Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical Switching in Globally Attractive Chimeras

We report on a new type of chimera state that attracts almost all initial conditions and exhibits power-law switching behavior in networks of coupled oscillators. Such switching chimeras consist of two symmetric configurations, which we refer to as subchimeras, in which one cluster is synchronized and the other is incoherent. Despite each subchimera being linearly stable, switching chimeras are extremely sensitive to noise: arbitrarily small noise triggers and sustains persistent switching between the two symmetric subchimeras. The average switching frequency scales as a power law with the noise intensity, which is in contrast with the exponential scaling observed in typical stochastic transitions. Rigorous numerical analysis reveals that the power-law switching behavior originates from intermingled basins of attraction associated with the two subchimeras, which in turn are induced by chaos and symmetry in the system. The theoretical results are supported by experiments on coupled optoelectronic oscillators, which demonstrate the generality and robustness of switching chimeras.
Fitness Done Right: a Real-time Intelligent Personal Trainer for Exercise Correction

Keeping fit has been increasingly important for people nowadays. However, people may not get expected exercise results without following professional guidance while hiring personal trainers is expensive. In this paper, an effective real-time system called Fitness Done Right (FDR) is proposed for helping people exercise correctly on their own. The system includes detecting human body parts, recognizing exercise pose and detecting errors for test poses as well as giving correction advice. Generally, two branch multi-stage CNN is used for training data sets in order to learn human body parts and associations. Then, considering two poses, which are plank and squat in our model, we design a detection algorithm, combining Euclidean and angle distances, to determine the pose in the image. Finally, key values for key features of the two poses are computed correspondingly in the pose error detection part, which helps give correction advice. We conduct our system in real-time situation with error rate down to $1.2\%$, and the screenshots of experimental results are also presented.
A Game-Theoretic Approach for Enhancing Security and Data Trustworthiness in IoT Applications

Wireless sensor networks (WSNs)-based Internet of Things (IoT) are among the fast booming technologies that drastically contribute to different systems’ management and resilience data accessibility. Designing a robust IoT network imposes some challenges, such as data trustworthiness (DT) and power management. This article presents a repeated game model to enhance clustered WSNs-based IoT security and DT against the selective forwarding (SF) attack. Besides, the model is capable of detecting the hardware (HW) failure of the cluster members (CMs), preserving the network stability, and conserving the power consumption due to packet retransmission. The model relies on the TDMA protocol to facilitate the detection process and to avoid collision between the delivered packets at the cluster head (CH). The proposed model aims to keep packets transmitting, isotropic or nonisotropic transmission, from the CMs to the CH for maximizing the DT and aims to distinguish between the malicious CM and the one suffering from the HW failure. Accordingly, it can manage the consequently lost power due to the malicious attack effect or HW malfunction. The simulation results indicate the proposed mechanism improved performance with TDMA over six different environments against the SF attack that achieves the Pareto-optimal DT as compared to a noncooperative defense mechanism.
Machine learning based model order reduction of inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve due to high degrees of freedom as well as the coupled nature of the conservation laws. While this poses a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced order models. However, such models are known to incur huge off-line costs which can sometimes outweigh their potential benefits. Furthermore, their prediction accuracy is known to be poor under transonic flow conditions. In this work, we propose a machine learning method to construct reduced order models via deep neural networks. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack show that the proposed approach adapts to parameter variation very well. Notably, the proposed framework precludes knowledge of numerical operators utilized in the data generation phase. Furthermore, the proposed approach incurs significantly lower offline costs compared to existing approaches while still producing comparable accuracy.
Randomized benchmarking for qudit Clifford gates

We introduce unitary-gate randomized benchmarking (URB) for qudit gates by extending single- and multi-qubit URB to single- and multi-qudit gates. Specifically, we develop a qudit URB procedure that exploits unitary 2-designs. Furthermore, we show that our URB procedure is not simply extracted from the multi-qubit case by equating qudit URB to URB of the symmetric multi-qubit subspace. Our qudit URB is elucidated by using pseudocode, which facilitates incorporating into benchmarking applications.
Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

Abstract In many countries, real estate appraisal is based on conventional methods that rely on appraisers’ abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.
Property Testing of LP-Type Problems

Given query access to a set of constraints $S$, we wish to quickly check if some objective function $\varphi$ subject to these constraints is at most a given value $k$. We approach this problem using the framework of property testing where our goal is to distinguish the case $\varphi(S) \le k$ from the case that at least an $\epsilon$ fraction of the constraints in $S$ need to be removed for $\varphi(S) \le k$ to hold. We restrict our attention to the case where $(S, \varphi)$ are LP-Type problems which is a rich family of combinatorial optimization problems with an inherent geometric structure. By utilizing a simple sampling procedure which has been used previously to study these problems, we are able to create property testers for any LP-Type problem whose query complexities are independent of the number of constraints. To the best of our knowledge, this is the first work that connects the area of LP-Type problems and property testing in a systematic way. Among our results is a tight upper bound on the query complexity of testing clusterability with one cluster considered by Alon, Dar, Parnas, and Ron (FOCS 2000). We also supply a corresponding tight lower bound for this problem and other LP-Type problems using geometric constructions.
MuonTrap: Preventing Cross-Domain Spectre-Like Attacks by Capturing Speculative State

