Miracle-dz/UniFAR-FTUs huggingface dataset:
A new vegetation heavy metal pollution index for detecting the pollution degree of different varieties of maize under copper stress

ABSTRACT This study proposed a new vegetation heavy metal pollution index VHMPI to detect the pollution degree of different varieties of maize under copper stress, which provides a new idea for the detection of heavy metal pollution in vegetation. In order to ensure the outdoor growth environment of maize, we put all maize into outdoor greenhouse. The spectral reflectance interval of 450 nm–850 nm of maize leaves was processed by the first order differential (D) and continuum removal (CR), and the DCR spectral curve was obtained. The Pearson correlation coefficient (R) was used to analyze the DCR data and the biochemical data and select characteristic bands that sensitive to heavy metal Cu. The calculated Pearson correlation coefficients suggested that the DCR value at 490 nm–520 nm and 680 nm–700 nm presented a linear positive correlation close to 1 with the Cu2+ contents in soil and leaves, and a linear negative correlation close to −1 was present in the range of 630 nm–650 nm and 710 nm–750 nm. We selected the DCR value of wavelengths 505 nm, 640 nm, 690 nm and 730 nm to establish VHMPI, and compared it with conventional vegetation indices (VIs) by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves, Vegetation indices include WBI (Water Band Index), PSNDa (Pigment Specific Normalized Difference a), PRI (Photochemical Reflectance Index), NDVI (Normalized Difference Vegetation Index). Maize leaf spectral data obtained from experiments in 2017 were used for verification, VHMPI was also compared with WBI, PSNDa, PRI and NDVI.The results suggested that VHMPI showed a significant correlation with Cu2+ stress concentration,and the correlation of VHMPI was much stronger than that of other vegetation indices. The proposed VHMPI detects the pollution degree of maize with different varieties and in different periods under copper stress has advantages of straightforward calculation, robustness, and high effectiveness. This study focused on the laboratory leaf scale, so it is expected that future work extends it to a wide range of field scale and image scale.
The Cedar Project: Racism and its impacts on health and wellbeing among young Indigenous people who use drugs in Prince George and Vancouver, BC

Racism continues to drive health disparities between Indigenous and non-Indigenous peoples in Canada. This study focuses on racism experienced by young Indigenous people who have used drugs in British Columbia (BC), and predictors of interpersonal racism. Cedar Project is a community-governed cohort study involving young Indigenous people who use drugs in Vancouver and Prince George, BC. This cross-sectional study included data collected between August 2015-October 2016. The Measure of Indigenous Racism Experiences (MIRE) scale was used to assess experiences of interpersonal racism across 9 unique settings on a 5-point Likert scale, collapsing responses into three categories (none/low/high). Multinomial logistic regression models were used to examine associations between key variables and interpersonal racism. Among 321 participants, 79% (n = 255) experienced racism in at least one setting. Thirty two percent (n = 102) experienced high interpersonal racism from police, governmental agencies (child ‘welfare’, health personnel), and in public settings. Ever having a child apprehended (AOR:2.76, 95%CI:1.14–6.65), probable post-traumatic stress (AOR:2.64; 95%CI:1.08–6.46), trying to quit substances (AOR:3.69; 95%CI:1.04–13.06), leaving emergency room without receiving treatment (AOR:3.05; 95%CI:1.22–7.64), and having a traditional language spoken at home while growing up (AOR:2.86; 95%CI:1.90–6.90) were associated with high interpersonal racism. Among women, experiencing high interpersonal racism was more likely if they lived in Prince George (AOR:3.94; 95%CI:1.07–14.50), ever had a child apprehended (AOR:5.09; 95%CI:1.50–17.30), and had probable post-traumatic stress (AOR:5.21; 95%CI:1.43–18.95). Addressing racism experienced by Indigenous peoples requires immediate structural systemic, and interpersonal anti-racist reforms.
Persistent high prevalence of modifiable cardiovascular risk factors among patients with osteoarthritis in the UK in 1992–2017

Objectives To compare the annual and period prevalence of modifiable cardiovascular risk factors (MCVRFs) between populations with and without osteoarthritis (OA) in the UK over 25 years. Methods 215 190 patients aged 35 years and over from the UK Clinical Practice Research Datalink GOLD database who were newly diagnosed OA between 1992 and 2017, as well as 1:1 age-matched, sex-matched, practice-matched and index year-matched non-OA individuals, were incorporated. MCVRFs including smoking, hypertension, type 2 diabetes, obesity and dyslipidaemia were defined by Read codes and clinical measurements. The annual and period prevalence and prevalence rate ratios (PRRs) of individual and clustering (≥1, ≥2 and ≥3) MCVRFs were estimated by Poisson regression with multiple imputations for missing values. Results The annual prevalence of MCVRFs increased in the population with OA between 1992 and 2017 and was consistently higher in the population with OA compared with the population without OA between 2004 and 2017. Trends towards increased or stable annual PRRs for individuals and clustering of MCVRFs were observed. A 26-year period prevalence of single and clustering MCVRFs was significantly higher in individuals with OA compared with non-OA individuals. Period PRRs were higher in Southern England, women and increased with age for most MCVRFs except for obesity, which has the higher PRR in the youngest age group. Conclusions A consistently higher long-term prevalence of MCVRFs was observed in individuals with OA compared to those without OA. The higher prevalence of obesity in the youngest age group with OA highlights the need for public health strategies. Further research to understand MCVRF management in OA populations is necessary.
The constant difference of elasticities function with applications to the EC animal feed sector

