Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple-Instance Ordinal Regression

Ordinal regression (OR) is a paradigm in supervised learning, which aims at learning a prediction model for ordered classes. The existing studies mainly focus on single-instance OR, and the multi-instance OR problem has not been explicitly addressed. In many real-world applications, considering the OR problem from a multiple-instance aspect can yield better classification performance than from a single-instance aspect. For example, in image retrieval, an image may contain multiple and possibly heterogeneous objects. The user is usually interested in only a small part of the objects. If we represent the whole image as a global feature vector, the useful information from the targeted objects that the user is of interest may be overridden by the noisy information from irrelevant objects. However, this problem fits in the multiple-instance setting well. Each image is considered as a bag, and each object region is treated as an instance. The image is considered as of the user interest if it contains at least one targeted object region. In this paper, we address the multi-instance OR where the OR classifier is learned on multiple-instance data, instead of single-instance data. To solve this problem, we present a novel multiple-instance ordinal regression (MIOR) method. In MIOR, a set of parallel hyperplanes is used to separate the classes, and the label ordering information is incorporated into learning the classifier by imputing the parallel hyperplanes with an order. Moreover, considering that a bag may contain instances not belonging to its class, for each bag, the instance which is nearest to the middle of the corresponding class is selected to learn the classifier. Compared with the existing single-instance OR work, MIOR is able to learn a more accurate OR classifier on multiple-instance data where only the bag label is available and the instance label is unknown. Extensive experiments show that MIOR outperforms the existing single-instance OR methods.
Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach

This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.
Cross-Modal Metric Learning for AUC Optimization

Cross-modal metric learning (CML) deals with learning distance functions for cross-modal data matching. The existing methods mostly focus on minimizing a loss defined on sample pairs. However, the numbers of intraclass and interclass sample pairs can be highly imbalanced in many applications, and this can lead to deteriorating or unsatisfactory performances. The area under the receiver operating characteristic curve (AUC) is a more meaningful performance measure for the imbalanced distribution problem. To tackle the problem as well as to make samples from different modalities directly comparable, a CML method is presented by directly maximizing AUC. The method can be further extended to focus on optimizing partial AUC (pAUC), which is the AUC between two specific false positive rates (FPRs). This is particularly useful in certain applications where only the performances assessed within predefined false positive ranges are critical. The proposed method is formulated as a log-determinant regularized semidefinite optimization problem. For efficient optimization, a minibatch proximal point algorithm is developed. The algorithm is experimentally verified stable with the size of sampled pairs that form a minibatch at each iteration. Several data sets have been used in evaluation, including three cross-modal data sets on face recognition under various scenarios and a single modal data set, the Labeled Faces in the Wild. Results demonstrate the effectiveness of the proposed methods and marked improvements over the existing methods. Specifically, pAUC-optimized CML proves to be more competitive for performance measures such as Rank-1 and verification rate at FPR = 0.1%.
Implementation of the North Carolina HIV Bridge Counseling Program to Facilitate Linkage and Reengagement in Care for Individuals Infected with HIV/AIDS

BACKGROUND Statewide interventions are critical to meeting the goals of the National HIV/AIDS Strategy in this country. In 2012, the North Carolina Division of Public Health developed the North Carolina State Bridge Counselor program to improve linkage to and reengagement in care for newly diagnosed persons and persons living with HIV who were out-of-care. METHODS We reviewed the planning process for the North Carolina State Bridge Counselor program, which involved a review of existing strengths-based counseling models for persons living with HIV, implementation of these models, and communication strategies with other providers. State bridge counselor responsibilities were delineated from the role of disease intervention specialists while retaining the fieldwork capability of disease intervention specialists to conduct outreach and provide services for persons living with HIV throughout the state. RESULTS Program implementation required extensive planning with stakeholders, incorporation of strengths-based counseling models, development of performance standards, and utilization of CAREWare, an HIV care software program to document referrals and data-sharing between state bridge counselors and clinics. By the end of 2014, state bridge counselor services were provided to approximately 60 of the 400 persons living with HIV (15%) who are diagnosed each quarter in North Carolina, with increasing utilization of the program. LIMITATIONS We assessed the development of this intervention specific to the North Carolina Division of Public Health, which may limit its generalizability. However, the State Bridge Counselor program was implemented in both urban and rural areas throughout the state, which increases its applicability to different public health programs throughout the country. CONCLUSION We demonstrated that a statewide State Bridge Counselor program for linkage and reengagement activities can be implemented by leveraging existing infrastructures, electronic medical records, HIV care networks, and fieldwork activities.
Removing physiological motion from intravital and clinical functional imaging data

