Miracle-dz/UniFAR-FTUs huggingface dataset:
Nemchinov-Dyson Solutions of the Two-Dimensional Axisymmetric Inviscid Compressible Flow Equations

We investigate the $2$D inviscid compressible flow equations in axisymmetric coordinates, constrained by an ideal gas equation of state (EOS). Beginning with the assumption that the $2$D velocity field is space-time separable and linearly variable in each corresponding spatial coordinate, we proceed to derive an infinite family of elliptic or hyperbolic, uniformly expanding or contracting "gas cloud" solutions. Construction of specific example solutions belonging to this family is dependent on the solution of a system of nonlinear, coupled, second-order ordinary differential equations, and the prescription of an additional physical process of interest (e.g., uniform temperature or uniform entropy flow). The physical and computational implications of these solutions as pertaining to quantitative code verification or model qualification studies are discussed in some detail.
Lateral diffusion on a frozen random surface

The lateral diffusion coefficient of a Brownian particle on a two-dimensional random surface is studied in the quenched limit for which the surface configuration is time independent. We start with the stochastic equation of motion for a Brownian particle on a fluctuating surface, which has been derived by Naji and Brown. The mean square displacement of the particle projected on a base plane is calculated exactly under the condition that the surface with a constant shape has no spatial correlation. We prove that the obtained lateral diffusion coefficient is in between the known upper and lower bounds.
Ability of Head-Mounted Display Technology to Improve Mobility in People With Low Vision: A Systematic Review

Purpose The purpose of this study was to undertake a systematic literature review on how vision enhancements, implemented using head-mounted displays (HMDs), can improve mobility, orientation, and associated aspects of visual function in people with low vision. Methods The databases Medline, Chinl, Scopus, and Web of Science were searched for potentially relevant studies. Publications from all years until November 2018 were identified based on predefined inclusion and exclusion criteria. The data were tabulated and synthesized to produce a systematic review. Results The search identified 28 relevant papers describing the performance of vision enhancement techniques on mobility and associated visual tasks. Simplifying visual scenes improved obstacle detection and object recognition but decreased walking speed. Minification techniques increased the size of the visual field by 3 to 5 times and improved visual search performance. However, the impact of minification on mobility has not been studied extensively. Clinical trials with commercially available devices recorded poor results relative to conventional aids. Conclusions The effects of current vision enhancements using HMDs are mixed. They appear to reduce mobility efficiency but improved obstacle detection and object recognition. The review highlights the lack of controlled studies with robust study designs. To support the evidence base, well-designed trials with larger sample sizes that represent different types of impairments and real-life scenarios are required. Future work should focus on identifying the needs of people with different types of vision impairment and providing targeted enhancements. Translational Relevance This literature review examines the evidence regarding the ability of HMD technology to improve mobility in people with sight loss.
Laparoscopic Hiatal Hernia Repair and Roux-en-Y Conversion for Refractory Duodenogastroesophageal Reflux after Billroth I Distal Gastrectomy

Distal gastrectomy with Billroth I or II reconstruction may cause duodenogastroesophageal reflux (DGER), thereby resulting in digestive or respiratory symptoms. The mainstay of treatment is medication with proton pump inhibitors. However, these drugs may have limited effects in DGER. Laparoscopic fundoplication has been proven to be highly effective in treating gastroesophageal reflux disease (GERD), but it cannot be performed optimally for GERD that develops after gastrectomy. We report the case of a 72-year-old man with a history of distal gastrectomy and Billroth I anastomosis due to early gastric cancer. GERD due to bile reflux occurred after surgery and was refractory to medical therapy. The patient underwent Roux-en-Y conversion from Billroth I gastroduodenostomy and hiatal hernia repair with only cruroplasty. Fundoplication was not performed. His symptoms improved significantly after the surgery. Therefore, laparoscopic hiatal hernia repair and Roux-en-Y conversion can be an effective surgical procedure to treat medically refractory DGER after Billroth I gastrectomy.
Mitochondrial Fatty Acid Oxidation Disorders: Laboratory Diagnosis, Pathogenesis, and the Complicated Route to Treatment

