Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Complete Row Space Recovery for Multi-View Subspace Clustering

Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention. Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views. The affinity construction process is time-consuming, and the combined affinity matrix is not guaranteed to reflect the whole true subspace structure. To overcome these issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed. Concretely, LCRSR is based on the assumption that the multi-view observations are generated from an underlying latent representation, which is further assumed to collect the authentic samples drawn exactly from multiple subspaces. LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions. LCRSR does not involve the graph construction procedure and is solved with an efficient and convergent algorithm, thereby being more scalable to large-scale datasets. The effectiveness and efficiency of LCRSR are validated by clustering various kinds of multi-view data and illustrated in the background subtraction task.
Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data

Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital beds and trained sleep technicians. To assist in the diagnosis process, automated detection methods are being developed. Recent works have demonstrated that deep learning models can extract useful information from raw respiratory data and that such models can be used as a robust sleep apnea detector. However, trained sleep technicians take into account multiple sensor signals when annotating sleep recordings instead of relying on a single respiratory estimate. To improve the predictive performance and reliability of the models, early and late sensor fusion methods are explored in this work. In addition, a novel late sensor fusion method is proposed which uses backward shortcut connections to improve the learning of the first stages of the models. The performance of these fusion methods is analyzed using CNN as well as LSTM deep learning base-models. The results demonstrate a significant and consistent improvement in predictive performance over the single sensor methods and over the other explored sensor fusion methods, by using the proposed sensor fusion method with backward shortcut connections.
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning

This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimension. Specifically, we assume that the columns of a matrix are generated by polynomials acting on a low-dimensional intrinsic variable, and wish to recover the missing entries under this assumption. We show that we can identify the complete matrix of minimum intrinsic dimension by minimizing the rank of the matrix in a high dimensional feature space. We develop a new formulation of the resulting problem using the kernel trick together with a new relaxation of the rank objective, and propose an efficient optimization method. We also show how to use our methods to complete data drawn from multiple nonlinear manifolds. Comparative studies on synthetic data, subspace clustering with missing data, motion capture data recovery, and transductive learning verify the superiority of our methods over the state-of-the-art.
A Robust Fluid Closure for Phase Mixing in Gyrokinetic ITG/ETG Turbulence

We present a novel phase mixing closure for a simple turbulent system - ion/electron temperature gradient driven turbulence in an unsheared slab. The closure is motivated by the observation that in a turbulent system the nonlinearity continually perturbs the system away from the linear solution, thus demanding versatility in the closure scheme. The closure, called the dynamic multi-mode (DMM) closure, is constructed by, first, extracting an optimal basis from a nonlinear kinetic simulation using singular value decomposition (SVD). Subsequent nonlinear fluid simulations are projected onto a subset of this basis and the results are used to formulate the closure. This scheme is quite versatile, having the ability to dynamically adapt to the nonlinear state. We compare the closure with both simple truncation and the Hammett-Perkins (HP) closure throughout a broad range of the relevant 2D parameter space (collisionality and gradient drive). Simple truncation results in roughly 20%-50% errors in heat flux in comparison with the kinetic system. The HP closure performs quite well in its targeted - weakly collisional - parameter regime (errors generally less than 10%) and clearly outperforms simple truncation throughout the parameter space. We show that the DMM closure outperforms both HP and simple truncation throughout the parameter space, even in regions quite far removed from the training simulation. Applications beyond this simple system are envisioned and discussed.
Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real‐time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision‐based systems to avoid hazards as well as plan safe routes. However, vision‐based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision‐based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this study, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared with traditional fixed‐sensor configurations.
Semismooth Newton-type method for bilevel optimization: global convergence and extensive numerical experiments

We consider the standard optimistic bilevel optimization problem, in particular upper- and lower-level constraints can be coupled. By means of the lower-level value function, the problem is transformed into a single-level optimization problem with a penalization of the value function constraint. For treating the latter problem, we develop a framework that does not rely on the direct computation of the lower-level value function or its derivatives. For each penalty parameter, the framework leads to a semismooth system of equations. This allows us to extend the semismooth Newton method to bilevel optimization. Besides global convergence properties of the method, we focus on achieving local superlinear convergence to a solution of the semismooth system. To this end, we formulate an appropriate CD-regularity assumption and derive sufficient conditions so that it is fulfilled. Moreover, we develop conditions to guarantee that a solution of the semismooth system is a local solution of the bilevel optimization problem. Extensive numerical experiments on 124 examples of nonlinear bilevel optimization problems from the literature show that this approach exhibits a remarkable performance, where only a few penalty parameters need to be considered.
Strong ergodicity breaking due to local constraints in a quantum system

