Miracle-dz/UniFAR-FTUs huggingface dataset:
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm

One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy and interpretability for user are considered as trade-off in OCC methods. Classifier based on Hyper-Rectangle (H-RTGL) is a sort of classifier that can be a remedy for such trade-off and uses H-RTGL formulated by conjunction of geometric rules called interval. This interval can be basis of interpretability since it can be easily understood by user. However, existing H-RTGL based OCC classifiers have limitations that (i) most of them cannot reflect density of target class and (ii) that considering density has primitive interval generation method, and (iii) there exists no systematic procedure for hyperparameter of H-RTGL based OCC classifier, which influences classification performance of classifier. Based on these remarks, we suggest One-Class Hyper-Rectangle Descriptor based on density (1-HRD_d) with more elaborate interval generation method including parametric and nonparametric approaches. In addition, we designed Genetic Algorithm (GA) that consists of chromosome structure and genetic operators for systematic generation of 1-HRD_d by optimization of hyperparameter. Our work is validated through a numerical experiment using actual data set with comparison of existing OCC algorithms along with other H-RTGL based classifiers.
Customizing Trusted AI Accelerators for Efficient Privacy-Preserving Machine Learning

The use of trusted hardware has become a promising solution to enable privacy-preserving machine learning. In particular, users can upload their private data and models to a hardware-enforced trusted execution environment (e.g. an enclave in Intel SGX-enabled CPUs) and run machine learning tasks in it with confidentiality and integrity guaranteed. To improve performance, AI accelerators have been widely employed for modern machine learning tasks. However, how to protect privacy on an AI accelerator remains an open question. To address this question, we propose a solution for efficient privacy-preserving machine learning based on an unmodified trusted CPU and a customized trusted AI accelerator. We carefully leverage cryptographic primitives to establish trust and protect the channel between the CPU and the accelerator. As a case study, we demonstrate our solution based on the open-source versatile tensor accelerator. The result of evaluation shows that the proposed solution provides efficient privacy-preserving machine learning at a small design cost and moderate performance overhead.
Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

Multimodal image registration is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.
Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint

Based on photoacoustic effect, photoacoustic tomography is developing very fast in recent years, and becoming an important imaging tool for both preclinical and clinical studies. With enough ultrasound transducers placed around the biological tissue, PAT can provide both deep penetration and high image contrast by hybrid usage of light and sound. However, considering space and measurement environmental limitations, transducers are always placed in a limited-angle way, which means that the other side without transducer coverage suffers severe information loss. With conventional image reconstruction algorithms, the limited-view tissue induces artifacts and information loss, which may cause doctors misdiagnosis or missed diagnosis. In order to solve limited-view PA imaging reconstruction problem, we propose to use both time domain and frequency domain reconstruction algorithms to get delay-and-sum (DAS) image inputs and k-space image inputs. These dual domain images share nearly same texture information but different artifact information, which can teach network how to distinguish these two kinds of information at input level. In this paper, we propose Dual Domain Unet (DuDoUnet) with specially designed Information Sharing Block (ISB), which can further share two domains' information and distinguish artifacts. Besides, we use mutual information (MI) with an auxiliary network, whose inputs and outputs are both ground truth, to compensate prior knowledge of limited-view PA inputs. The proposed method is verified with a public clinical database, and shows superior results with SSIM = 93.5622% and PSNR = 20.8859.
Numerically exact treatment of dissipation in a driven two-level system

Recent progress in the experimental implementations of controlled quantum systems and especially of tunable dissipation for them provides an ideal starting point for precision studies of open quantum systems and their models. However, typically the standard weak-coupling approximations are very accurate in describing the dynamics of the simplest and most easily experimentally approachable system, the qubit, since it is challenging to engineer dissipation that is strong relative to the qubit frequency while preserving a high level of control. In this paper, we propose to overcome such challenges by studying a driven qubit, for which the weak-coupling approaches may fail at dissipation rates comparable to the Rabi frequency of the drive which is typically much lower than the qubit frequency. Consequently, we observe such failure of the temporally local Lindblad master equation, the induced dynamics of which are compared with a numerically exact method. To this end, we propose a metric that may be used in experiments to verify our predictions. In addition, we study the well-known Mollow triplet and observe its meltdown owing to dissipation in an experimentally feasible parameter regime. Thus we expect our results to inspire future experimental research on open quantum systems and expand the understanding on this intriguing field of physics.
Robust approach for variable selection with high dimensional longitudinal data analysis

