Miracle-dz/UniFAR-FTUs huggingface dataset:
Pemphigus vulgaris and infections - A retrospective study

Pemphigus Vulgaris is an autoimmune disease and is treated with immunosuppressive therapy. This reason but either the fact that the skin is not intact are predisposition factors why patients with Pemphigus vulgaris are predisposed to infections. We aimed to study the frequency of infections, categorical and numeric variables related to infection, in Pemphigus vulgaris s patients admitted to dermatology service. This retrospective study was conducted on 130 patients diagnosed with Pemphigus vulgaris and hospitalized at Dermatology Clinic during the year 2020-2022. The diagnosis of Pemphigus vulgaris was confirmed by biopsy and indirect/direct immunofluorescence findings. Data were collected through the charts in the Statistics Department, in UHC "Mother Teresa", Tirana, Albania. 130 patients (70 males, 60 females) were hospitalized in UHC " Mother Theresa) during the period 2020-20122. 48.69% of patients had infections. The occurrence of infection was significantly related to the severity of the disease, multiple admissions, and hospitalization duration. The most frequent infections encountered: are skin infections, urinary infections, pulmonary infections, and opportunistic infections (candida). The most common pathogenic germs isolated from cultures were Staphylococcus aureus and Escherichia coli. Infections are the most common complication among patients treated for Pemphigus vulgaris. Patients with longer duration disease and hospitalization have a higher probability to develop infections, mainly skin infections. Due to the limitations of a retrospective study, we recommend a prospective study.
Heat Transfer and Pressure Drop in a Developing Channel Flow with Streamwise Vortices

Experiments to assess the heat transfer and pressure-drop effects of delta-wing vortex generators placed at the entrance of developing channel flows are reported in this study. The experimental geometry simulates common heat exchanger configurations and tests are conducted over a velocity range important to heating, air conditioning and refrigeration. An innovative liquid-crystal thermography technique is used to determine the local and average Nusselt numbers for an isoflux channel wall, and conventional methods are used to determine the Fanning friction factor. Vortex generators with aspect ratios of A = 2 and A = 4 are studied at attack angles of a. = 20° to 45°·. The results indicate that the streamwise vortices generated by a delta wing can enhance local Nusselt numbers by more than 200% in a developing channel flow. Under some conditions, the spatially average Nusselt number nearly doubled for a heat transfer area that was 37 to 63 times the wing area. The Fanning friction factor increased by a few percent to nearly 60%, depending on the Reynolds number.
Multi-classification on the diagnosis of four early stages of Alzheimer’s disease by transfer learning models

Nowadays, deep learning models are widely used in medical diagnosis, with advantages of higher accuracy and better capability of analyzing complex medical images over traditional check. This work compares seven commonly used CNN models - VGG16, ResNet50, InceptionV3, DenseNet-121, Xception, Inception-Resnet V2, and MobilenetV3-small - on the diagnosis of the four early stages of Alzheimer’s Disease – Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Models are initially adjusted with a dense layer of four outputs on the multi-classification problem and deployed in various overfitting prevention setting. Also, by using transfer learning and fine-tuning technique, the training accuracy and testing accuracy of the seven models have both improved by over 30%. Among all, DenseNet121 has the overall best performance of 95.48% testing accuracy, 0.92 F1- score and second smallest number of parameters of 7,041,604, with a good portability on mobile computing devices, likely due to the reuse of feature maps to extract more accurate features. Inception-ResNet-v2 is adopted when only considering accuracy and reliability and due to its largest number of parameters, Inception-ResNet-v2 is more suggested to be used on computer medical application. VGG16 has the highest accuracy of 96.11% and a small number of parameters of 14,716,740, however, it is currently not suitable for applying to real-life application since its F1-score of 0.8943 and precision of 0.8266, indicating numerous false cases.
An introduction to continuous optimization for imaging

A large number of imaging problems reduce to the optimization of a cost function, with typical structural properties. The aim of this paper is to describe the state of the art in continuous optimization methods for such problems, and present the most successful approaches and their interconnections. We place particular emphasis on optimal first-order schemes that can deal with typical non-smooth and large-scale objective functions used in imaging problems. We illustrate and compare the different algorithms using classical non-smooth problems in imaging, such as denoising and deblurring. Moreover, we present applications of the algorithms to more advanced problems, such as magnetic resonance imaging, multilabel image segmentation, optical flow estimation, stereo matching, and classification.
Starobinsky-type B-L Higgs inflation Leading beyond MSSM