Mitochondrial fatty acid (FA) oxidation deficiencies represent a genetically heterogeneous group of diseases in humans caused by defects in mitochondrial FA beta-oxidation (mFAO). A general characteristic of all mFAO disorders is hypoketotic hypoglycemia resulting from the enhanced reliance on glucose oxidation and the inability to synthesize ketone bodies from FAs. Patients with a defect in the oxidation of long-chain FAs are at risk to develop cardiac and skeletal muscle abnormalities including cardiomyopathy and arrhythmias, which may progress into early death, as well as rhabdomyolysis and exercise intolerance. The diagnosis of mFAO-deficient patients has greatly been helped by revolutionary developments in the field of tandem mass spectrometry (MS) for the analysis of acylcarnitines in blood and/or urine of candidate patients. Indeed, acylcarnitines have turned out to be excellent biomarkers; not only do they provide information whether a certain patient is affected by a mFAO deficiency, but the acylcarnitine profile itself usually immediately points to which enzyme is likely deficient. Another important aspect of acylcarnitine analysis by tandem MS is that this technique allows high-throughput analysis, which explains why screening for mFAO deficiencies has now been introduced in many newborn screening programs worldwide. In this review, we will describe the current state of knowledge about mFAO deficiencies, with particular emphasis on recent developments in the area of pathophysiology and treatment.
Application of Chromosome Conformation Capture Method for Detection MYC/TRD Chromosomal Translocation in Leukemia Cell Line

Background: Chromosomal breakpoints are the most common cause of hereditary diseases and cancers. Today, many standard clinical methods such as cytogenetic and PCR based techniques are used which have limitation regarding detection resolution. Chromosome conformation capture is a method for detecting gene proximity and chromosomal rearrangements. Materials and Methods: In this study, SKW3 cell line was used for detecting t(8;14)(q24;q11) using a 3C-based technique. SKW3 cell line was used for 3C library preparation. For Inverse PCR, two regions were selected in upstream and downstream of the viewpoint locus on chromosome 8-MYC gene based on EcoRI restriction sites. The captured sequence with intra-chromosomal interaction between chr8-c-MYC and chr14-TRD was selected for the translocation PCR primer design. Results: The DNA fragment captured in 3C PCR showed a specific TRD sequence translocated downstream of the MYC gene. Translocation PCR demonstrated the existence of (8; 14) (q24; q11) MYC /TRD in both library and genomic DNA. Conclusion: This result demonstrated 3C- based method could be used as a useful low-cost easy operating technique in chromosomal rearrangements detection. In this study, the integration of whole genome library monitoring and PCR method was used as a high- through put method in chromosomal breakpoints detection.
Identifying Breast Cancer Care Quality Measures for a Cancer Facility in Rural Sub-Saharan Africa: Results of a Systematic Literature Review and Modified Delphi Process

PURPOSE The burden of cancer is growing in low- and middle-income countries (LMICs), including sub-Saharan Africa. Ensuring the delivery of high-quality cancer care in such regions is a pressing concern. There is a need for strategies to identify meaningful and relevant quality measures that are applicable to and usable for quality measurement and improvement in resource-constrained settings. METHODS To identify quality measures for breast cancer care at Butaro Cancer Center of Excellence (BCCOE) in Rwanda, we used a modified Delphi process engaging two panels of experts, one with expertise in breast cancer evidence and measures used in high-income countries and one with expertise in cancer care delivery in Rwanda. RESULTS Our systematic review of the literature yielded no publications describing breast cancer quality measures developed in a low-income country, but it did provide 40 quality measures, which we adapted for relevance to our setting. After two surveys, one conference call, and one in-person meeting, 17 measures were identified as relevant to pathology, staging and treatment planning, surgery, chemotherapy, endocrine therapy, palliative care, and retention in care. Successes of the process included participation by a diverse set of global experts and engagement of the BCCOE community in quality measurement and improvement. Anticipated challenges include the need to continually refine these measures as resources, protocols, and measurement capacity rapidly evolve in Rwanda. CONCLUSION A modified Delphi process engaging both global and local expertise was a promising strategy to identify quality measures for breast cancer in Rwanda. The process and resulting measures may also be relevant for other LMIC cancer facilities. Next steps include validation of these measures in a retrospective cohort of patients with breast cancer.
Relationships between Emotional Schemas, Mindfulness, Self-Compassion and Unconditional Self-Acceptance on the Regulation of Psychological Needs