This article proposes a new robust smooth‐threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates pn increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth‐threshold estimating equations under “large n, diverging pn ” are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.
Conversion to Parkinsonism and Dementia in REM-Sleep Behavior Disorder Using the Chronobiotic Melatonin

Background: Isolated REM sleep behavior disorder (iRBD), a reliable prodromal stage marker of alpha-synucleinopathies like Parkinson disease or Lewy body dementia, offers an early window for disease-modifying intervention. Current treatments of iRBD, including the two level B therapies with clonazepam and melatonin, are considered symptomatic. However, numbers of reported patients treated with melatonin are low and whether melatonin has disease-modifying potential is unclear. Methods: This single-center, prospective cohort study included 206 consecutive patients diagnosed with iRBD until January 2020. Thirty-nine patients had applied mixed treatments on the advice of the referring physician, 167 had administered melatonin according to our chronobiotic protocol (low dose, longer 6 months, always-at-the-same-clock-time, between 10 and 11 pm - corrected for chronotype), which differs from existing melatonin prescriptions. Clinical examination to determine phenoconversion was performed from October 2018 to August 2020. To evaluate generalizability, we compared factors such as neuropsychological and neuromotor performance, olfactory ability, neurovegetative behavior, and dopamine transporter density in our patients with those reported for other cohorts. Primary outcome was phenoconversion to clinical synucleinopathy, assessed using Kaplan-Meier analysis. Secondary outcomes were changes in cognitive and motor performance, and in RBD-symptom severity, analyzed using mixed models. Results: RBD characteristics were comparable to those in other published cohorts, including frequency of phenoconversion in our patients with mixed treatments (10/39; follow-up 3.1 +/- 2.1 years). In contrast, long-term melatonin-treated patients rarely converted (4/167; follow-up 4.2 +/- 3.1 years; hazard-ratio 0.07, 95% CI, 0.02-0.22, p<0.001). Neuromotor and neuropsychological performance did not decline, improved in some domains. Symptom severity gradually improved over the first 4 weeks of treatment (Clinical Global Impression Severity: 5.7 vs. 3.0) and remained stable over years, also in those patients who had stopped melatonin intake after 6 months. The initial response was slower in patients with melatonin suppressing (beta blockers) or REM sleep spoiling co-medication (antidepressants) and failed with inadequate timing of melatonin intake. Conclusion: Clock-timed melatonin treatment in patients with iRBD appears to be associated with a marked reduction in the development of parkinsonism and dementia as well as with an improvement in neuromotor, cognitive, and specific RBD symptoms. Findings suggest that melatonin treatment may have disease-modifying effects in synucleinopathies. The fact that melatonin is available anywhere at low cost provides the perspective of immediate clinical application in patients at risk for clinical synucleinopathy. On the other hand, clock-time dependency challenges existing prescription guidelines for melatonin. Melatonin should be acknowledged as the darkness signal to circadian clock-work rather than a hypnotic.
Online Influence Maximization under Linear Threshold Model

Online influence maximization (OIM) is a popular problem in social networks to learn influence propagation model parameters and maximize the influence spread at the same time. Most previous studies focus on the independent cascade (IC) model under the edge-level feedback. In this paper, we address OIM in the linear threshold (LT) model. Because node activations in the LT model are due to the aggregated effect of all active neighbors, it is more natural to model OIM with the node-level feedback. And this brings new challenge in online learning since we only observe aggregated effect from groups of nodes and the groups are also random. Based on the linear structure in node activations, we incorporate ideas from linear bandits and design an algorithm LT-LinUCB that is consistent with the observed feedback. By proving group observation modulated (GOM) bounded smoothness property, a novel result of the influence difference in terms of the random observations, we provide a regret of order $\tilde{O}(\mathrm{poly}(m)\sqrt{T})$, where $m$ is the number of edges and $T$ is the number of rounds. This is the first theoretical result in such order for OIM under the LT model. In the end, we also provide an algorithm OIM-ETC with regret bound $O(\mathrm{poly}(m)\ T^{2/3})$, which is model-independent, simple and has less requirement on online feedback and offline computation.
Approximating the Weighted Minimum Label s-t Cut Problem