Quantum systems that violate the eigenstate thermalisation hypothesis thereby falling outside the paradigm of conventional statistical mechanics are of both intellectual and practical interest. We show that such a breaking of ergodicity may arise purely due to local constraints on random many-body Hamiltonians. As an example, we study an ergodic quantum spin-1/2 model which acquires a localised phase upon addition of East-type constraints. We establish its phenomenology using spectral and dynamical properties obtained by exact diagonalisation. Mapping the Hamiltonian to a disordered hopping problem on the Fock space graph we find that potentially non-resonant bottlenecks in the Fock-space dynamics, caused by spatially local segments of frozen spins, lie at the root of localisation. We support this picture by introducing and solving numerically a class of random matrix models that retain the bottlenecks. Finally, we obtain analytical insight into the origins of localisation using the forward-scattering approximation. A numerical treatment of the forward-scattering approximation yields critical points which agree quantitatively with the exact diagonalisation results.
Penalized-Likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding

Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors into PET image reconstruction, especially with the development of hybrid PET/CT and PET/MRI systems. In this work, we proposed a cube-based 3D structural convolutional sparse coding (CSC) concept for penalized-likelihood PET image reconstruction, named 3D PET-CSC. The proposed 3D PET-CSC takes advantage of the convolutional operation and manages to incorporate anatomical priors without the need of registration or supervised training. As 3D PET-CSC codes the whole 3D PET image, instead of patches, it alleviates the staircase artifacts commonly presented in traditional patch-based sparse coding methods. Compared with traditional coding methods in Fourier domain, the proposed method extends the 3D CSC to a straightforward approach based on the pursuit of localized cubes. Moreover, we developed the residual-image and order-subset mechanisms to further reduce the computational cost and accelerate the convergence for the proposed 3D PET-CSC method. Experiments based on computer simulations and clinical datasets demonstrate the superiority of 3D PET-CSC compared with other reference methods.
Psychiatric and cognitive correlates of quality of life among persons with primary brain tumors

Background: Quality of life (QoL) in primary brain tumour (PBT) is often the main outcome measure in an otherwise incurable disease. The impact of psychiatric, cognitive correlates on quality of life in primary brain tumours is less well studied. Aims and objectives: The primary objective was to find out the association of psychiatric morbidity, cognitive functions with quality of life in patients with primary brain tumours. The secondary objective was to study whether any association exists with tumour grading, laterality, location and psychiatric morbidity. Materials and Methods: 100 consecutive patients of PBT were screened in the Neuro-behavioural Clinic. Age, gender matched 52 healthy subjects were taken for comparison. Quality of life (qol) measure (EORTC), Hospital Anxiety Depression Scale (HADS), GHQ (12 item) and Mini Mental State Examination (MMSE) were administered. Results: 52 PBT cases were included, out of which 17.30% had Organic Anxiety Disorder (F06.4), 23.07% had Organic Mood disorder (F06.3%). Statistically significant association was found in EORTC qol scores and anxiety scores (p 0.001), depressive scores (p 0.029), psychiatric morbidity (p0.000) . Significant association with tumour laterality , depression scores (p0.041) was found. PBT patients had poor quality of life as compared to matched healthy volunteers (p <0.001). Significant negative correlation between EORTC B-20, cognitive scores using Spearman's Rho (p0.005; r - 0.385), implying more symptoms with poor cognitive function scores. Psychiatric morbidity, cognitive dysfunction, poor qol were noted, though no association with tumour grading, location. Conclusion: Regular assessments, early intervention will help in improving quality of life in PBT.
Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students