Emotional schemas are pervasive mental structures associated with a wide array of psychological symptoms, while mindfulness, self-compassion, and self-acceptance are viewed as adaptive psychological constructs. Psychological needs may be described as the cornerstone of mental health and well-being. However, a study of the relationships between emotional schemas, mindfulness, self-compassion, and self-acceptance with psychological needs was not performed. For this purpose, 250 subjects (M=20.67, SD=4.88, Male=33, Female=217), were evaluated through self-report questionnaires, in a cross-sectional design. Negative correlations were found between emotional schemas, mindfulness, self-compassion, unconditional self-acceptance, and psychological needs. Symptomatology was positively correlated with emotional schemas. Mindfulness, self-compassion, and unconditional self-acceptance predicted the regulation of psychological needs and mediated the relationship between emotional schemas and psychological needs. Emotional schemas may be associated with a tendency for experiential avoidance of internal reality, self-rejection/shame and self-criticism which may impair the regulation of psychological needs. These variables may be targets of integrative case conceptualization and clinical decision making focused on patient’s timings, styles of communication and needs.
Association of Sensory Impairments With Cognitive Decline and Depression Among Older Adults in China

Key Points Question Are visual and/or hearing impairments associated with cognitive decline and depression? Findings In this cross-sectional study of 18 038 participants aged 45 years or older in China, visual impairment was associated with worse performance in episodic memory and global cognition as well as with worse depression symptoms. Hearing impairment was also associated with poorer performance in episodic memory, mental intactness, and global cognition. Meaning In this study, visual and/or hearing impairments were associated with higher risks of cognitive decline and depression among middle-aged and older adults in China.
Viable Threat on News Reading: Generating Biased News Using Natural Language Models

Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader’s behavior and influence their bias. In this paper, we use a threat model to demonstrate that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.
Neighbourhood Distillation: On the benefits of non end-to-end distillation

End-to-end training with back propagation is the standard method for training deep neural networks. However, as networks become deeper and bigger, end-to-end training becomes more challenging: highly non-convex models gets stuck easily in local optima, gradients signals are prone to vanish or explode during back-propagation, training requires computational resources and time. In this work, we propose to break away from the end-to-end paradigm in the context of Knowledge Distillation. Instead of distilling a model end-to-end, we propose to split it into smaller sub-networks - also called neighbourhoods - that are then trained independently. We empirically show that distilling networks in a non end-to-end fashion can be beneficial in a diverse range of use cases. First, we show that it speeds up Knowledge Distillation by exploiting parallelism and training on smaller networks. Second, we show that independently distilled neighbourhoods may be efficiently re-used for Neural Architecture Search. Finally, because smaller networks model simpler functions, we show that they are easier to train with synthetic data than their deeper counterparts.
Right and left, partisanship predicts vulnerability to misinformation

We analyze the relationship between partisanship, echo chambers, and vulnerability to online misinformation by studying news sharing behavior on Twitter. While our results confirm prior findings that online misinformation sharing is strongly correlated with right-leaning partisanship, we also uncover a similar, though weaker trend among left-leaning users. Because of the correlation between a user's partisanship and their position within a partisan echo chamber, these types of influence are confounded. To disentangle their effects, we perform a regression analysis and find that vulnerability to misinformation is most strongly influenced by partisanship for both left- and right-leaning users.
Numerical Simulation of Iced Wing Using Separating Shear Layer Fixed Turbulence Models

Aerodynamic prediction of glaze ice accretion on airfoils and wing is studied using the Reynolds-averaged Navier-Stokes method. Two separation fixed turbulence models are developed considering the nonequilibrium characteristics of turbulence. The key ad hoc fix is a term of the local ratio of turbulent production to dissipation, which is used to amplify the destruction term of the {\omega}-equation to increase the eddy viscosity in a separating shear layer of the fully turbulent region. A shear stress limiter is adopted to appropriately simulate the beginning process of the shear layer transition when the turbulence is under development. The proposed separation fixed terms can be easily implemented into current solvers. Two airfoils and a three-dimensional swept wing with ice accretions are numerically tested using the modified models. The results indicate that the separating shear layer fixes improve the ability of the models in predicting the stall behavior at large angles of attack. The simulated averaged flow field and turbulence intensity distribution are consistent with experimental data.
Accounts, Accountability and Agency for Safe and Ethical AI