Intravital microscopy can provide unique insights into the function of biological processes in a native context. However, physiological motion caused by peristalsis, respiration and the heartbeat can present a significant challenge, particularly for functional readouts such as fluorescence lifetime imaging (FLIM), which require longer acquisition times to obtain a quantitative readout. Here, we present and benchmark Galene, a versatile multi-platform software tool for image-based correction of sample motion blurring in both time resolved and conventional laser scanning fluorescence microscopy data in two and three dimensions. We show that Galene is able to resolve intravital FLIM-FRET images of intra-abdominal organs in murine models and NADH autofluorescence of human dermal tissue imaging subject to a wide range of physiological motions. Thus, Galene can enable FLIM imaging in situations where a stable imaging platform is not always possible and rescue previously discarded quantitative imaging data.
Prior-Free Respiratory Motion Estimation in Rotational Angiography

Rotational coronary angiography using C-arm angiography systems enables intra-procedural 3-D imaging that is considered beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery procedures due to challenges associated with substantial intra-scan respiratory and cardiac motion. While gating handles cardiac motion during reconstruction, respiratory motion requires compensation. State-of-the-art algorithms rely on 3-D / 2-D registration that requires an uncompensated reconstruction of sufficient quality. To overcome this limitation, we investigate two prior-free respiratory motion estimation methods based on the optimization of: 1) epipolar consistency conditions (ECCs) and 2) a task-based auto-focus measure (AFM). The methods assess redundancies in projection images or impose favorable properties of 3-D space, respectively, and are used to estimate the respiratory motion of the coronary arteries within rotational angiograms. We evaluate our algorithms on the publicly available CAVAREV benchmark and on clinical data. We quantify reductions in error due to respiratory motion compensation using a dedicated reconstruction domain metric. Moreover, we study the improvements in image quality when using an analytic and a novel temporal total variation regularized algebraic reconstruction algorithm. We observed substantial improvement in all figures of merit compared with the uncompensated case. Improvements in image quality presented as a reduction of double edges, blurring, and noise. Benefits of the proposed corrections were notable even in cases suffering little corruption from respiratory motion, translating to an improvement in the vessel sharpness of (6.08 ± 4.46)% and (14.7 ± 8.80)% when the ECC-based and the AFM-based compensation were applied. On the CAVAREV data, our motion compensation approach exhibits an improvement of (27.6 ± 7.5)% and (97.0 ± 17.7)% when the ECC and AFM were used, respectively. At the time of writing, our method based on AFM is leading the CAVAREV scoreboard. Both motion estimation strategies are purely image-based and accurately estimate the displacements of the coronary arteries due to respiration. While current evidence suggests the superior performance of AFM, future work will further investigate the use of ECC in the context of angiography as they solely rely on geometric calibration and projection-domain images.
On adaptive propensity score truncation in causal inference

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.
Decision-making about motherhood among women living with HIV in Canada: a negotiation of multidimensional risks

Abstract Little research in Canada has examined the perspectives of women living with HIV on decision-making across the stages of motherhood. In 2004–2005, semi-structured interviews were conducted with 42 African, Caucasian and Haitian HIV-positive women recruited in Montreal. All were or wished to be biological mothers. Transcripts underwent thematic analysis organised by three culturally informed models of motherhood described by the participants, which influenced decision-making and perceived risks. For women who saw motherhood as ‘self-fulfilment and completeness’, vertical HIV transmission was a primary concern. It threatened their identity as a ‘good mother’, which also meant adhering to antiretrovirals. For women who viewed motherhood as a ‘social realisation’ (all African or Haitian), fears of compromised fertility dominated. Not becoming pregnant threatened their social status and presumed health within their community. Antiretrovirals were abandoned after delivery, fearing they would reveal their HIV status. For women endorsing a ‘personal growth’ model of motherhood (all Caucasian), threats to personal health were paramount. Pregnancy meant purifying body and soul. Antiretrovirals, seen as pollutants, were stopped after delivery. These findings can inform current research and sensitise health providers to the complex biological, psychological, social and spiritual risks that HIV-positive women negotiate in motherhood-related decision-making, towards more patient-centred care.
Collaborative Deconvolutional Neural Networks for Joint Depth Estimation and Semantic Segmentation

Semantic segmentation and single-view depth estimation are two fundamental problems in computer vision. They exploit the semantic and geometric properties of images, respectively, and are thus complementary in scene understanding. In this paper, we propose a collaborative deconvolutional neural network (C-DCNN) to jointly model these two problems for mutual promotion. The C-DCNN consists of two DCNNs, of which each is for one task. The DCNNs provide a finer resolution reconstruction method and are pretrained with hierarchical supervision. The feature maps from these two DCNNs are integrated via a pointwise bilinear layer, which fuses the semantic and depth information and produces higher order features. Then, the integrated features are fed into two sibling classification layers to simultaneously learn for semantic segmentation and depth estimation. In this way, we combine the semantic and depth features in a unified deep network and jointly train them to benefit each other. Specifically, during network training, we process depth estimation as a classification problem where a soft mapping strategy is proposed to map the continuous depth values into discrete probability distributions and the cross entropy loss is used. Besides, a fully connected conditional random field is also used as postprocessing to further improve the performance of semantic segmentation, where the proximity relations of pixels on position, intensity, and depth are jointly considered. We evaluate our approach on two challenging benchmarks: NYU Depth V2 and SUN RGB-D. It is demonstrated that our approach effectively utilizes these two kinds of information and achieves state-of-the-art results on both the semantic segmentation and depth estimation tasks.
Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the gastrointestinal tract, including Crohn's Disease and ulcerative colitis. Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due to its non-specific symptoms and because an invasive colonoscopy is required for confirmation, which leads to poor growth in children and worse treatment outcomes. Feature selection algorithms are often applied to microbial communities to identify bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can improve the robustness of feature selection algorithms, which is defined as the variation of feature selector output caused by small changes to the dataset. In this work, we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.
Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

