Miracle-dz/UniFAR-FTUs huggingface dataset:
A Failure to Guide: Patient Experiences within a State-Run Cannabis Program in Pennsylvania, United States

Abstract Introduction Medical cannabis has been available for purchase in dispensaries in Pennsylvania, United States since April 2018. Patients wanting to access medical cannabis must receive certification from physicians for a limited number of physical and psychological conditions. Despite increasing numbers of patients using cannabis in the United States, little is known about the patient experience during certification and entry into state-regulated cannabis programs and how and if they are guided by health care professionals and dispensary staff. Through focus group discussions, we sought to capture patient perspectives of certification, cannabis acquisition and cannabis use. Methods Twenty-seven Pennsylvania participants took part in 7 virtual focus groups from June to July 2020. Participants were recruited statewide from the community, medical settings, and dispensaries. Results Focus group results indicate that while the medical cannabis program is functional, policymakers and the medical community have failed to meaningfully integrate cannabis into the health care system. Participants expressed frustration around two central themes: there was no overarching education about medical use of cannabis and there was little consistency and availability for people once they found a suitable product, resulting in inadequate symptom relief and exorbitant out of pocket costs to pursue cannabis use as an adjuvant therapeutic. Participants noted a siloed experience between the certification process, accessing dispensaries, and receiving ongoing medical care. The lack of integrated care required high levels of self-reliance and experimentation with medical cannabis for participants. Conclusion We recommend that cannabis be better integrated into medical care for patients with qualifying conditions.
Numerical simulation and experimental study of heat-fluid-solid coupling of double flapper-nozzle servo valve

The double flapper-nozzle servo valve is widely used to launch and guide the equipment. Due to the large instantaneous flow rate of servo valve working under specific operating conditions, the temperature of servo valve would reach 120°C and the valve core and valve sleeve deform in a short amount of time. So the control precision of servo valve significantly decreases and the clamping stagnation phenomenon of valve core appears. In order to solve the problem of degraded control accuracy and clamping stagnation of servo valve under large temperature difference circumstance, the numerical simulation of heat-fluid-solid coupling by using finite element method is done. The simulation result shows that zero position leakage of servo valve is basically impacted by oil temperature and change of fit clearance. The clamping stagnation is caused by warpage-deformation and fit clearance reduction of the valve core and valve sleeve. The distribution rules of the temperature and thermal-deformation of shell, valve core and valve sleeve and the pressure, velocity and temperature field of flow channel are also analyzed. Zero position leakage and electromagnet’s current when valve core moves in full-stroke are tested using Electro-hydraulic Servo-valve Characteristic Test-bed of an aerospace sciences and technology corporation. The experimental results show that the change law of experimental current at different oil temperatures is roughly identical to simulation current. The current curve of the electromagnet is smooth when oil temperature is below 80°C, but the amplitude of current significantly increases and the hairy appears when oil temperature is above 80°C. The current becomes smooth again after the warped valve core and valve sleeve are reground. It indicates that clamping stagnation is caused by warpage-deformation and fit clearance reduction of valve core and valve sleeve. This paper simulates and tests the heat-fluid-solid coupling of double flapper-nozzle servo valve, and the obtained results provide the reference value for the design of double flapper-nozzle force feedback servo valve.
Verb morphology in Turkish-speaking children with and without DLD: the role of morphophonology

ABSTRACT In this study, we examined the verb morphology system of Turkish-speaking preschoolers with developmental language disorder (DLD) and compared their use to that of two groups of typically developing (TD) children. We report data from a total of 80 monolingual children – 40 children with DLD, 20 TD age-matched children and 20 TD younger MLU-matched children. Language samples obtained from the children served as the source of the data. The results show that the children with DLD were less accurate in their use of verb suffixes than both the younger and the age-matched TD children. The most frequent error types included use of bare stems, omission of the suffix, and replacing one finite verb suffix with another. The distinction between witnessed past and reported past also posed a challenge. Multi-level model results showed that phonemic length and irregular morphophonology were the best predictors of the children’s level of accuracy. These results indicate that even though Turkish is considered a “verb friendly” language, children with DLD do not succeed in closing the gap with their TD peers. The complex interplay of morphology and phonology in Turkish appears to be the major obstacle for children with DLD acquiring this agglutinative language.
Adipose tissue plays a major role in retinoic acid-mediated metabolic homoeostasis

