Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans

Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For 2 labels (ground truth, lung lesions) segmentation, the model gets 99.57% of accuracy, 0.2191 of train loss and $0.78\pm 0.03$ of MeanDice±Std, while the 4 labels (ground truth, left lung, right lung, lung lesions) segmentation achieves 98.89% of accuracy, 0.1132 of train loss and $0.83\pm 0.13$ of MeanDice±Std. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant viruses in the COVID-19 situation may also be successfully solved.
Thermal Ringdown of a Kerr Black Hole: Overtone Excitation, Fermi-Dirac Statistics and Holography

We ﬁnd a signiﬁcant destructive interference among Kerr overtones in the early ringdown induced by an extreme mass-ratio merger of a massive black hole and a compact object, and that the ringdown spectrum apparently follows the Fermi-Dirac distribution. We numerically compute the spectral amplitude of gravitational waves induced by a particle plunging into a Kerr black hole and study the excitation of multiple quasi-normal (QN) modes. We ﬁnd that the start time of ringdown is before the strain peak of the signal and corresponds to the time when the particle passes the photon sphere. When the black hole has the near-extremal rotation, the Kerr QN frequencies are close to the fermionic Matsubara frequencies with the Hawking temperature and a chemical potential of the superradiant frequency. We indeed ﬁnd that the absolute square of the spectral amplitude apparently follows the Fermi-Dirac distribution with the chemical potential of around the real QN frequency of the fundamental mode. Fitting the Boltzmann distribution to the data in higher frequencies, the best-ﬁt temperature is found out to be close to the Hawking temperature, especially for rapid rotations. In the near-extremal limit, the gravitational-wave spectrum exhibits a would-be Fermi degeneracy with the Fermi surface at the superradiant frequency ω = µ H . This opens a new possibility that we can test the holographic nature of Kerr black holes, such as the Kerr/CFT correspondence, by observationally searching for the Boltzmann distribution in frequencies higher than µ H without extracting overtones out of ringdown.
MOVE: Effective and Harmless Ownership Verification via Embedded External Features

—Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performed DNN is resource-consuming. Accordingly, the well-trained model is valuable intellectual property for its owner. However, recent studies revealed the threats of model stealing, where the adversaries can obtain a function-similar copy of the victim model, even when they can only query the model. In this paper, we propose an effective and harmless model ownership veriﬁcation (MOVE) to defend against different types of model stealing simultaneously, without introducing new security risks. In general, we conduct the ownership veriﬁcation by verifying whether a suspicious model contains the knowledge of defender-speciﬁed external features. Speciﬁcally, we embed the external features by tempering a few training samples with style transfer. We then train a meta-classiﬁer to determine whether a model is stolen from the victim. This approach is inspired by the understanding that the stolen models should contain the knowledge of features learned by the victim model. In particular, we develop our MOVE method under both white-box and black-box settings to provide comprehensive model protection. Extensive experiments on benchmark datasets verify the effectiveness of our method and its resistance to potential adaptive attacks. The codes for reproducing the main experiments of our method are available at https://github.com/THUYimingLi/MOVE.
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations

Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled k -space data. However, these approaches currently have no guarantees for reconstruction quality and the reliability of such algorithms is only poorly understood. Adversarial attacks oﬀer a valuable tool to understand possible failure modes and worst case performance of DL-based reconstruction algorithms. In this paper we describe adversarial attacks on multi-coil k -space measurements and evaluate them on the recently proposed E2E-VarNet and a simpler UNet-based model. In contrast to prior work, the attacks are targeted to speciﬁcally alter diagnostically relevant regions. Using two realistic attack models (adversarial k -space noise and adversarial rotations) we are able to show that current state-of-the-art DL-based reconstruction algorithms are indeed sensitive to such perturbations to a degree where relevant diagnostic information may be lost. Surprisingly, in our experiments the UNet and the more sophisticated E2E-VarNet were similarly sensitive to such attacks. Our ﬁndings add further to the evidence that caution must be exercised as DL-based methods move closer to clinical practice.
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis

The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of multi-layer deep learning techniques to analyze complex data. For instance, Google incorporated “DeepMind” health mobile tool that integrates & leverage medical data needed to enhance professional healthcare delivery to patients. Moorfield Eye Hospital London introduced DeepMind Research Algorithms with dozens of retinal scans attributes while DeepMind UCL handled the identification of cancerous tissues using CT & MRI Scan tools. Atomise analyzed drugs and chemicals with Deep Learning Neural Networks to identify accurate pre-clinical prescriptions. Health informatics makes medical care intelligent, interactive, cost-effective, and accessible; especially with DL application tools for detecting the actual cause of diseases. The extensive use of neural network tools leads to the expansion of different medical disciplines which mitigates data complexity and enhances 3-4D overlap images using target point label data detectors that support data augmentation, un-semi-supervised learning, multi-modality and transfer learning architecture. Health science over the years focused on artificial intelligence tools for care delivery, chronic care management, prevention/wellness, clinical supports, and diagnosis. The outcome of their research leads to cardiac arrest diagnosis through Heart Signal Computer-Aided Diagnostic tool (CADX) and other multi-functional deep learning techniques that offer care, diagnosis & treatment. Health informatics provides monitored outcomes of human body organs through medical images that classify interstitial lung disease, detects image nodules for reconstruction & tumor segmentation. The emergent medical research applications gave rise to clinical-pathological human-level performing tools for handling Radiological, Ophthalmological, and Dental diagnosis. This research will evaluate methodologies, Deep learning architectures, approaches, bio-informatics, specified function requirements, monitoring tools, ANN (artificial neural network), data labeling & annotation algorithms that control data validation, modeling, and diagnosis of different diseases using smart monitoring health informatics applications.
Padding-only Defenses Add Delay in Tor

Website fingerprinting is an attack that uses size and timing characteristics of encrypted downloads to identify targeted websites. Since this can defeat the privacy goals of anonymity networks such as Tor, many algorithms to defend against this attack in Tor have been proposed in the literature. These algorithms typically consist of some combination of the injection of dummy "padding'' packets with the delay of actual packets to disrupt timing patterns. For usability reasons, Tor is intended to provide low latency; as such, many authors focus on padding-only defenses in the belief that they are "zero-delay. '' We demonstrate through Shadow simulations that by increasing queue lengths, padding-only defenses add delay when deployed network-wide, so they should not be considered "zero-delay. '' We further argue that future defenses should also be evaluated using network-wide deployment simulations.
Viscous flow through a finite-width slit: Boundary conditions and dissipation

We study the hydrodynamic viscous electronic transport in a two-dimensional sample separated into two semi-infinite planes by a one-dimensional infinite barrier. The semi-infinite planes are electrically connected via the finite-size slit in the barrier. We calculate the current through the slit assuming finite voltage drop between the planes and neglecting disorder-induced Ohmic resistance, so that dissipation and resistance are purely viscosity-induced. We find that the only solution to the Stokes equation in this geometry, which yields a finite dissipation at finite resistance (and, hence, is not self-contradictory), is the one that fulfills both the no-stress and no-slip boundary conditions simultaneously. As a remarkable consequence, the obtained velocity profile satisfies the so-called"partial-slip"(Maxwell) boundary condition for any value of the slip length, which drops out from all final results. We also calculate the electronic temperature profile for the small and large heat conductivity, and find asymmetric (with respect to the barrier) temperature patterns in the former case.
Beware of Discarding Used SRAMs: Information is Stored Permanently

Data recovery has long been a focus of the electronics industry for decades by security experts, focusing on hard disk recovery, a type of non-volatile memory. Unfortunately, none of the existing research, neither from academia, industry, or government, has ever considered data recovery from volatile memories. The data is lost when it is powered off, by definition. To the best of our knowledge, we are the first to present an approach to recovering data from a static random access memory. It is conventional wisdom that SRAM loses its contents whenever it turns off, and it is not required to protect sensitive information, e.g., the firmware code, secret encryption keys, etc., when an SRAM-based computing system retires. Unfortunately, the recycling of integrated circuits poses a severe threat to the protection of intellectual properties. In this paper, we present a novel concept to retrieve SRAM data as the aging of SRAMs leads to a power-up state with an imprint of the stored values. We show that our proposed approaches can partially recover the previously used SRAM content. The accuracy of the recovered data can be further increased by incorporating multiple SRAM chips compared to a single one. It is impossible to retrieve the prior content of some stable SRAM cells, where aging shifts these cells towards stability. As the locations of these cells vary from chip to chip due to uncontrollable process variation, the same cell has a higher chance of being unstable or stable against aging in any of the chips, which helps us recover the content. Finally, majority voting is used to combine a set of SRAM chips' data to recover the stored data. We present our experimental result using commercial off-the-shelf SRAM chips with stored binary image data before performing accelerated aging. We demonstrate the successful partial retrieval on SRAM chips that are aged with as little as four hours of accelerated aging with 85° C.
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach

This paper proposes a novel decision-making framework for autonomous vehicles (AVs), called predictor-corrector potential game (PCPG), composed of a Predictor and a Corrector. To enable human-like reasoning and characterize agent interactions, a receding-horizon multi-player game is formulated. To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed. In the PG Predictor, the agent cost functions are heuristically predefined. We acknowledge that the behaviors of other traffic agents, e.g., human-driven vehicles and pedestrians, may not necessarily be consistent with the predefined cost functions. To address this issue, a best response-based PG Corrector is designed. In the Corrector, the action deviation between the ego vehicle prediction and the surrounding agent actual behaviors are measured and are fed back to the ego vehicle decision-making, to correct the prediction errors caused by the inaccurate predefined cost functions and to improve the ego vehicle strategies. Distinguished from most existing game-theoretic approaches, this PCPG 1) deals with multi-player games and guarantees the existence of a pure-strategy Nash equilibrium (PSNE), convergence of the PSNE seeking algorithm, and global optimality of the derived PSNE when multiple PSNE exist; 2) is computationally scalable in a multi-agent scenario; 3) guarantees the ego vehicle safety under certain conditions; and 4) approximates the actual PSNE of the system despite the unknown cost functions of others. Comparative studies between the PG, the PCPG, and the control barrier function (CBF) based approaches are conducted in diverse traffic scenarios, including oncoming traffic scenario and multi-vehicle intersection-crossing scenario.
Applications of Telemedicine in the Middle East and North Africa Region: Benefits Gained and Challenges Faced

Information and communication technology has left a print on all fields of life, including medicine and the health care system. Telemedicine is the perfect way to ensure adequate healthcare delivery to all people at any time, particularly during pandemics, regardless of any geographic or economic considerations. This article investigates the different types, categories, and benefits in addition to the barriers to telemedicine implementation, especially in the Middle East and North Africa (MENA) region. After a thorough review of medical literature related to telemedicine using PubMed, Google Scholar, and some other gray literature, it has been found that telemedicine has been involved in almost all medical specialties with a positive influence on healthcare delivery and medical education and research. It had a major role during the COVID-19 pandemic. However, many obstacles prevent its proper application and need to be addressed carefully by the government and relevant authorities. Due to the rapidly growing population, unequal distribution of healthcare services, and social distancing of the COVID-19 pandemic, the role of telemedicine has become increasingly essential. Regarding medical education and research, telemedicine facilitates the exchange of information and ideas between physicians and professionals from all over the world, bringing these various minds together on a single platform.
Coevolution of social and communicative complexity in lemurs

The endemic lemurs of Madagascar (Lemuriformes: Primates) exhibit great social and communicative diversity. Given their independent evolutionary history, lemurs provide an excellent opportunity to identify fundamental principles in the coevolution of social and communicative traits. We conducted comparative phylogenetic analyses to examine patterns of interspecific variation among measures of social complexity and repertoire sizes in the vocal, olfactory and visual modality, while controlling for environmental factors such as habitat and number of sympatric species. We also examined potential trade-offs in signal evolution as well as coevolution between body mass or brain size and communicative complexity. Repertoire sizes in the vocal, olfactory and visual modality correlated positively with group size, but not with environmental factors. Evolutionary changes in social complexity presumably antedated corresponding changes in communicative complexity. There was no trade-off in the evolution of signals in different modalities and neither body mass nor brain size correlated with any repertoire size. Hence, communicative complexity coevolved with social complexity across different modalities, possibly to service social relationships flexibly and effectively in pair- and group-living species. Our analyses shed light on the requirements and adaptive possibilities in the coevolution of core elements of social organization and social structure in a basal primate lineage. This article is part of the theme issue ‘Cognition, communication and social bonds in primates’.
Flexible signalling strategies by victims mediate post-conflict interactions in bonobos

