Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

Objective The objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer. Methods Candidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as non-cancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of eight biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique. Results The final selected optimal combination of five biomarkers (CA125, HE4, cancer antigen 15-3, apolipoprotein [Apo] A1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation. Conclusion Combining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.
What does gender has to do with math? Complex questions require complex answers

Whether mathematics is a gendered domain or not is a long‐lasting issue bringing along major social and educational implications. The females' underrepresentation in science, technology, engineering, and mathematics (STEM) has been considered one of the key signs of the math gender gap, although the current view largely attributes the origin of this phenomenon to sociocultural factors. Indeed, recent approaches to math gender differences reached the universal conclusion that nature and nurture exert reciprocal effects on each other, establishing the need for approaching the study of the math gender issue only once its intrinsic complexity has been accepted. Building upon a flourishing literature, this review provides an updated synthesis of the evidence for math gender equality at the start, and for math gender inequality on the go, challenging the role of biological factors. In particular, by combining recent findings from different research areas, the paper discusses the persistence of the “math male myth” and the associated “female are not good at math myth,” drawing attention to the complex interplay of social and cultural forces that support such stereotypes. The suggestion is made that longevity of these myths results from the additive effects of two independent cognitive biases associated with gender stereotypes and with math stereotypes, respectively. Scholars' responsibility in amplifying these myths by pursuing some catching lines of research is also discussed.
Performance Evaluation in MANET by Using NCPR Scheme with Cluster Technique

MANET is the one of the type of special wireless network. In MANET there is no static topology due to high mobility of nodes, there exists frequent link breakages which leads to path failure and route discovery. Due to link breakage problem arises in data transmission that raise issue of routing overhead. This will also decrease Throughput and Packet Delivery Ratio. It provides the necessary network functionality in the absence of fixed infrastructure. To overcome this problem in this paper propose a solution to reduce routing overhead and communication overhead and improving routing performance by using Clustering Technique. The NCPR protocol used to find Uncovered Nodes in the network. The problem in NCPR is nodes get same RREQ packet again and again. We propose solution, provide Cluster Technique which builds a stable cluster in NCPR to reduce routing overhead and improve routing performance. The Cluster having Distributed and Reactive nature we can improve efficiency of routing, packet delivery ratio and throughput. This will also reduce End-to-End delay in network.
Evaluation of sulfonated oxidized chitosan antifungal activity against Fusarium graminearum

Abstract Chitosan biomaterials are widely used in the biological area because of their broad-spectrum antibacterial activity. However, chitosan cannot be dissolved in a neutral solution, limiting its application in various fields seriously. In this study, water-soluble sulfonated oxidized chitosan (SOCS) with antifungal activity were prepared by oxidization and sulfonation. Its structure was clearly confirmed by spectroscopy data (FTIR, 1H NMR, 13C NMR) and elemental analysis. SEM images of OCS and SOCS revealed that there was a little curly and an irregular sheet-like morphologies on them which was attributed to the oxidation and sulfonation on CS. Moreover, the FTIR and NMR indicated that –OH on the CS was oxidized into -COOH on the OCS and -SO3H groups on the SOCS. The EDS results of OCS and SOCS confirmed the presence of the oxygen element in OCS and the S element in SOCS. All studies confirmed the OCS and SOCS were synthesized successfully. Furthermore, the inhibitory activity of SOCS biocomposites against plant pathogenic fungi, (Fusarium graminearum), was investigated. The results showed that the SOCS have significant inhibitory effects on the mycelial growth of F. graminearum. The EC50 value of SOCS against F. graminearum is 79.46 μg/mL. The research results presented above indicated that SOCS can be used as a candidate material for the control of plant pathogenic fungi, and can broaden the application of chitosan materials in plant protection and sustainable agriculture. Research highlights SOCS showed better solubility in deionized water. The antifungal effect of SOCS dissolved in acetic acid was higher than that of CS dissolved in acetic acid. SOCS dissolved in water can cause an inhibitory effect on F. graminearum at lower concentrations.
Does psychological process change during psychotherapy predict long-term depression outcome after successful cognitive therapy or interpersonal psychotherapy? Secondary analysis of a randomized trial