ABSTRACT Retinoic acid (RA), a bioactive metabolite of vitamin A, has shown therapeutic effects in liver disease, and its effect in improving non-alcoholic fatty liver disease (NAFLD) is associated with the inhibition of adipogenesis in the white adipose tissue (WAT) and fatty acid oxidation induction in the liver. However, the major target organ of RA is unknown. We performed chronic administration of RA in high-fat diet (HFD)-induced NAFLD mice. Further, hepatic and adipose cells were used to study the direct effect of RA on lipid metabolism. In addition, qRT-PCR was performed to examine differential gene expression in mouse adipose tissue. RA administration ameliorated NAFLD in HFD-induced obese mice and increased mouse energy expenditure. Although RA had therapeutic effects on liver histology and lipid accumulation, it did not directly affect lipid metabolism in HepG2 cells. In contrast, RA reduced the weight of several adipose tissues and improved lipid accumulation in OP9 cells. In addition, RA upregulated genes responsible for fatty acid oxidation and thermogenesis in three different WATs. Our work suggests that the liver may not be the main target organ of RA during NAFLD treatment. WAT browning induced by RA may be the primary contributor towards the amelioration of NAFLD in HFD-induced obese mice.
dNLR and TILS can be used as indicators for prognosis and efficacy evaluation of neo-adjuvant chemotherapy in breast cancer.

BACKGROUND To explore the clinical value of dNLR and TILS in the prognostic diagnosis of breast cancer (BC) and the efficacy evaluation of neo-adjuvant chemotherapy. METHODS From January 2012 to January 2014, 72 patients with BC who received neo-adjuvant chemotherapy in our hospital were selected for this study and analyzed retrospectively. The clinical value of dNLR and TILS in the prognosis of BC was observed, and the clinical data of the patients were collected for Cox regression analysis to observe their independent prognostic indicators. According to the clinical efficacy of neo-adjuvant chemotherapy, patients were divided into effective group (EG) (CR+PR) and invalid group (IG) (SD+PD), and the value of dNLR and TILS in the therapeutic effect of patients was observed. RESULTS After therapy, there were 10 cases of CR, 44 cases of PR, 13 cases of SD and 5 cases of PD in 72 patients. With the improvement of curative effect, the expression of dNLR was gradually declined (P < 0.05). The less effective the patients were, the less TILS positive rate was (P < 0.05). However, the expression of dNLR in the EG was obviously lower than that in the IG (P < 0.05). The area under dNLR curve was 0.844, while the area under TILS curve was 0.618. Multivariate Cox regression analysis revealed that TNM staging, dNLR and TILS were independent prognostic factors that affected the patients. CONCLUSION Patients with high expression of dNLR and TILS-negative BC have poor efficacy of neoadjuvant chemotherapy, and dNLR and TILS can be used as prognostic observation indexes for BC.
FLEIM: A stable, accurate and robust extrapolation method at infinity for computing the ground state of electronic Hamiltonians

The Kohn–Sham method uses a single model system, and corrects it by a density functional the exact user friendly expression of which is not known and is replaced by an approximated, usable, model. We propose to use instead more than one model system, and use a greedy extrapolation method to correct the results of the model systems. Evidently, there is a higher price to pay for it. However, there are also gains: within the same paradigm, e.g., excited states and physical properties can be obtained.
N-Omniglot: a Large-scale Dataset for Spatio-Temporal Sparse Few-shot Learning

Few-shot learning (learning with a few samples) is one of the most important capacities of the human brain. However, the current artiﬁcial intelligence systems meet difﬁculties in achieving this ability, so as the biologically plausible spiking neural networks (SNNs). Datasets for traditional few-shot learning domains provide few amounts of temporal information. And the absence of the neuromorphic datasets has hindered the development of few-shot learning for SNNs. Here, we provide the ﬁrst neuromorphic dataset: N-Omniglot, using the Dynamic Vision Sensor (DVS). It contains 1623 categories of handwritten characters, with only 20 samples per class. N-Omniglot eliminates the need for a neuromorphic dataset for SNNs with high spareness and tremendous temporal coherence. Additionally, the dataset provides a powerful challenge and a suitable benchmark for developing SNNs algorithm in the few-shot learning domain due to the chronological information of strokes. We also provide the improved nearest neighbor, convolutional network, SiameseNet, and meta-learning algorithm in spiking version for veriﬁcation.
Active Learning with Pseudo-Labels for Multi-View 3D Pose Estimation