In the weighted (minimum) {\sf Label $s$-$t$ Cut} problem, we are given a (directed or undirected) graph $G=(V,E)$, a label set $L = \{\ell_1, \ell_2, \dots, \ell_q \}$ with positive label weights $\{w_\ell\}$, a source $s \in V$ and a sink $t \in V$. Each edge edge $e$ of $G$ has a label $\ell(e)$ from $L$. Different edges may have the same label. The problem asks to find a minimum weight label subset $L'$ such that the removal of all edges with labels in $L'$ disconnects $s$ and $t$. The unweighted {\sf Label $s$-$t$ Cut} problem (i.e., every label has a unit weight) can be approximated within $O(n^{2/3})$, where $n$ is the number of vertices of graph $G$. However, it is unknown for a long time how to approximate the weighted {\sf Label $s$-$t$ Cut} problem within $o(n)$. In this paper, we provide an approximation algorithm for the weighted {\sf Label $s$-$t$ Cut} problem with ratio $O(n^{2/3})$. The key point of the algorithm is a mechanism to interpret label weight on an edge as both its length and capacity.
Exploring Gender Disparities in Time to Diagnosis

Sex and gender-based healthcare disparities contribute to differences in health outcomes. We focus on time to diagnosis (TTD) by conducting two large-scale, complementary analyses among men and women across 29 phenotypes and 195K patients. We first find that women are consistently more likely to experience a longer TTD than men, even when presenting with the same conditions. We further explore how TTD disparities affect diagnostic performance between genders, both across and persistent to time, by evaluating gender-agnostic disease classifiers across increasing diagnostic information. In both fairness analyses, the diagnostic process favors men over women, contradicting the previous observation that women may demonstrate relevant symptoms earlier than men. These analyses suggest that TTD is an important yet complex aspect when studying gender disparities, and warrants further investigation.
Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning

Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction, however, is often found in unstructured clinician notes within medical records but manual review by experts is time consuming and often prone to errors. Automated mining of these notes presents a potential opportunity to label patients with cognitive concerns who could benefit from an evaluation or be referred to specialist care. In order to identify patients with cognitive concerns in electronic medical records, we applied natural language processing (NLP) algorithms and compared model performance to a baseline model that used structured diagnosis codes and medication data only. An attention-based deep learning model outperformed the baseline model and other simpler models.
$L^2$ boundedness of pseudodifferential operators on manifolds with ends

We investigate properties of pseudodifferential operators on $L^2$ space on manifold with ends including asymptotically conical or hyperbolic ends. Our pseudodifferential operators are a generalization of the canonical quantization which naturally appears in the quantum mechanics on curved spaces. We prove a Calderon-Vaillancourt type theorem for our pseudodifferential operators and discuss a construction of parametrix of elliptic differential operators on manifolds with ends.
Towards Better Approximation of Graph Crossing Number

Graph Crossing Number is a fundamental and extensively studied problem with wide ranging applications. In this problem, the goal is to draw an input graph $G$ in the plane so as to minimize the number of crossings between the images of its edges. The problem is notoriously difficult, and despite extensive work, non-trivial approximation algorithms are only known for bounded-degree graphs. Even for this special case, the best current algorithm achieves a $\tilde{O}(\sqrt{n})$-approximation, while the best current negative results do not rule out constant-factor approximation. All current approximation algorithms for the problem build on the same paradigm, which is also used in practice: compute a set $E^{\prime}$ of edges (called a planarizing set) such that $G\ \backslash\ E^{\prime}$ is planar; compute a planar drawing of $G\ \backslash\ E^{\prime}$; then add the drawings of the edges of $E^{\prime}$ to the resulting drawing. Unfortunately, there are examples of graphs $G$, in which any implementation of this method must incur $\Omega$ (OPT2) crossings, where OPT is the value of the optimal solution. This barrier seems to doom the only currently known approach to designing approximation algorithms for the problem, and to prevent it from yielding a better than $O(\sqrt{n})$-approximation. In this paper we propose a new paradigm that allows us to overcome this barrier. We show an algorithm, that, given a bounded-degree graph G and a planarizing E' set of its edges, computes another planarizing edge set E" with E' ⊆ E", such that |E"| is relatively small, and there exists a near-optimal drawing of in which no edges of participate in crossings. This allows us to reduce the Crossing Number problem to Crossing Number with Rotation System – a variant of the Crossing Number problem, in which the ordering of the edges incident to every vertex is fixed as part of input. In our reduction, we obtain an instance of this problem, where is roughly bounded by the crossing number of the original graph. We show a randomized algorithm for this new problem, that allows us to obtain an - approximation for Graph Crossing Number on bounded-degree graphs, for some constant.
Generating unfavourable VaR scenarios with patchwork copulas.