Abstract Objective Psychotherapies for depression are similarly effective, but the processes through which these therapies work have not been identified. We focus on psychological process changes during therapy as predictors of long-term depression outcome in treatment responders. Method: Secondary analysis of a randomized trial comparing cognitive therapy (CT) and interpersonal psychotherapy (IPT) that focuses on 85 treatment responders. Using mixed-effects models, changes during therapy (0–7 months) on nine process variables were associated with depression severity (BDI-II) at follow-up (7–24 months). Results: A decrease in dysfunctional attitudes was associated with a decrease in depression scores over time. Improved self-esteem was associated with less depression at follow-up (borderline significant). More improvement in both work and social functioning and interpersonal problems was associated with better depression outcomes in IPT relative to CT, while less improvement in work and social functioning and interpersonal problems was associated with better outcomes in CT relative to IPT. Conclusions: Less negative thinking during therapy is associated with lower depression severity in time, while changes during therapy in work and social functioning and interpersonal problems appear to predict different long-term outcomes in CT vs. IPT. If replicated, these findings can be used to guide clinical decision-making during psychotherapy.
Facial attractiveness is more associated with individual warmth than with competence: Behavioral and neural evidence

ABSTRACT Individuals appear to infer others’ psychological characteristics according to facial attractiveness and these psychological characteristics can be classified into two categories in social cognition, that is, warmth and competence. However, which category of psychological characteristic is more associated with face attractiveness and its neural mechanisms have not been explored. To address this, participants were asked to judge others’ warmth and competence traits based on face attractiveness, while their brains were scanned using functional magnetic resonance imaging (fMRI). They also assessed the attractiveness of faces after scanning. Behavioral results showed that the correlation between face attractiveness and warmth ratings was significantly higher than that with competence ratings. fMRI results demonstrated that the dorsomedial prefrontal cortex (dmPFC), temporoparietal junction (TPJ), lateral prefrontal cortex, and lateral temporal lobe were more involved in the warmth task. Moreover, attractiveness ratings were negatively correlated with activation of the dmPFC and TPJ only in the warmth task. Furthermore, the attractiveness ratings were negatively correlated with the defined dmPFC, region related to attractiveness judgment, only in the warmth task. In conclusion, people are more inclined to infer others’ warmth than competence characteristics from face attractiveness, that is, face attractiveness is more associated with warmth than with competence.
Sex-Related Differences in Transcatheter Mitral Valve Repair: A Systematic Review and Meta-Analysis

Objective: Inequalities in postoperative outcomes between males and females are well described with females often experiencing inferior outcomes after heart valve surgery. The recent literature has demonstrated equivalent or improved outcomes for females after transcatheter aortic valve replacement. Transcatheter mitral valve repair (TMVr) and replacement (TMVR) is a relatively newer field with significantly less literature comparing sex differences. This systematic review and meta-analysis looks to provide a comprehensive summary of the published literature comparing outcomes between males and females undergoing transcatheter MV interventions. Methods: PubMed, MEDLINE, and Scopus were systematically searched for all studies comparing outcomes between males and females undergoing TMVr and TMVR. A total of 2,178 English manuscript titles and abstracts were reviewed. Articles were excluded if data were not provided regarding sex differences, transcatheter MV intervention, full-length text was not accessible, or if insufficient data was provided. A total of 2,170 articles were excluded, and 8 articles were included in this study. Results: Pooled estimates of outcomes demonstrated rates of acute kidney injury (OR 1.28 [95% CI, 1.14–1.44; p < 0.0001]) favored females, while rates of major bleeding favored males (OR 0.85 [95% CI 0.76–0.96; p = 0.01]). Rates of mortality, postoperative MI, and stroke did not differ significantly. Conclusion: A trend has emerged in heart valve interventions with males tending to have improved outcomes after surgical intervention and females experiencing equivalent or improved outcomes after transcatheter interventions. This meta-analysis identified increased rates of acute kidney injury for males, increased rates of major bleeding for females, and otherwise comparable morbidity and mortality in males and females undergoing TMVr.
Systems Architecting for a Retrofittable Subsea System Application