Pose estimation of the human body/hand is a fundamental problem in computer vision, and learning-based solutions require a large amount of annotated data. Given limited annotation budgets, a common approach to increasing label efﬁciency is Active Learning (AL), which selects examples with the highest value to annotate, but choosing the selection strategy is often nontrivial. In this work, we improve Active Learning for the problem of 3D pose estimation in a multi-view setting, which is of increasing importance in many application scenarios. We develop a framework that allows us to efﬁciently extend existing single-view AL strategies, and then propose two novel AL strategies that make full use of multi-view geometry. Moreover, we demonstrate additional performance gains by incorporating predicted pseudo-labels, which is a form of self-training. Our system signiﬁcantly outperforms baselines in 3D body and hand pose estimation on two large-scale benchmarks: CMU Panoptic Studio and In-terHand2.6M. Notably, on CMU Panoptic Studio, we are able to match the performance of a fully-supervised model using only 20% of labeled training data.
A phase-field approach to model evaporation in porous media: Upscaling from pore to Darcy scale

We develop a phase-field model for evaporation in a porous medium by explicitly considering a vapor component together with the liquid and gas phases in the system. The phase-field model consists of the conservation of mass (for phases and vapor component), momentum, and energy. In addition, the evolution of the phase field is described by the Allen-Cahn equation. In the limit of vanishing interface width, matched asymptotic expansions reveal that the phase-field model reduces to the sharp-interface model with all the relevant transmission conditions at the moving interface. An energy estimate is derived, which suggests that for the diffusiondominated regime, energy always decreases with time. However, this is not always the case for other regimes. The phase-field model is upscaled to the Darcy scale using periodic homogenization for the diffusion-dominated regime. The effective parameters at the Darcy scale are connected to the pore scale through corresponding cell problems.
On arbitrarily regular conforming virtual element methods for elliptic partial differential equations

The Virtual Element Method (VEM) is a very effective framework to design numerical approximations with high global regularity to the solutions of elliptic partial differential equations. In this paper, we review the construction of such approximations for an elliptic problem of order $p_1$ using conforming, finite dimensional subspaces of $ H^{p_2}(\Omega)$, where $p_1$ and $p_2$ are two integer numbers such that $p_2 \geq p_1 \geq 1$ and $\Omega\in R^2$ is the computational domain. An abstract convergence result is presented in a suitably defined energy norm. The space formulation and major aspects such as the choice and unisolvence of the degrees of freedom are discussed, also providing specific examples corresponding to various practical cases of high global regularity. Finally, the construction of the"enhanced"formulation of the virtual element spaces is also discussed in details with a proof that the dimension of the"regular"and"enhanced"spaces is the same and that the virtual element functions in both spaces can be described by the same choice of the degrees of freedom.
Video Joint Modelling Based on Hierarchical Transformer for Co-Summarization

Video summarization aims to automatically generate a summary (storyboard or video skim) of a video, which can facilitate large-scale video retrieval and browsing. Most of the existing methods perform video summarization on individual videos, which neglects the correlations among similar videos. Such correlations, however, are also informative for video understanding and video summarization. To address this limitation, we propose Video Joint Modelling based on Hierarchical Transformer (VJMHT) for co-summarization, which takes into consideration the semantic dependencies across videos. Specifically, VJMHT consists of two layers of Transformer: the first layer extracts semantic representation from individual shots of similar videos, while the second layer performs shot-level video joint modelling to aggregate cross-video semantic information. By this means, complete cross-video high-level patterns are explicitly modelled and learned for the summarization of individual videos. Moreover, Transformer-based video representation reconstruction is introduced to maximize the high-level similarity between the summary and the original video. Extensive experiments are conducted to verify the effectiveness of the proposed modules and the superiority of VJMHT in terms of F-measure and rank-based evaluation.
Ultrasonic imaging contrast enhancement using modified dehaze image model

In practical ultrasonic images, post-processing that involves incoherent frame averaging such as persistence, spatial compounding and frequency compounding creates a layer of smoothed out electronic noise that is analogous to fog in photographic images. A novel approach to enhance ultrasound image contrast based on a modified dark channel model used to dehaze photographs is presented. Local minimum intensity is used to highlight image contrast, control influence of noise layer and improve detail resolution by different levels. The experimental results show that the proposed method has better performance over the existing methods in improving the quality of the ultrasonic image.
Vision Transformer for Small-Size Datasets