Models of induced-gravity inflation are formulated within Supergravity employing as inflaton the Higgs field which leads to a spontaneous breaking of a U(1)_{B-L} symmetry at Mgut=2x10^16 GeV. We use a renormalizable superpotential, fixed by a U(1) R symmetry, and logarithmic or semi-logarithmic Kahler potentials with integer prefactors which exhibit a quadratic non-minimal coupling to gravity. We find inflationary solutions of Starobinsky type in accordance with the observations. The inflaton mass is predicted to be of the order of 10^13 GeV. The model can be nicely linked to MSSM offering an explanation of the magnitude of the mu parameter consistently with phenomenological data. Also it allows for baryogenesis via non-thermal leptogenesis, provided that the gravitino is heavier than about 10 TeV.
Inclusion of radiation in the CCM approach of the $\phi^4$ model

We present an effective Lagrangian for the $\phi^4$ model that includes radiation modes as collective coordinates. The coupling between these modes to the discrete part of the spectrum, i.e., the zero mode and the shape mode, gives rise to different phenomena which can be understood in a simple way in our approach. In particular, the energy transfer between radiation, translation and shape modes is carefully investigated in the single-kink sector. Finally, we also discuss the inclusion of radiation modes in the study of oscillons. This leads to relevant phenomena such as the oscillon decay and the kink-antikink creation.
Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse

3D shape generation techniques utilizing deep learning are increasing attention from both computer vision and architectural design. This survey focuses on investigating and comparing the current latest approaches to 3D object generation with deep generative models (DGMs), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), 3D-aware images, and diffusion models. We discuss 187 articles (80.7% of articles published between 2018-2022) to review the field of generated possibilities of architecture in virtual environments, limited to the architecture form. We provide an overview of architectural research, virtual environment, and related technical approaches, followed by a review of recent trends in discrete voxel generation, 3D models generated from 2D images, and conditional parameters. We highlight under-explored issues in 3D generation and parameterized control that is worth further investigation. Moreover, we speculate that four research agendas including data limitation, editability, evaluation metrics, and human-computer interaction are important enablers of ubiquitous interaction with immersive systems in architecture for computer-aided design Our work contributes to researchers' understanding of the current potential and future needs of deep learnings in generating virtual architecture.
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skepticism by practitioners. This disconnection is due to the intrinsically limited scope of research papers, many of which primarily aim to demonstrate new methods "outperforming" prior work—oftentimes overlooking the practical implications for deploying the proposed solutions in real systems. Unfortunately, the value of ML for NID depends on a plethora of factors, such as hardware, that are often neglected in scientific literature.This paper aims to reduce the practitioners’ skepticism towards ML for NID by changing the evaluation methodology adopted in research. After elucidating which factors influence the operational deployment of ML in NID, we propose the notion of pragmatic assessment, which enable practitioners to gauge the real value of ML methods for NID. Then, we show that the state-of-research hardly allows one to estimate the value of ML for NID. As a constructive step forward, we carry out a pragmatic assessment. We re-assess existing ML methods for NID, focusing on the classification of malicious network traffic, and consider: hundreds of configuration settings; diverse adversarial scenarios; and four hardware platforms. Our large and reproducible evaluations enable estimating the quality of ML for NID. We also validate our claims through a user-study with security practitioners.
Causal effects of intervening variables in settings with unmeasured confounding

We present new results on average causal effects in settings with unmeasured exposure-outcome confounding. Our results are motivated by a class of estimands, e.g., frequently of interest in medicine and public health, that are currently not targeted by standard approaches for average causal effects. We recognize these estimands as queries about the average causal effect of an intervening variable. We anchor our introduction of these estimands in an investigation of the role of chronic pain and opioid prescription patterns in the opioid epidemic, and illustrate how conventional approaches will lead unreplicable estimates with ambiguous policy implications. We argue that our altenative effects are replicable and have clear policy implications, and furthermore are non-parametrically identified by the classical frontdoor formula. As an independent contribution, we derive a new semiparametric efficient estimator of the frontdoor formula with a uniform sample boundedness guarantee. This property is unique among previously-described estimators in its class, and we demonstrate superior performance in finite-sample settings. Theoretical results are applied with data from the National Health and Nutrition Examination Survey.
Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History

