβγ-ENaC subunits in the apical plasma membrane. Silencing HIF-2α partially prevented impaired fluid reabsorption in hypoxic rats and prevented the hypoxia-induced decrease in α- but not the βγ-subunits of ENaC protein expression resulting in a less active form of ENaC in hypoxic AEC. Inhibition of alveolar reabsorption also caused pulmonary vasoconstriction in ventilated rats. These results indicate that a HIF-2α-dependent decrease in Na+-transport in hypoxic alveolar epithelium decreases alveolar reabsorption. Because susceptibles to high-altitude pulmonary edema (HAPE) have decreased Na+-transport even in normoxia, inhibition of alveolar reabsorption by hypoxia at high altitude might further impair alveolar gas exchange. Thus, aggravated hypoxemia might further enhance hypoxic pulmonary vasoconstriction and might subsequently cause HAPE.
Criticality-enhanced quantum sensing in ferromagnetic Bose-Einstein condensates: Role of readout measurement and detection noise

We theoretically investigate estimation of the control parameter in a ferromagnetic Bose-Einstein condensate near second order quantum phase transitions. We quantify sensitivity by quantum and classical Fisher information and using the error-propagation formula. For these different metrics, we find the same, beyond-standard-quantum-limit (SQL) scaling with atom number near critical points, and SQL scaling away from critical points. We find that both depletion of the $m_f=0$ Zeeman sub-level and transverse magnetization provide signals of sufficient quality to saturate the sensitivity scaling. To explore the effect of experimental imperfections, we study the scaling around criticality at nonzero temperature and with nonzero detection noise. Our results suggest the feasibility of sub-SQL sensing in ferromagnetic condensates with current experimental capabilities.
Scribble-Based Weakly Supervised Deep Learning for Road Surface Extraction From Remote Sensing Images

Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this article, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a road label propagation algorithm, which considers both the buffer-based properties of road networks and the color and spatial information of super-pixels, to produce a proposal mask with categories road, nonroad, and unknown. The proposal mask, along with the auxiliary boundary prior information detected from images, is utilized to train a dual-branch encoder–decoder network which we designed for precise road surface segmentation. We perform experiments on three diverse road data sets that are comprised of high-resolution remote sensing satellite and aerial images across the world. The results demonstrate that ScRoadExtractor exceeds the classic scribble-supervised segmentation method by 20% for the intersection over union (IoU) indicator and outperforms the state-of-the-art scribble-based weakly supervised methods at least 4%.
Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms

Generalization error bounds are critical to understanding the performance of machine learning models. In this work, we propose a new information-theoretic based generalization error upper bound applicable to supervised learning scenarios. We show that our general bound can specialize in various previous bounds. We also show that our general bound can be specialized under some conditions to a new bound involving the Jensen-Shannon information between a random variable modelling the set of training samples and another random variable modelling the hypothesis. We also prove that our bound can be tighter than mutual information-based bounds under some conditions.