We examine the problem of explainable AI (xAI) and explore what delivering xAI means in practice, particularly in contexts that involve formal or informal and ad-hoc collaboration where agency and accountability in decision-making are achieved and sustained interactionally. We use an example from an earlier study of collaborative decision-making in screening mammography and the difficulties users faced when trying to interpret the behavior of an AI tool to illustrate the challenges of delivering usable and effective xAI. We conclude by setting out a study programme for future research to help advance our understanding of xAI requirements for safe and ethical AI.
End-to-End Differentiable Molecular Mechanics Force Field Construction

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to molecules or biopolymers, making them difficult to optimize to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph nets to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using a feed-forward neural network. Since all stages are built using smooth functions, the entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach has the capacity to reproduce legacy atom types and can be fit to MM and QM energies and forces, among other targets.
Feature Whitening via Gradient Transformation for Improved Convergence

Feature whitening is a known technique for speeding up training of DNN. Under certain assumptions, whitening the activations reduces the Fisher information matrix to a simple identity matrix, in which case stochastic gradient descent is equivalent to the faster natural gradient descent. Due to the additional complexity resulting from transforming the layer inputs and their corresponding gradients in the forward and backward propagation, and from repeatedly computing the Eigenvalue decomposition (EVD), this method is not commonly used to date. In this work, we address the complexity drawbacks of feature whitening. Our contribution is twofold. First, we derive an equivalent method, which replaces the sample transformations by a transformation to the weight gradients, applied to every batch of B samples. The complexity is reduced by a factor of S=(2B), where S denotes the feature dimension of the layer output. As the batch size increases with distributed training, the benefit of using the proposed method becomes more compelling. Second, motivated by the theoretical relation between the condition number of the sample covariance matrix and the convergence speed, we derive an alternative sub-optimal algorithm which recursively reduces the condition number of the latter matrix. Compared to EVD, complexity is reduced by a factor of the input feature dimension M. We exemplify the proposed algorithms with ResNet-based networks for image classification demonstrated on the CIFAR and Imagenet datasets. Parallelizing the proposed algorithms is straightforward and we implement a distributed version thereof. Improved convergence, in terms of speed and attained accuracy, can be observed in our experiments.
The Design of Tangible Digital Musical Instruments

Here we present guidelines that highlight the impact of haptic feedback upon the experiences of computer musicians using Digital Musical Instruments (DMIs). In this context, haptic feedback offers a tangible, bi-directional exchange between a musician and a DMI. We propose that by adhering to and exploring these guidelines the application of haptic feedback can enhance and augment the physical and affective experiences of a musician in interactions with these devices. It has been previously indicated that in the design of haptic DMIs, the experiences and expectations of a musician must be considered for the creation of tangible DMIs and that haptic feedback can be used to address the physical-digital divide that currently exists between users of such instruments.
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure

Estimating absolute camera orientations is essential for attitude estimation tasks. An established approach is to first carry out visual odometry (VO) or visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach, besides the redundancy in estimating full 6 DOF camera poses, is the dependency on estimating a map (3D scene points) jointly with the 6 DOF poses due to the basic constraint on structure-and-motion. To simplify the task of absolute orientation estimation, we formulate the monocular rotational odometry problem and devise a fast algorithm to accurately estimate camera orientations with 2D-2D feature matches alone. Underpinning our system is a new incremental rotation averaging method for fast and constant time iterative updating. Furthermore, our system maintains a view-graph that 1) allows solving loop closure to remove camera orientation drift, and 2) can be used to warm start a V-SLAM system. We conduct extensive quantitative experiments on real-world datasets to demonstrate the accuracy of our incremental camera orientation solver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solving the known rotation problem to estimate the trajectory of the camera and the surrounding map, and 2) enabling V-SLAM systems to track pure rotational motions.
Unknown Presentation Attack Detection against Rational Attackers

Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real-life settings. Some of the challenges for existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few-shot learning, and explainability. In this study, these limitations are approached by reliance on a game-theoretic view for modeling the interactions between the attacker and the detector. Consequently, a new optimization criterion is proposed and a set of requirements are defined for improving the performance of these systems in real-life settings. Furthermore, a novel detection technique is proposed using generator-based feature sets that are not biased towards any specific attack species. To further optimize the performance on known attacks, a new loss function coined categorical margin maximization loss (C-marmax) is proposed which gradually improves the performance against the most powerful attack. The proposed approach provides a more balanced performance across known and unknown attacks and achieves state-of-the-art performance in known and unknown attack detection cases against rational attackers. Lastly, the few-shot learning potential of the proposed approach is studied as well as its ability to provide pixel-level explainability.