Most human interactions occur in the form of spoken conversations where the semantic meaning of a given utterance depends on the context. Each utterance in spoken conversation can be represented by many semantic and speaker attributes, and there has been an interest in building Spoken Language Understanding (SLU) systems for automatically predicting these attributes. Recent work has shown that incorporating dialogue history can help advance SLU performance. However, separate models are used for each SLU task, leading to an increase in inference time and computation cost. Motivated by this, we aim to ask: can we jointly model all the SLU tasks while incorporating context to facilitate low-latency and lightweight inference? To answer this, we propose a novel model architecture that learns dialog context to jointly predict the intent, dialog act, speaker role, and emotion for the spoken utterance. Note that our joint prediction is based on an autoregressive model and we need to decide the prediction order of dialog attributes, which is not trivial. To mitigate the issue, we also propose an order agnostic training method. Our experiments show that our joint model achieves similar results to task-specific classifiers and can effectively integrate dialog context to further improve the SLU performance.1
Faster Submodular Maximization for Several Classes of Matroids

The maximization of submodular functions have found widespread application in areas such as machine learning, combinatorial optimization, and economics, where practitioners often wish to enforce various constraints; the matroid constraint has been investigated extensively due to its algorithmic properties and expressive power. Recent progress has focused on fast algorithms for important classes of matroids given in explicit form. Currently, nearly-linear time algorithms only exist for graphic and partition matroids [ICALP '19]. In this work, we develop algorithms for monotone submodular maximization constrained by graphic, transversal matroids, or laminar matroids in time near-linear in the size of their representation. Our algorithms achieve an optimal approximation of $1-1/e-\epsilon$ and both generalize and accelerate the results of Ene and Nguyen [ICALP '19]. In fact, the running time of our algorithm cannot be improved within the fast continuous greedy framework of Badanidiyuru and Vondr\'ak [SODA '14]. To achieve near-linear running time, we make use of dynamic data structures that maintain bases with approximate maximum cardinality and weight under certain element updates. These data structures need to support a weight decrease operation and a novel FREEZE operation that allows the algorithm to freeze elements (i.e. force to be contained) in its basis regardless of future data structure operations. For the laminar matroid, we present a new dynamic data structure using the top tree interface of Alstrup, Holm, de Lichtenberg, and Thorup [TALG '05] that maintains the maximum weight basis under insertions and deletions of elements in $O(\log n)$ time. For the transversal matroid the FREEZE operation corresponds to requiring the data structure to keep a certain set $S$ of vertices matched, a property that we call $S$-stability.
Learning, Diversity and Adaptation in Changing Environments: The Role of Weak Links

Adaptation to dynamic conditions requires a certain degree of diversity. If all agents take the best current action, learning that the underlying state has changed and behavior should adapt will be slower. Diversity is harder to maintain when there is fast communication between agents, because they tend to find out and pursue the best action rapidly. We explore these issues using a model of (Bayesian) learning over a social network. Agents learn rapidly from and may also have incentives to coordinate with others to whom they are connected via strong links. We show, however, that when the underlying environment changes sufficiently rapidly, any network consisting of just strong links will do only a little better than random choice in the long run. In contrast, networks combining strong and weak links, whereby the latter type of links transmit information only slowly, can achieve much higher long-run average payoffs. The best social networks are those that combine a large fraction of agents into a strongly-connected component, while still maintaining a sufficient number of smaller communities that make diverse choices and communicate with this component via weak links.
Ensemble Learning for CME Arrival Time Prediction

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 hours.
Online Platt Scaling with Calibeating

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i. d. and non-i.i. d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.
Efficient Numerical Methods for Pricing American Options under Lévy Models

