Miracle-dz/UniFAR-FTUs huggingface dataset:
An inexpensive system for the deterministic transfer of 2D materials

The development of systems for the deterministic transfer of two-dimensional (2D) materials have undoubtedly contributed to great advancements in 2D materials research. In fact, they have made it possible to fabricate van der Waals heterostructures and 2D materials-based devices with complex architectures. Nonetheless, as far as we know, the number of existing papers providing enough details to reproduce these systems by other research groups is very scarce in the literature. Moreover, these systems typically require the use of expensive optical and mechanical components hampering their applicability in research groups with a low budget. Here, we demonstrate how a deterministic placement system for 2D materials, set up with full capabilities, can be implemented for under 900 € -- which can be easily implemented in labs and educational labs with low budgets.
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behavior modelling, prediction and interaction control.
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes.

We study in this paper a variant of Wasserstein barycenter problem, which we refer to as tree-Wasserstein barycenter, by leveraging a specific class of ground metrics, namely tree metrics, for Wasserstein distance. Drawing on the tree structure, we propose an efficient algorithmic approach to solve the tree-Wasserstein barycenter and its variants. The proposed approach is not only fast for computation but also efficient for memory usage. Exploiting the tree-Wasserstein barycenter and its variants, we scale up multi-level clustering and scalable Bayes, especially for large-scale applications where the number of supports in probability measures is large. Empirically, we test our proposed approach against other baselines on large-scale synthetic and real datasets.
Numerical solution of inverse problems by weak adversarial networks

In this paper, a weak adversarial network approach is developed to numerically solve a class of inverse problems, including electrical impedance tomography and dynamic electrical impedance tomography problems. The weak formulation of the partial differential equation for the given inverse problem is leveraged, where the solution and the test function are parameterized as deep neural networks. Then, the weak formulation and the boundary conditions induce a minimax problem of a saddle function of the network parameters. As the parameters are alternatively updated, the network gradually approximates the solution of the inverse problem. Theoretical justifications are provided on the convergence of the proposed algorithm. The proposed method is completely mesh-free without any spatial discretization, and is particularly suitable for problems with high dimensionality and low regularity on solutions. Numerical experiments on a variety of test inverse problems demonstrate the promising accuracy and efficiency of this approach.
Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

We show how to use the Operational Space Control framework (OSC) under joint and Cartesian constraints for reinforcement learning in Cartesian space. Our method is able to learn fast and with adjustable degrees of freedom, while we are able to transfer policies without additional dynamics randomizations on a KUKA LBR iiwa peg-in-hole task. Before learning in simulation starts, we perform a system identification for aligning the simulation environment as far as possible with the dynamics of a real robot. Adding constraints to the OSC controller allows us to learn in a safe way on the real robot or to learn a flexible, goal conditioned policy that can be easily transferred from simulation to the real robot.1
Flat space, dark energy, and the cosmic microwave background

This paper reviews some of the results of the Planck collaboration and shows how to compute the distance from the surface of last scattering, the distance from the farthest object that will ever be observed, and the maximum radius of a density fluctuation in the plasma of the CMB. It then explains how these distances together with well-known astronomical facts imply that space is flat or nearly flat and that dark energy is 69% of the energy of the Universe.
Learning a Directional Soft Lane Affordance Model for Road Scenes Using Self-Supervision

Humans navigate complex environments in an organized yet flexible manner, adapting to the context and implicit social rules. Understanding these naturally learned patterns of behavior is essential for applications such as autonomous vehicles. However, algorithmically defining these implicit rules of human behavior remains difficult. This work proposes a novel self-supervised method for training a probabilistic network model to estimate the regions humans are most likely to drive in as well as a multimodal representation of the inferred direction of travel at each point. The model is trained on individual human trajectories conditioned on a representation of the driving environment. The model is shown to successfully generalize to new road scenes, demonstrating potential for realworld application as a prior for socially acceptable driving behavior in challenging or ambiguous scenarios which are poorly handled by explicit traffic rules.
Dual Graph Representation Learning

Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs. Although inductive approaches can generalize to unseen nodes, they neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, \textbf{CADE}, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. We exhibit that our approach is effective by comparing to state-of-the-art methods.
Deep Learning Based Cell Parasites Detection

— Since 2012 deep learning has dramatically improved the performance in many application areas such as image classification, object detection, speech recognition, drug discovery and etc. Deep learning algorithms promise to discover the intricate hidden information inside the data by leveraging the large dataset, modeling and computing power. Although deep learning techniques show medical expert level performance in a lot of cell level images analyzing applications, however some of the cell level images analyzing applications are still not explored or under explored. In this work, we explored the scattering based cell level Cryptosporidium and Giardia detection in the water with deep learning. Our experimental demonstrates that the new developed deep learning-based algorithm surpassed the hand-crafted SVM based algorithm with above 95.6 percentage in accuracy and 100fps in speed on embedded Jetson TX2 platform. Our research will lend to real-time and high accuracy cell level Cryptosporidium and Giardia detection system in the future.
Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation

We propose a new deep learning network that introduces a deeper CNN channel filter and constraints as losses to reduce joint position and motion errors for 3D video human body pose estimation. Our model outperforms the previous best result from the literature based on mean per-joint position error, velocity error, and acceleration errors on the Human 3.6M benchmark corresponding to a new state-of-the-art mean error reduction in all protocols and motion metrics. Mean per joint error is reduced by 1%, velocity error by 7% and acceleration by 13% compared to the best results from the literature. Our contribution increasing positional accuracy and motion smoothness in video can be integrated with future end to end networks without increasing network complexity. Our model and code are available at this https URL Keywords: 3D, human, image, pose, action, detection, object, video, visual, supervised, joint, kinematic
Experimental study on the effectiveness of Motivational Counseling (CM) in the nutritional field

The project has evaluated the effectiveness of interventions for nutritional counseling through the use of techniques of motivational counseling, aimed at increasing motivation to change unhealthy lifestyles. The study was carried out almost experimentally with three nutritional outpatients, that enrolled subjects to be submitted to nutritional counseling using techniques of motivational counseling representing the "experimental group" and one outpatient which recruited subjects submitted only to traditional Nutritional Counseling representing the "control group". The trial, conducted between September 2009 to December 2011, was structured in 5 stages: Visit, First Control, Second, Third and Fourth Control. Aspects investigated were: 1) Willingness to change, 2) Motivational factors, 3) Body Mass Index and 4) Lifestyles and physical activity. The work of NO was considered effective if it had obtained following results: not gaining weight for normal weight people, a reduction of at least 5% for overweight in the period that went from the first visit to the fourth control. This definition allowed us to distinguish outcomes in "Success" and "Failures". For the four motivational factors analyzed in the "Pilot" and "Controls", it was found that the Inner Fracture and the Temptation decreased, while the Self-efficacy and Importance increased. The increase of scores recorded in the evaluation of the Availability to change has increased the effectiveness of treatment. From the analysis of results for the Willingness to change and the four motivational factors, it was observed that the increase of average scores recorded also an increase of the effectiveness of the treatment.
Cystic fibrosis-related diabetes: practice challenges.