The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union.
Spin and Polarization in High-Energy Hadron-Hadron and Lepton-Hadron Scattering

The role of spin degrees of freedom in high-energy hadron-hadron and lepton-hadron scattering is reviewed with emphasis on the dominant role of soft, diffractive, non-perturbative effects. Explicit models based on analyticity and Regge-pole theory, including the pomeron trajectory (gluon exchange in the t channel) are discussed. We argue that there is a single, universal pomeron in Nature, manifest as relatively “soft” or “hard”, depending on the kinematics considered. Both the pomeron and the non-leading (secondary) Regge trajectories, made of quarks are non-linear, complex functions. They are populated by a finite number of resonances: known baryons and mesons in case of the reggeons and hypothetical glueballs in case of the pomeron (“oddballs” on the odderon trajectory). Explicit models and fits are presented that may be used in recovering generalized parton distributions from deeply virtual Compton scattering and electoproduction of vector mesons.
Different Clusters in Patients with Chronic Obstructive Pulmonary Disease (COPD): A Two-Center Study in Brazil

Background Chronic obstructive pulmonary disease (COPD) has a functional definition. However, differences in clinical characteristics and systemic manifestations make COPD a heterogeneous disease and some manifestations have been associated with different risks of acute exacerbations, hospitalizations, and death. Objective Therefore, the objective of the study was to evaluate possible clinical clusters in COPD at two study centers in Brazil and identify the associated exacerbation and mortality rate during 1 year of follow-up. Methods We included patients with COPD and all underwent an evaluation composed of the Charlson Index, body mass index (BMI), current pharmacological treatment, smoking history (packs-year), history of exacerbations/hospitalizations in the last year, spirometry, six-minute walking test (6MWT), quality of life questionnaires, dyspnea, and hospital anxiety and depression scale. Blood samples were also collected for measurements of C-reactive protein (CRP), blood gases, laboratory analysis, and blood count. For the construction of the clusters, 13 continuous variables of clinical importance were considered: hematocrit, CRP, triglycerides, low density lipoprotein, absolute number of peripheral eosinophils, age, pulse oximetry, BMI, forced expiratory volume in the first second, dyspnea, 6MWD, total score of the Saint George Respiratory Questionnaire and packs-year of smoking. We used the Ward and K-means methods and determined the best silhouette value to identify similarities of individuals within the cluster (cohesion) in relation to the other clusters (separation). The number of clusters was determined by the heterogeneity values of the cluster, which in this case was determined as four clusters. Results We evaluated 301 COPD patients and identified four different groups of COPD patients. The first cluster (203 patients) was characterized by fewer symptoms and lower functional severity of the disease, the second cluster by higher values of peripheral eosinophils, the third cluster by more systemic inflammation and the fourth cluster by greater obstructive severity and worse gas exchange. Cluster 2 had an average of 959±3 peripheral eosinophils, cluster 3 had a higher prevalence of nutritional depletion (46.1%), and cluster 4 had a higher BODE index. Regarding the associated comorbidities, we found that only obstructive sleep apnea syndrome and pulmonary thromboembolism were more prevalent in cluster 4. Almost 50% of all patients presented an exacerbation during 1 year of follow-up. However, it was higher in cluster 4, with 65% of all patients having at least one exacerbation. The mortality rate was statistically higher in cluster 4, with 26.9%, vs 9.6% in cluster 1. Conclusion We could identify four clinical different clusters in these COPD populations, that were related to different clinical manifestations, comorbidities, exacerbation, and mortality rate. We also identified a specific cluster with higher values of peripheral eosinophils.
Generalized kernel two-sample tests

Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space mainly target specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power improvements over existing kernel two-sample tests for a wide range of alternatives. We also propose alternative testing procedures that maintain high power with low computational cost, offering easy off-the-shelf tools for large datasets. The new approaches are compared to other state-of-the-art tests under various settings and show good performance. We showcase the new approaches through two applications: the comparison of musks and non-musks using the shape of molecules, and the comparison of taxi trips starting from John F. Kennedy airport in consecutive months. All proposed methods are implemented in an R package kerTests.