Two new numerical methods for the valuation of American and Bermudan options are proposed, which admit a large class of asset price models for the underlying. In particular, the methods can be applied with Levy models that admit jumps in the asset price. These models provide a more realistic description of market prices and lead to better calibration results than the well-known Black-Scholes model. The proposed methods are not based on the indirect approach via partial differential equations, but directly compute option prices as risk-neutral expectation values. The expectation values are approximated by numerical quadrature methods. While this approach is initially limited to European options, the proposed combination with interpolation methods also allows for pricing of Bermudan and American options. Two different interpolation methods are used. These are cubic splines on the one hand and a mesh-free interpolation by radial basis functions on the other hand. The resulting valuation methods allow for an adaptive space discretization and error control. Their numerical properties are analyzed and, finally, the methods are validated and tested against various single-asset and multi-asset options under different market models.
On–off pumping for drag reduction in a turbulent channel flow

Abstract We show that the energy required by a turbulent flow to displace a given amount of fluid through a straight duct in a given time interval can be reduced by modulating in time the pumping power. The control strategy is hybrid: it is passive, as it requires neither a control system nor control energy, but it manipulates how pumping energy is delivered to the system (as in active techniques) to increase the pumping efficiency. Our control employs a temporally periodic pumping pattern, where a short and intense acceleration (in which the pumping system is on) followed by a longer deceleration (in which the pumping system is off) makes the flow alternately visit a quasi-laminar and a turbulent state. The computational study is for a plane channel flow, and employs direct numerical simulations, which present specific computational challenges, for example the highly varying instantaneous value of the Reynolds number, and the importance of discretisation effects. Particular care is devoted to a meaningful definition of drag reduction in the present context. The ability of the forcing to yield significant savings is demonstrated. Since only a small portion of the parameter space is investigated, the best performance of the control technique remains to be assessed.
Fourier-Gegenbauer Pseudospectral Method for Solving Periodic Higher-Order Fractional Optimal Control Problems

In [1], we inaugurated a new area of optimal control (OC) theory that we called"periodic fractional OC theory,"which was developed to find optimal ways to periodically control a fractional dynamic system. The typical mathematical formulation in this area includes the class of periodic fractional OC problems (PFOCPs), which can be accurately solved numerically for a fractional order {\alpha} in the range 0<{\alpha}<1 using Fourier collocation at equally spaced nodes and Fourier and Gegenbauer quadratures. In this study, we extend this earlier work to cover periodic higher-order fractional OC problems (PHFOCPs) of any positive non-integer fractional order {\alpha}.
Event-Free Moving Object Segmentation from Moving Ego Vehicle

Moving object segmentation (MOS) in dynamic scenes is challenging for autonomous driving, especially for sequences obtained from moving ego vehicles. Most state-of-the-art methods leverage motion cues obtained from optical flow maps. However, since these methods are often based on optical flows that are pre-computed from successive RGB frames, this neglects the temporal consideration of events occurring within inter-frame and limits the practicality of these methods in real-life situations. To address these limitations, we propose to exploit event cameras for better video understanding, which provide rich motion cues without relying on optical flow. To foster research in this area, we first introduce a novel large-scale dataset called DSEC-MOS for moving object segmentation from moving ego vehicles. Subsequently, we devise EmoFormer, a novel network able to exploit the event data. For this purpose, we fuse the event prior with spatial semantic maps to distinguish moving objects from the static background, adding another level of dense supervision around our object of interest - moving ones. Our proposed network relies only on event data for training but does not require event input during inference, making it directly comparable to frame-only methods in terms of efficiency and more widely usable in many application cases. An exhaustive comparison with 8 state-of-the-art video object segmentation methods highlights a significant performance improvement of our method over all other methods. Project Page: https://github.com/ZZY-Zhou/DSEC-MOS.
Detecting the secondary spin with extreme mass ratio inspirals in Scalar-Tensor theory

In this paper, we investigate the detectability of secondary spin in the extreme mass ratio inspirals system within a modified gravity model coupled with a scalar field. The central black hole, which reduces to a Kerr one, is circularly spiralled by a scalar-charged spinning secondary body on the equatorial plane. The analysis reveals that the presence of the scalar field amplifies the secondary spin effect, allowing for a lower limit of the detectability and an improved resolution of the secondary spin when the scalar charge is sufficiently large. Our findings suggest that the secondary spin detection is more feasible when the primary mass is not large, and TianQin is the optimal choice for detection.