This paper highlights a model‐based systems engineering (MBSE) approach tailored to the unique complexities of developing a consistent functional and physical architecture for retrofittable subsea equipment used in offshore deepwater operations. A new architecture in this application must satisfy demanding requirements in safety, reliability, availability, maintainability, retrofittability, deployment timelines, and interface compatibility. Additionally, the new architecture must be defendable against the industry's decades‐long established regulatory, cultural and technological legacies. To objectively design within these constraints, a system model was developed iteratively using both bottom‐up and top‐down analysis to define a complete functional and physical architecture. Inconsistencies between the functional and physical models encountered are resolved through detailed functional analysis and product realization process. The hybrid bottom‐up, top‐down approach employed, supports continuous stakeholder validation and requirements verification. The tailored MBSE approach and conceptual functional and physical models are presented.
and strong

of a (RSG) peri-odically the subgradient (SG). We show that, when applied to a broad class of convex optimization problems, the RSG method can ﬁnd an ϵ -optimal solution with a lower complexity than the SG method. In particular, we ﬁrst show that RSG can reduce the dependence of SG’s iteration complexity on the distance between the initial solution and the optimal set to that between the ϵ -level set and the optimal set multiplied by a logarithmic factor. Moreover, we show the advantages of RSG over SG in solving a broad family of problems that satisfy a local error bound condition, and also demonstrate its advantages for three speciﬁc families of convex optimization problems with diﬀerent power constants in the local error bound condition. (a) For the problems whose epigraph is a polyhedron, RSG is shown to converge linearly. (b) For the problems with local quadratic growth property in the ϵ -sublevel set, RSG has an O ( 1 ϵ log( 1 ϵ )) iteration complexity. (c) For the problems that admit a local Kurdyka-Łojasiewicz property with a power constant of β ∈ [0 , 1) , RSG has an O ( 1 ϵ 2 β log( 1 ϵ )) iteration complexity. The novelty of our analysis lies at exploiting the lower bound of the ﬁrst-order optimality residual at the ϵ -level set. (e.g., local quadratic property, local Kurdyka-Łojasiewicz convergence local bound We demonstrate the eﬀectiveness of the proposed algorithms on several machine learning tasks including regression, classiﬁcation and matrix completion.
A comparison of critical shear force in low-voltage, all-polymer electroadhesives to a basic friction model

Elastomer-based electroadhesion can be an effective method to provide tunable adhesion between robots and grasped objects or surfaces. However, there has been little work to develop models of electroadhesion and characterization of adhesive performance relative to these models. In this paper, a basic friction model is proposed to describe the critical shear force for a single electrode electroadhesive fabricated from conductive PDMS encased in parylene. The use of parylene results in thin dielectrics that require only tens of Volts to achieve shear pressures greater than 100 kPa. The experimental results gathered by characterizing voltage, dielectric thickness, adhesive area, and adhesive thickness follow the trends predicted by theory with some important deviations that are studied using high speed video capture of the soft adhesive failure.
A Graphical Workflow Exploration Environment For Visual Analytics

Graphical history mechanisms have been widely utilized in many domains to support humans’ limited working memory, error recovery, collaboration, and presentation in visual analysis. Yet, there are aspects that remain under-explored in designing graphical history systems for visual analytics systems to help analysts who have complicated workflows. In this paper we report on our design study performed with domain experts, where we characterize domain tasks and designed a visual graphical workflow management environment. Our environment allows analysts to efficiently review, edit, navigate, and explore their complex workflows with their colleagues. In order to evaluate the environment, we present a case study and user study. In the case study, we explore how two domain experts perform collaborative review, communication, and training with our environment; while in the user study with the car data, we reveal that how our environment helps users and how the history mechanism affects users’ visual problem-solving behaviors.
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation

Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages of estimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2) We leverage the uncertainty of the estimated depth map to improve accuracy and robustness of the output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.
Sonic Interactions in Virtual Environments: the Egocentric Audio Perspective of the Digital Twin