Estimating the 6-DoF pose of a camera from a single image relative to a 3D point-set is an important task for many computer vision applications. Perspective-n-point solvers are routinely used for camera pose estimation, but are contingent on the provision of good quality 2D–3D correspondences. However, finding cross-modality correspondences between 2D image points and a 3D point-set is non-trivial, particularly when only geometric information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers and many local optima are common for this problem, we instead propose a robust and globally-optimal inlier set maximisation approach that jointly estimates the optimal camera pose and correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds on the number of inliers and local optimisation is integrated to accelerate convergence. The algorithm outperforms existing approaches on challenging synthetic and real datasets, reliably finding the global optimum, with a GPU implementation greatly reducing runtime.
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets

We propose a multitask deep convolutional neural network, trained on multimodal data (clinical and dermoscopic images, and patient metadata), to classify the 7-point melanoma checklist criteria and perform skin lesion diagnosis. Our neural network is trained using several multitask loss functions, where each loss considers different combinations of the input modalities, which allows our model to be robust to missing data at inference time. Our final model classifies the 7-point checklist and skin condition diagnosis, produces multimodal feature vectors suitable for image retrieval, and localizes clinically discriminant regions. We benchmark our approach using 1011 lesion cases, and report comprehensive results over all 7-point criteria and diagnosis. We also make our dataset (images and metadata) publicly available online at http://derm.cs. sfu.ca.
Role of personality traits in reporting the development of adverse drug reactions: a prospective cohort study of the Estonian general population

Objective To examine the role of the Five Factor Model (FFM) personality traits in reporting the development of adverse drug reactions (ADRs) when controlling for sociodemographic variables and health status. Design Prospective cohort study. Setting The Estonian Biobank of the Estonian Genome Centre, University of Tartu. Participants 814 women and 543 men (mean age=47.9 years; SD=15.2) who after the initial enrolment in the Estonian Biobank were re-contacted for follow-up purposes about 5.3 years after the enrolment and for whom both self- and informant-reported personality data were available. Main outcome measure Participants who did not report having any ADRs at baseline but who reported ADRs at the follow-up about 5.3 years later versus participants who did not report any ADRs at either time point. The reports of developing ADRs were predicted from the FFM personality traits after statistically controlling for sociodemographic variables (age, gender and education), baseline indicators of health status (number of diagnoses and medicines taken, body mass index and blood pressure), and the change in health status between the two measurements. Results The results of a hierarchical binary logistic regression analysis showed that participants who reported the development of ADRs between the two measurements had higher levels of conscientiousness, were more likely to be women, were taking more medicines at baseline and had a higher increase in the number of medicines taken during the study period than participants who did not report any ADRs at either time point (all p values <0.05). Higher neuroticism (p=0.067) and a higher number of diagnosed diseases at baseline (p=0.053) also made marginal contributions to predicting the development of ADRs. Conclusions This study shows for the first time that higher levels of conscientiousness and neuroticism are associated with reporting the development of ADRs.
On fixpoint logics and equivalences for processes with restricted nondeterminism

In concurrency, processes can be studied using a partial order or an interleaving semantics. In partial order semantics, at least four different kinds of behaviour can be recognised: concurrency, causality, conflict, and confusion. In interleaving semantics, only conflicts can be observed. All these features can be characterised in logical terms, and various logics have been defined for this purpose. For instance, Hennessy– Milner logic is a modal language that captures strong bisimilarity, the standard bisimulation equivalence for processes with interleaving semantics. However, when considering processes with partial order semantics, stronger equivalences are used and more discriminating logics are needed. In the present paper, we study conditions to ease the definition of such logics and equivalences for processes with partial order semantics. More specifically, we study the impact that nondeterminism can have on some fixpoint modal logics and bisimulation equivalences for concurrent and multi-agent systems, when it is systematically restricted within the four kinds of behaviour mentioned above. Our results show that when the concurrency and confusion relations are taken to be deterministic, then the main equivalence for causal behaviour can be completely captured (even in logical and game-theoretic terms) by a simpler, weaker, more local bisimulation relation. We also provide key examples of the kinds of processes that can be modelled using deterministic confusion in order to illustrate the expressive power of the general framework defined here.
Support-Free Hollowing

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.