Compared to other animals, humans supposedly excel at voluntarily controlling and strategically displaying emotional signals. Yet, new data shows that nonhuman great apes' emotion expressions may also be subject to voluntary control. A key context to further explore this is during post-conflict (PC) periods, where signalling by distressed victims may influence bystander responses, including the offering of consolation. To address this, our study investigates the signalling behaviour of sanctuary-living bonobo victims following aggression and its relation to audience composition and PC interactions. Results show that the production of paedomorphic signals by victims (regardless of age) increased their chances of receiving consolation. In adults, the production of such signals additionally reduced the risk of renewed aggression from opponents. Signal production also increased with audience size, yet strategies differed by age: while immatures reduced signalling in proximity of close-social partners, adults did so especially after receiving consolation. These results suggest that bonobos can flexibly adjust their emotion signalling to influence the outcome of PC events, and that this tendency has a developmental trajectory. Overall, these findings highlight the potential role that flexible emotion communication played in the sociality of our last common ancestor with Pan. This article is part of the theme issue ‘Cognition, communication and social bonds in primates’.
Predictors of post-stroke cognitive impairment at three-month following first episode of stroke among patients attended at tertiary hospitals in Dodoma, central Tanzania: A protocol of a prospective longitudinal observational study metadata

Introduction Neurocognitive deficits after stroke are a common manifestation and pose a significant impact on the quality of life for patients and families; however, little attention is given to the burden and associated impact of cognitive impairment following stroke. The study aims to determine the prevalence and predictors of post-stroke cognitive impairment (PSCI) among adult stroke patients admitted to tertiary hospitals in Dodoma, Tanzania Methodology A prospective longitudinal study is conducted at tertiary hospitals in the Dodoma region, central Tanzania. So far, participants with the first stroke confirmed by CT/MRI brain aged [≥] 18 years who meet the inclusion criteria are enrolled and followed up. Baseline socio-demographic and clinical factors are identified during admission, while other clinical variables are determined during the three-month follow-up period. Descriptive statistics are used to summarize data; continuous data will be reported as Mean (SD) or Median (IQR), and categorical data will be summarized using proportions and frequencies. Univariate and multivariate logistic regression analysis will be used to determine predictors of PSCI
Preservation solutions for static cold storage in donation after circulatory death and donation after brain death liver transplantation in the United States

Static cold preservation remains the cornerstone for storing donor livers following procurement; however, the choice between University of Wisconsin solution (UW) and histidine‐tryptophan‐ketoglutarate solution (HTK) remains controversial. Recent International Liver Transplantation Society (ILTS) guidelines have recommended avoiding HTK for donation after circulatory death (DCD) grafts based on older reports. We studied the latest US adult graft outcomes in three recent eras (2006–2010, 2011–2015, 2016–2020) comparing HTK and UW among 5956 DCD LTs: 3873 (65.0%) used UW and 1944 (32.7%) used HTK. In a total of 82,679 donation after brain death (DBD) liver transplantations (LTs), 63,511 (76.8%) used UW and 15,855 (19.2%) used HTK. The HTK group had higher 1‐year and 5‐year graft survival rates of 89.7% and 74.3%, respectively, compared with 85.9% and 70.8% in the UW group in the 2016–2020 era (p = 0.005). This difference remained when adjusted for important potential confounders (hazard ratio, 0.78; 95% confidence interval: 0.60, 0.99). There were no differences between groups among DCD LTs in the earlier eras or among DBD LTs in all eras (all p values > 0.05). The latest US data suggest that HTK is at least noninferior to UW for preserving DCD livers. These data support HTK use in DCD LT and contradict ILTS guidance.
CMOS Cross-Coupled Differential-Drive Rectifier in Subthreshold Operation for Ambient RF Energy Harvesting—Model and Analysis

This brief models and analyzes the CMOS cross-coupled differential-drive (CCDD) rectifier in subthreshold operation for ambient radio frequency (RF) energy harvesting. The developed model can predict accurately the output voltage against the input RF level, number of stages, transistor size, and output load. For validation, three CCDD rectifiers with 3, 4, and 7 stages, and dissimilar transistor sizes, were fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$ {\mu }\text{m}$ </tex-math></inline-formula> CMOS. Each rectifier was tested against different load conditions (10/30/100/500 <inline-formula> <tex-math notation="LaTeX">$\text{k} \Omega $ </tex-math></inline-formula> and open circuit), and input RF levels below the threshold level of the transistor. The verified model has an accuracy of >90% up to 0 dBm input power, and 80% up to 3 dBm input power, across all denoted variations.