The relationships between the listener, physical world and virtual environment (VE) should not only inspire the design of natural multimodal interfaces but should be discovered to make sense of the mediating action of VR technologies. This chapter aims to transform an archipelago of studies related to sonic interactions in virtual environments (SIVE) into a research field equipped with a first theoretical framework with an inclusive vision of the challenges to come: the egocentric perspective of the auditory digital twin. In a VE with immersive audio technologies implemented, the role of VR simulations must be enacted by a participatory exploration of sense-making in a network of human and non-human agents, called actors. The guardian of such locus of agency is the auditory digital twin that fosters intra-actions between humans and technology, dynamically and fluidly redefining all those configurations that are crucial for an immersive and coherent experience. The idea of entanglement theory is here mainly declined in an egocentric-spatial perspective related to emerging knowledge of the listener’s perceptual capabilities. This is an actively transformative relation with the digital twin potentials to create movement, transparency, and provocative activities in VEs. The chapter will contain an original theoretical perspective complemented by several bibliographical references and links to the other book chapters that have contributed significantly to the proposal presented here.
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla

Many areas, such as the biological and healthcare domain, artistic works, and organization names, have nested, overlapping, discontinuous entity mentions that may even be syntactically or semantically ambiguous in practice. Traditional sequence tagging algorithms are unable to recognize these complex mentions because they may violate the assumptions upon which sequence tagging schemes are founded. In this paper, we describe our contribution to SemEval 2022 Task 11 on identifying such complex Named Entities. We have leveraged the ensemble of multiple ELECTRA-based models that were exclusively pretrained on the Bangla language with the performance of ELECTRA-based models pretrained on English to achieve competitive performance on the Track-11. Besides providing a system description, we will also present the outcomes of our experiments on architectural decisions, dataset augmentations, and post-competition findings.
Power structures of directed spaces

Powerdomains in domain theory plays an important role in modeling the semantics of nondeterministic functional programming languages. In this paper, we extend the notion of powerdomain to the category of directed spaces, which is equivalent to the notion of the T 0 monotone-determined space [4]. We deﬁne the notion of upper, lower and convex powerspace of a directed space by the way of free algebras. We show that the upper, lower and convex powerspace over any directed space exist and give their concrete structures. Generally, the upper, lower and convex powerspaces of a directed spaces are diﬀerent from the upper, lower and convex powerdomains of a dcpos endowed with the Scott topology and the observationally-induced upper and lower powerspaces introduced by Battenfeld and Sch¨oder in 2015.
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues

We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images of multiple object categories without viewpoint annotation, forcing the model to learn across multiple object categories without 3D supervision. To facilitate learning with such minimal supervision, we use category labels to guide shape learning with a novel categorical metric learning approach. We also utilize adversarial and viewpoint regularization techniques to further disentangle the effects of viewpoint and shape. We obtain the first results for large-scale (more than 50 categories) single-viewpoint shape prediction using a single model without any 3D cues. We are also the first to examine and quantify the benefit of class information in single-view supervised 3D shape reconstruction. Our method achieves superior performance over state-of-the-art methods on ShapeNet-13, ShapeNet-55 and Pascal3D+.
Bures-Wasserstein Minimizing Geodesics between Covariance Matrices of Different Ranks

The set of covariance matrices equipped with the Bures-Wasserstein distance is the orbit space of the smooth, proper and isometric action of the orthogonal group on the Euclidean space of square matrices. This construction induces a natural orbit stratification on covariance matrices, which is exactly the stratification by the rank. Thus, the strata are the manifolds of symmetric positive semi-definite (PSD) matrices of fixed rank endowed with the Bures-Wasserstein Riemannian metric. In this work, we study the geodesics of the Bures-Wasserstein distance. Firstly, we complete the literature on geodesics in each stratum by clarifying the set of preimages of the exponential map and by specifying the injection domain. We also give explicit formulae of the horizontal lift, the exponential map and the Riemannian logarithms that were kept implicit in previous works. Secondly, we give the expression of all the minimizing geodesic segments joining two covariance matrices of any rank. More precisely, we show that the set of all minimizing geodesics between two covariance matrices $\Sigma$ and $\Lambda$ is parametrized by the closed unit ball of $\mathbb{R}^{(k-r)\times(l-r)}$ for the spectral norm, where $k, l, r$ are the respective ranks of $\Sigma$, $\Lambda$, $\Sigma$$\Lambda$. In particular, the minimizing geodesic is unique if and only if $r = \min(k, l)$. Otherwise, there are infinitely many.