vol 17 no 2 March 2005 Paediatric Nursing 23 Cystic fibrosis (CF) is the most common life threatening, multi-system, recessively inherited disease of Caucasian populations with an incidence of one in 2,500 births. Most of the morbidity and more than 90 per cent of the mortality of CF is related to chronic pulmonary sepsis and its complications (CF Trust 2001). However, life expectancy has increased dramatically and has been predicted to be at least 40 years for children born in the 1990s (Elborn et al 1991). With this increased life expectancy, however, new long-term complications have evolved. Cystic fibrosis-related diabetes (CFRD) is one such complication. The existence of CFRD has been found to be a powerful determinant of severe lung disease and reduced survival (Koch et al 2001) and is therefore of increasing importance as CF life-expectancy increases. CFRD is a complex condition and shares features of both Type 1 and Type 2 diabetes. Insulin deficiency as a result of severe fibrosis-induced islet damage and insulin resistance are contributory factors (Yung et al 2002). Mild insulin resistance occurs initially and glucose tolerance is determined by the insulin secretory capacity. Both may fluctuate over time within each patient. Periods of acute infection, enteral feeding or glucocorticoid therapy, for example, may cause frank diabetes. This may necessitate insulin therapy in those with otherwise normal glucose tolerance needing no insulin treatment. Such variability could explain why CFRD can be so difficult to manage. The major goals of management include the control of hyperglycaemia, the avoidance of hypoglycaemia and optimisation of nutrition and growth (Moran 2002). The standard medical treatment of hyperglycaemia is insulin but goals and aggressiveness of treatment varies (Mackie et al 2003). While some suggest that oral glucose-lowering agents may be beneficial (Rosenecker et al 2001), most of the literature on this subject consists primarily of abstracts using small numbers of patients. Randomised trials, comparing insulin treatment with oral agents are needed.
Psychosocial aspects of medication nonadherence after kidney transplantation

__Background:__ Patients have to take immunosuppressive medication after kidney transplantation to prevent rejection of the graft. This thesis investigated patients’ adherence behavior, attitudes and beliefs about the immunosuppressive regime. The research questions were (1) What is the rate of nonadherence among our population of kidney transplant recipients and does this change over time? (2) What are the attitudes, beliefs and goals of kidney transplant recipients towards the medication regime? (3) To what extent are these attitudes, beliefs and goals related to nonadherence? (4) To what extent is nonadherence related to graft survival? __Methods:__ We used different questionnaires and self-reported nonadherence at different time points in young adults (18-25yrs), the elderly (>65 yrs) and in a kidney transplantation cohort of all ages. __Results:__ Three distinct attitude profiles concerning post-transplant health lifestyle were repeatedly found: (i) patients that were afraid of rejection and therefore take their medication, (ii) patients that feel secure and know how they can take good care of this kidney, and (iii) patients who found their appearance important and experience side-effects of the medication. The profiles were not correlated with nonadherence. Patients find themselves capable of taking the medication correct and understand the importance; nevertheless 6 weeks after kidney transplantation 17% were classified as nonadherent, with a significant lower graft survival. The rate of nonadherence increases to 31% at 18 months. Patients who are younger, have a job, have unrealistic beliefs about the graft survival have a higher risk of being nonadherent. Patients who reported low importance of medication adherence as a personal goal were more likely to become nonadherent over time. __Discussion:__ The self-reported nonadherence levels found in this study so soon after transplantation demonstrate the need for early and continued intervention after kidney transplantation in order to maximize adherence and consequently clinical outcomes.
Handling Constrained Multi-objective Optimization By Ignoring Constraints and Using Two Evolutionary Frameworks

Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well,constraint satisfaction is emphasized over objective minimization.Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems.
Immune Checkpoint Blockade in Gastrointestinal Cancers: The Current Status and Emerging Paradigms

Immunotherapy is a rapidly evolving treatment paradigm that holds promise to provide long-lasting survival benefits for patients with cancer. This promise, however, remains unfulfilled for the majority of patients with gastrointestinal (GI) cancers, as significant limitations in efficacy exist with immune checkpoint inhibitors (ICIs) in this disease group. A plethora of novel combination treatment strategies are currently being investigated in various clinical trials to make them more efficacious as our understanding of molecular mechanisms mediating resistance to immunotherapy advances. In this article, we summarize the current status of immune checkpoint blockade in GI cancers and discuss the biological rationales that underlie the emerging treatment strategies being tested in ongoing clinical trials in combination with ICIs. We also highlight the promising early results from these strategies and provide future perspectives on enhancing response to immunotherapy for patients with GI cancers.
Antibiotic Treatment and Age Are Associated With Staphylococcus aureus Carriage Profiles During Persistence in the Airways of Cystic Fibrosis Patients