Recently, the Vision Transformer (ViT), which applied the transformer structure to the image classification task, has outperformed convolutional neural networks. However, the high performance of the ViT results from pre-training using a large-size dataset such as JFT-300M, and its dependence on a large dataset is interpreted as due to low locality inductive bias. This paper proposes Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA), which effectively solve the lack of locality inductive bias and enable it to learn from scratch even on small-size datasets. Moreover, SPT and LSA are generic and effective add-on modules that are easily applicable to various ViTs. Experimental results show that when both SPT and LSA were applied to the ViTs, the performance improved by an average of 2.96% in Tiny-ImageNet, which is a representative small-size dataset. Especially, Swin Transformer achieved an overwhelming performance improvement of 4.08% thanks to the proposed SPT and LSA.
Evolutionary Generation of Visual Motion Illusions

Why do we sometimes perceive static images as if they were moving? Visual motion illusions enjoy a sustained popularity, yet there is no definitive answer to the question of why they work. We present a generative model, the Evolutionary Illusion GENerator (EIGen), that creates new visual motion illusions. The structure of EIGen supports the hypothesis that illusory motion might be the result of perceiving the brain's own predictions rather than perceiving raw visual input from the eyes. The scientific motivation of this paper is to demonstrate that the perception of illusory motion could be a side effect of the predictive abilities of the brain. The philosophical motivation of this paper is to call attention to the untapped potential of"motivated failures", ways for artificial systems to fail as biological systems fail, as a worthy outlet for Artificial Intelligence and Artificial Life research.
Pulmonary Guardians and Special Regulatory Devices in the Lung of Nile Monitor Lizard (Varanus niloticus) with Special Attention to the Communication Between Telocyte, Pericyte, and Immune Cells

Abstract Monitor lizards are acclimatized to a variety of environments. Most of the monitor species are terrestrial, although there are arboreal and semiaquatic monitors. Such accommodation requires unique cellular structure and regulatory devices in various organs, particularly their lungs. This study aimed to report the pulmonary guardians and special regulatory devices that may guard and promote the function of the lungs of the Nile monitor lizards (Varanus niloticus). Specially structured vessels were recorded in the pulmonary tissue involving atypical glomus vessels, vessels with variable wall thickness, and a venule with specialized internal elastic membrane. Moreover, numerous lung resident guardians could be identified including both alveolar and interstitial macrophages, dendritic cells, mast cells, and B- and T-lymphocytes. Pericytes were demonstrated surrounding the capillary endothelium with a characteristic direct hetero-cellular junction with telocytes. Telocytes established a microenvironment through an indirect hetero-cellular junction with the interstitial macrophage, dendritic cells, and pneumocyte type II. Collectively, these data indicate a significant role played by the specially structured vessels and the resident immune cells in guarding the pulmonary tissue of the Nile monitor lizards and promoting its function. Telocytes are suggested to play a key role in angiogenesis and cellular communication to promote the function of the immune cells.
FMViz: Visualizing Tests Generated by AFL at the Byte-level

Software fuzzing is a strong testing technique that has become the de facto approach for automated software testing and software vulnerability detection in the industry. The random nature of fuzzing makes monitoring and understanding the behavior of fuzzers difficult. In this paper, we report the development of Fuzzer Mutation Visualizer (FMViz), a tool that focuses on visualizing byte-level mutations in fuzzers. In particular, FMViz extends American Fuzzy Lop (AFL) to visualize the generated test inputs and highlight changes between consecutively generated seeds as a fuzzing campaign progresses. The overarching goal of our tool is to help developers and students comprehend the inner-workings of the AFL fuzzer better. In this paper, we present the architecture of FMViz, discuss a sample case study of it, and outline the future work. FMViz is open-source and publicly available at https://github.com/AftabHussain/afl-test-viz.
Determining Inputs for Neural Network Models of Multivariate Time Series

In recent years, artificial neural networks have been used successfully to model multivariate water resources time series. By using analytical approaches to determine appropriate model inputs, network size and training time can be reduced. In this paper, it is proposed that the method of Haugh and Box and a new neural network–based approach can be used to identify the inputs for multivariate artificial neural network models. Both methods were used to obtain the inputs for a multivariate artificial neural network model used for forecasting salinity in the River Murray at Murray Bridge, South Australia. The methods were compared with a third method that uses knowledge of travel times in the river to identify a reasonable set of inputs. The results obtained indicate that all three methods are suitable for determining the inputs for multivariate time series models. However, the neural network–based method is preferable because it is quicker and simpler to use. Any prior knowledge of the underlying processes should be used in conjunction with the neural network method.
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

Deep neural networks (DNNs) have demonstrated their outper-formance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable re-source allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably de-ployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretabil-ity, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) inter-pretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~ ×5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems. The code of NeuronFair is open-sourced at https:/github.com/haibinzheng/NeuronFair.