The disclosure of the Spectre speculative-execution attacks in January 2018 has left a severe vulnerability that systems are still struggling with how to patch. The solutions that currently exist tend to have incomplete coverage, perform badly, or have highly undesirable performance edge cases.MuonTrap allows processors to continue to speculate, avoiding significant reductions in performance, without impacting security. We instead prevent the propagation of any state based on speculative execution, by placing the results of speculative cache accesses into a small, fast L0 filter cache, that is non-inclusive, non-exclusive with the rest of the cache hierarchy. This isolates all parts of the system that can’t be quickly cleared on any change in threat domain. MuonTrap uses these speculative filter caches, which are cleared on context and protection-domain switches, along with a series of extensions to the cache coherence protocol and prefetcher. This renders systems immune to cross-domain information leakage via Spectre and a host of similar attacks based on speculative execution, with low performance impact and few changes to the CPU design.
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning

Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficiently implemented with the rich toolbox developed for gradient methods from the deep learning literature. Most notably, these gradient-based algorithms can be combined with stochastic gradient descent methods to produce efficient online sparse principal component analysis algorithms with provable numerical and statistical performance guarantees. The practical performance and usefulness of the new algorithms are demonstrated in various simulation studies. As an application, we show how the scalability and statistical accuracy of our method enable us to find interesting functional gene groups in high-dimensional RNA sequencing data.
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets

When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the detailed information of the target leading to a defective output, is the most common failure mode. The problem has several potential sources such as the identity of the driver leaking due to the identity mismatch, or dealing with unseen large poses. To overcome such problems, we introduce components that address the mentioned problem: image attention block, target feature alignment, and landmark transformer. Through attending and warping the relevant features, the proposed architecture, called MarioNETte, produces high-quality reenactments of unseen identities in a few-shot setting. In addition, the landmark transformer dramatically alleviates the identity preservation problem by isolating the expression geometry through landmark disentanglement. Comprehensive experiments are performed to verify that the proposed framework can generate highly realistic faces, outperforming all other baselines, even under a significant mismatch of facial characteristics between the target and the driver.
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations

Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for nonEuclidean data on graphs, which often require a large amount of labeled data with high cost. It it thus critical to learn graph feature representations in an unsupervised manner in practice. To this end, we propose a novel unsupervised learning of Graph Transformation Equivariant Representations (GraphTER), aiming to capture intrinsic patterns of graph structure under both global and local transformations. Specifically, we allow to sample different groups of nodes from a graph and then transform them node-wise isotropically or anisotropically. Then, we self-train a representation encoder to capture the graph structures by reconstructing these node-wise transformations from the feature representations of the original and transformed graphs. In experiments, we apply the learned GraphTER to graphs of 3D point cloud data, and results on point cloud segmentation/classification show that GraphTER significantly outperforms state-of-the-art unsupervised approaches and pushes greatly closer towards the upper bound set by the fully supervised counterparts. The code is available at: https://github.com/gyshgx868/graph-ter.
A holistic and probabilistic approach to the ground-based and spaceborne data of HAT-P-19 system

We update the main physical and orbital properties of the transiting hot Saturn planet HAT-P-19 b, based on a global modelling of high-precision transit and occultation light curves, taken with ground-based and space telescopes, archive spectra and radial velocity measurements, brightness values from broad-band photometry, and Gaia parallax. We collected 65 light curves by amateur and professional observers, measured mid-transit times, and analysed their differences from calculated transit timings based on reference ephemeris information, which we update as a result. We have not found any periodicity in the residuals of a linear trend, which we attribute to the accumulation of uncertainties in the reference mid-transit time and the orbital period. We comment on the scenarios describing the formation and migration of this hot-Saturn type exoplanet with a bloated atmosphere yet a small core, although it is orbiting a metal-rich ([Fe/H] = 0.24 dex) host star. Finally, we review the planetary mass–radius, the orbital period–radius, and density, and the stellar metallicity–core mass diagrams, based on the parameters we derive for HAT-P-19 b and those of the other 70 transiting Saturn-mass planets from the NASA Exoplanet Archive.
Two-Way Physical Layer Security Protocol for Gaussian Channels