Background Staphylococcus aureus is one of the most isolated pathogens from the airways of cystic fibrosis (CF) patients. There is a lack of information about the clonal nature of S. aureus cultured from CF patients and their impact on disease. We hypothesized that patients would differ in their clinical status depending on S. aureus clonal carriage profiles during persistence. Methods During a 21-months prospective observational multicenter study (Junge et al., 2016), 3893 S. aureus isolates (nose, oropharynx, and sputa) were cultured from 183 CF patients (16 German centers, 1 Austrian center) and subjected to spa-sequence typing to assess clonality. Data were associated to lung function, age, gender, and antibiotic treatment by multivariate regression analysis. Results Two hundred and sixty-five different spa-types were determined with eight prevalent spa-types (isolated from more than 10 patients): t084, t091, t008, t015, t002 t012, t364, and t056. We observed different carriage profiles of spa-types during the study period: patients being positive with a prevalent spa-type, only one, a dominant or related spa-type/s. Patients with more antibiotic cycles were more likely to be positive for only one spa-type (p = 0.005), while older patients were more likely to have related (p = 0.006), or dominant spa-types (p = 0.026). Two percent of isolates were identified as methicillin-resistant S. aureus (MRSA) and evidence of transmission of clones within centers was low. Conclusion There was a significant association of antibiotic therapy and age on S. aureus carriage profiles in CF patients indicating that antibiotic therapy prevents acquisition of new clones, while during aging of patients with persisting S. aureus, dominant clones were selected and mutations in the spa-repeat region accumulated.
A comparative study of 2 mg, 4 mg intravenous ondansetron and 2% lignocaine hydrochloride pre-treatment to alleviate pain of propofol injection

Introduction: Propofol belongs to one of the commonly used intravenous (IV)-inducing anaesthetic agents. However, at least 70% of the patients experience pain on propofol injection. To reduce pain, various methods were tried. A randomised, double-blinded comparative study was done by us. Materials and Methods: A total of 180 patients belonging to American Society of Anesthesiologists Grade 1 and 2, who were for elective or emergency surgery, were randomly divided into three groups. All surgeries were under general anaesthesia. Group 1 was administered 2 ml of 2% IV lignocaine, Group 2 was administered IV ondansetron 2 mg and Group 3 was administered IV ondansetron 4 mg. One-fourth of the total dosage was injected over 5 s, and pain was assessed in the patients during injection of propofol by verbal rating scale. Results: The intensity and incidence of pain were comparatively less with lignocaine and ondansetron 4 mg overall. Even though there was better relief of pain with lignocaine, ondansetron 4 mg along with venous occlusion also reduced propofol injection pain. Conclusion: Ondansetron also can be used as an alternative in relief of pain on propofol injection just like lignocaine.
PI3Kinase-p110δ Overexpression Impairs Dendritic Morphogenesis and Increases Dendritic Spine Density

Activity and expression of the phosphoinositide 3-kinase (PI3K) catalytic isoform, PIK3CD/p110δ, is increased in schizophrenia, autism, and intellectual delay and pro-cognitive preclinical efficacy of p110δ-inhibition has been demonstrated in pharmacological, genetic, and developmental rodent models of psychiatric disorders. Although PI3K signaling has been implicated in the development and function of neurons and glia; isoform-specific roles of the individual PI3Ks are less clear and the biological effects of increased p110δ on neuronal development are unknown. Since the pathobiological direction of p110δ changes in neurodevelopmental disorders are increased expression and activity, we hypothesized that overexpression of p110δ would impact measures of neuronal development and maturation relevant to connectivity and synaptic transmission. p110δ overexpression in primary rat hippocampal cultures significantly reduced dendritic morphogenesis and arborization and increased immature and mature dendritic spine densities, without impacting cell viability, soma size, or axon length. Together, our novel findings demonstrate the importance of homeostatic regulation of the p110δ isoform for normative neuronal development and highlight a potential pathophysiological mechanism of association to disorders of neurodevelopment.