Aims: The study aims to assess the prevalence of smartphone addiction and its effects on sleep quality among medical students. Study Setting and Design: A cross-sectional study was carried out by convenience sampling of medical students at a tertiary care hospital in South India. Materials and Methods: Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision axis I disorders research version was used for screening past and current psychiatric illness. A semi-structured pro forma was used to obtain demographic details. Smartphone Addiction Scale-Short Version was used to assess smartphone addiction in the participants. Sleep quality was assessed using Pittsburgh's Sleep Quality Index (PSQI). Results: Among 150 medical students, 67 (44.7%) were addicted to smartphone usage. Despite the preponderance of male students (31 [50%]) being addicted, there was no statistically significant gender difference in smartphone addiction (P = 0.270). The PSQI revealed poor sleep quality in 77 (51.3%) which amounts to half of the participants. Smartphone addiction was found to be statistically significantly associated with poor sleep quality (odds ratio: 2.34 with P < 0.046). Conclusions: The prevalence of smartphone addiction among younger population is higher compared to those of contemporary studies. No gender difference in smartphone addiction could be made out in the current study. Smartphone addiction was found to be associated with poor sleep quality. The findings support screening for smartphone addiction which will be helpful in early identification and prompt management.
Anesthetic Efficacy of Single Buccal Infiltration of 4% Articaine and 2% Lignocaine in Extraction of Maxillary 1st Molar

Background: The extraction of tooth being the most common procedure in oral surgery should be pain free with limited dosage and limited needlepricks. Articaine being unique among amide local anesthetics contains a thiophene group, which increases its liposolubility, and an ester group which helps biotransformation in plasma. Because of the high diffusion properties, it can be used as a single buccal infiltration to extract a maxillary tooth. Aim and Objective: Objective of the study was to compare the efficacy of single buccal infiltration of 4% articaine with that of 2% lignocaine for maxillary first molar extraction. Methodology: A triple blind randomized controlled study was carried on 100 patients of age group 18-60 years who required maxillary first molar extraction, visiting the Department of Oral and Maxillofacial surgery. They were included in the study after obtaining informed consent. Buccal infiltration of 1.8 ml of anesthetic solution was given randomly to 100 patients with appropriate blinding of the cartridges. Objective signs were checked. If any additional injection was given, it was noted as type and number of rescue injection given. Postoperatively VAS score and surgeon's quality of anesthesia was noted. Duration of anesthesia was measured every 5 minutes for 50 minutes from infiltration. Results: Out of 50 patients in group A (Articaine), in 44 patients extraction was done without the need of additional injection whereas in group B(Lignocaine), 29 patients require additional infiltration on the palatal side. The VAS score values for group A were also significantly less in comparison with group B. The mean duration of anesthesia for Group A being (71.70 ± 17.82 min) in 44 patients who only received buccal infiltration. Interpretation and Conclusion: The efficacy of single buccal injection of articaine is comparable to buccal and palatal injection of lignocaine.
Psychopathology and personality factors in first-degree relatives of patients with schizophrenia

Background: Schizophrenia is a chronic mental illness which has far-reaching consequences not only on patients but also on relatives. These relatives are at an increased risk for developing schizophrenia, depression, and substance dependence. Psychopathology may be related to personality factors. This study was planned to assess psychopathology and personality factors in first degree relatives of patients with schizophrenia and to evaluate any relation between them. Materials and Methods: This was a cross-sectional study conducted at psychiatry out- and in-patient clinic at a tertiary care teaching hospital. One hundred and fifty patients who were first-degree relatives of patients diagnosed with schizophrenia on the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition were enrolled in this study. Psychopathology was assessed on “Symptom Checklist 90-Revised. ” “Big Five Inventory” (BFI) was used to study the personality factors. The two variables were compared to look for any relation between them. Results: Eighty-two of 150 (54.67%) first-degree relatives were diagnosed to have significant psychopathology. Among them, 42 (i.e., 58.5%) had either somatization or depression. Those relatives who had psychopathology scored higher on neuroticism (P = 3.51E-04) and lower on agreeableness (P = 0.029) domains of BFI. Conclusion: Relatives of patients with schizophrenia should be screened for psychopathology regularly, also relation between personality and psychopathology needs to be explored further.
Experiences of choledochal cyst in pediatric and adult population: A case series