In this paper we propose a two-way protocol of physical layer security using the method of privacy amplification against eavesdroppers. First we justify our proposed protocol by analyzing the physical layer security provided by the classic wiretap channel model (i.e. one-way protocol). In the Gaussian channels, the classic one-way protocol requires Eve’s channel to be degraded w.r. t. Bob’s channel. However, this channel degradation condition depends on Eve’s location and whether Eve’s receiving antenna is more powerful than Bob’s. To overcome this limitation, we introduce a two-way protocol inspired in IEEE TIT (1993) that eliminates the channel degradation condition. In the proposed two-way protocol, on a first phase, via Gaussian channel, Bob sends randomness to Alice, which is partially leaked to Eve. Then, on a second phase, Alice transmits information to Bob over a public noiseless channel. We derive the secrecy capacity of the two-way protocol when the channel to Eve is also Gaussian. We show that the capacity of the two-way protocol is always positive. We present numerical values of the capacities illustrating the gains obtained by our proposed protocol. We apply our result to simple yet realistic models of satellite communication channels.
The daily costs of chemotherapy-associated adverse events: A cluster-based analysis

6001 Background: Chemotherapy-associated adverse events (C-AEs) including neutropenia (N), anemia (A), thrombocytopenia (T), mucositis (M) and dehydration (D) are common and associated with increased hospital costs. However, C-AEs often occur in clusters, and as a consequence, it makes little clinical sense to discuss the costs associated with a single C-AE in isolation from other C-AEs. Methods: This study estimates the costs of inpatient and outpatient C-AEs based on a new approach to classifying C-AEs - the clinically relevant C-AE cluster. The study population included all cancer patients receiving chemotherapy (N=2,067) during August 1, 2000 and July 31, 2001 at The University of Pennsylvania Health System. For these patients, C-AE clusters were determined using clinical algorithms based on the start and end dates of defined adverse events and dates of chemotherapy administration. Regression models were developed using a loglinear variance adjusted estimation procedure, to better estimate the daily c.
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE chieves the state-of-the-art on link prediction over four temporal KGs.
Volenti non fit injuria: Ransomware and its Victims

With the recent growth in the number of malicious activities on the internet, cybersecurity research has seen a boost in the past few years. However, as certain variants of malware can provide highly lucrative opportunities for bad actors, significant resources are dedicated to innovations and improvements by vast criminal organisations. Among these forms of malware, ransomware has experienced a significant recent rise as it offers the perpetrators great financial incentive. Ransomware variants operate by removing system access from the user by either locking the system or encrypting some or all of the data, and subsequently demanding payment or ransom in exchange for returning system access or providing a decryption key to the victim. Due to the ubiquity of sensitive data in many aspects of modern life, many victims of such attacks, be they an individual home user or operators of a business, are forced to pay the ransom to regain access to their data, which in many cases does not happen as renormalisation of system operations is never guaranteed. As the problem of ransomware does not seem to be subsiding, it is very important to investigate the underlying forces driving and facilitating such attacks in order to create preventative measures. As such, in this paper, we discuss and provide further insight into variants of ransomware and their victims in order to understand how and why they have been targeted and what can be done to prevent or mitigate the effects of such attacks.
Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution

Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e.g., constrained spherical deconvolution (CSD). Here, we apply deep learning to investigate the potential of single shell diffusion weighted MRI to explain histologically observed fiber orientation distributions (FOD) and compare the derived deep learning model with a leading CSD approach. This study (1) demonstrates that there exists additional information in the diffusion signal that is not currently exploited by CSD, and (2) provides an illustrative data-driven model that makes use of this information.
Faster feature selection with a Dropping Forward-Backward algorithm

In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward and stepwise feature selection regression remained widely used due to their simplicity and efficiency. However, they all involving rescanning all the un-selected features again and again. Moreover, many times, the backward steps in stepwise deem unnecessary, as we will illustrate in our example. These remarks motivate us to introduce a novel algorithm that may boost the speed up to 65.77% compared to the stepwise procedure while maintaining good performance in terms of the number of selected features and error rates. Also, our experiments illustrate that feature selection procedures may be a better choice for high-dimensional problems where the number of features highly exceeds the number of samples.
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training. We strive to reduce the energy cost during training, by dropping unnecessary computations from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. For example, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring a top-1 accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving is achieved without degrading inference accuracy.
Strip blob for Monte-Carlo based PET imaging reconstruction

A novel object model, the strip blob, for Positron Emission Tomography (PET) is presented in this article. This object model is formed by combining the generalized natural pixel (GNP) and the blob. This object model combines the advantages of both the GNP and the blob. An Algebraic Reconstruction Technique (ART) based image reconstruction algorithm for strip blob is developed. The algorithm is evaluated using a realistic MonteCarlo simulator, Geant4 Application for Tomography Emission (GATE). The evaluation study suggests that the strip blob is superior to the GNP in term of contrast recovery versus noise reduction though only limited improvements are observed. Keywords—PET, Reconstruction, GATE, Strip Blob