Introduction: Choledochal cysts are a relatively uncommon disease of the bile duct which is the cystic dilatation of the intra- or extrahepatic bile ducts. Literature is scarce on its clinicopathological behavior among children and adults and its comparison. Methods: The retrospective study was aimed to study the clinical characteristics, management, and complications between pediatric and adult patients with choledochal cysts. Results: There was higher female preponderance with male to female ratio of 3:17 in pediatric age group while 1:4 in adult age. Abdominal pain was more common among adults, while palpable mass was more common among pediatrics population (P < 0.05). Jaundice was more evident in the pediatric age group, yet the classic triad of choledochal cyst (abdominal pain, jaundice, and a palpable mass) was not observed in any age group. About 76% of the cysts were type 1 cysts, which was more common among pediatric age group (40% vs. 90% P < 0.05) while with adults presented more with a type IVA cyst (60% vs. 10% P > 0.05). No patients with type II, type III, type IVB cysts, or type V were found. Sixteen patients underwent Lilly technique, with resection of the choledochal mucosa and Roux-en-Y hepaticojejunostomy, while nine patients underwent resection of the choledochal cyst and Roux-en-Y hepaticojejunostomy. Patients who received total excision had fewer surgical complications in both groups. Conclusion: Although there is a significant difference in the clinical characteristics of choledochal cysts between children and adults, yet early detection and necessary surgery is essential for patients with choledochal cysts.
Exploring Complicity of Cervical Cancer Screening in Malawi: The Interplay of Behavioral, Cultural, and Societal Influences

Objective: Cervical cancer is preventable, and early diagnosis is possible using low-cost technologies, but a scant number of women receive cancer screening in Malawi. This study aims to identify facilitators and barriers that influence the uptakes of cervical cancer screening behavior in Malawi. Methods: A rapid ethnographic approach with the goal of optimizing planning for a future intervention study was utilized. Data were collected from three focus groups and seven individual interviews with adults in their communities, stakeholders, and health-care providers. Results: Three categories (sociocultural influences, access to the health-care system, and individual factors) have emerged as facilitators or barriers to cervical cancer screening among Malawian women. The findings also showed that cervical cancer screening behavior is situated socially through cultural and health-care services of a given community. Conclusions: Cancer screenings are only sought when illness symptoms persist or worsen. Awareness and knowledge of cervical cancer and cervical cancer screening is low among both health-care providers and the general population. Health-care systems are donor driven and focus on a single disease, health-care access is the greatest challenge to cervical cancer screening, and health-care providers are not adequately prepared to work for rapid increase in the prevalence of cervical cancer. Integrating cervical cancer screening into the existing health-care system is sustainable way forward, and nurses prepared to handle cervical cancer management can play an essential role to promote cervical cancer screening in a health resource-constrained setting.
A comparative study of psychosocial determinants and mental well-being in chronic kidney disease patients: A closer look

Background: Depressive illness in chronic kidney disease (CKD) is an independent risk factor for morbidity and mortality. The relation between depressive illness and quality of life (QoL) in this vulnerable group is complex. We attempted to study the burden of depressive illness, the QoL in patients of CKD on hemodialysis (HD), and peritoneal dialysis (PD) as well as those who were not on any dialysis but on conservative medical management only. Materials and Methods: Observational study with cross-sectional analytical controlled design. Statistical Methods Used: Chi-square statistic or Fisher's exact test for categorical variables and t-test and ANOVA for continuous variables. Correlational analysis was done using Spearman's correlation coefficient. P <0.05 was considered as statistically significant. Results: Depressive symptoms were present significantly across all 3 groups of CKD patients. Depressive disorder was significantly higher in the HD group. Mean QoL was significantly better in patients of CKD on PD. Discussion: There is huge hidden burden of depressive symptoms and depressive illness in patients of CKD whether on dialysis or on conservative medical management. The study found significantly higher burden of depressive illness and lower QoL among the HD vis a vis PD patients, even though the majority of our CKD who require dialysis are on HD. Conclusion: Depressive burden is the hidden factor behind poor QoL, poor overall satisfaction as well as treatment outcome in patients of CKD whether or not on dialysis. To address this hidden depressive burden comprehensively, close cooperation between nephrologist and psychiatrist is called for.