Unlike those flexible functional forms which are local approximations, the ‘Constant Difference of Elasticities' (CDE) function is globally well-behaved. Due to its properties and parsimony in the number of parameters, the CDE functional form offers many potential applications. Two empirical applications are presented. First, aggregate CDE input demand functions are specified and estimated econometrically for the feed concentrate markets in Denmark, Ireland and the United Kingdom. The second empirical application illustrates how a matrix of compensated price elasticities can be obtained with only a priori knowledge of its direct and diagonal elements. The suggested approach exploits some of the properties of the Allen elasticities of substitution associated with the CDE functional form.
Reentrant phase behavior in systems with density-induced tunneling

Open many body quantum systems play a paramount role in various branches of physics, such as quantum information, nonlinear optics or condensed matter. The dissipative character of open systems has gained a lot of interest especially within the fields of quantum optics, due to unprecedented stabilization of quantum coherence, and quantum information, with its desire to control environmental degrees of freedom. We look beyond the typical mechanism of dissipation associated with an external source and show that strongly interacting many particle systems can create quantum decoherence within themselves. We study a quantum bosonic two-dimensional many body system with extended interactions between particles. Analytical calculations show that the system can be driven out of its coherent state, which is prevalent among commonly used setups. However, we also observe a revival of the superfluid phase within the same framework for sufficiently large interaction strength. The breakdown of quantum coherence is inevitable, but can be misinterpreted if one assumes improper coupling between the constituents of the many particle system. We show an adequate path to retrieve physically relevant results and consider its limitations. The system displays a natural cutoff that enforces the breakdown of superfluidity.
Exact and Efficient Bayesian Inference for Privacy Risk Quantification (Extended Version)

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov Chain Monte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics.
Hyperprolactinemia: A Systematic Review of Diagnosis and Management

Hyperprolactinemia is a common endocrine disorder that can be associated with significant morbidity. It can result from a number of causes, including use of medication, hypothyroidism and pituitary disorders. Depending on the cause and consequences of hyperprolactinemia, selected patients require treatment considering the underlying cause, age sex, and reproductive status. We describe a systematic review of hyperprolactinemia, including microadenomas and macroadenomas, in various clinical settings, with emphasis on newer diagnostic strategies and the role of various therapeutic options, including treatment with selective dopamine agonists. Through this review, we aimed to compare efficacy and adverse effects of medications, surgery and radiotherapy in the treatment of hyperprolactinemia.Delta Med Col J. Jan 2018 6(2): 90-101
Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections

Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in such scenarios, this paper proposes a curriculum proximal policy optimization (CPPO) framework with stage-decaying clipping. By adjusting the clipping parameter during different stages of training through proximal policy optimization (PPO), the vehicle can first rapidly search for an approximate optimal policy or its neighborhood with a large parameter, and then converges to the optimal policy with a small one. Particularly, the stage-based curriculum learning technology is incorporated into the proposed framework to improve the generalization performance and further accelerate the training process. Moreover, the reward function is specially designed in view of different curriculum settings. A series of comparative experiments are conducted in intersection-crossing scenarios with bi-lane carriageways to verify the effectiveness of the proposed CPPO method. The results show that the proposed approach demonstrates better adaptiveness to different dynamic and complex environments, as well as faster training speed over baseline methods.
Last Mile Delivery with Drones and Sharing Economy

We consider a combined system of regular delivery trucks and crowdsourced drones, available via a sharing economy platform, to provide a technology-assisted crowd-based last-mile delivery experience. We develop analytical models and methods for a system in which package delivery is performed by a big truck carrying many packages to a neighborhood or a town in a metropolitan area and then the packages are assigned to crowdsourced drone operators to deliver them to their final destinations. We develop several optimization models for various cases of the problem and use a combination of heuristic algorithms to solve this NP-hard problem. Finally, we present computational results for the models and algorithms, and conduct an exhaustive sensitivity analysis to check the influence of different parameters and assumptions on the final results. We also provide extensive managerial insights on the benefits of our proposed model if implemented in practice.
Defining the extracellular matrix in non-cartilage soft tissues in osteoarthritis - a systematic review

Objective Osteoarthritis (OA) is increasingly seen as a disease of global joint dysfunction, affecting not only cartilage but also the other joint tissues. Extracellular matrix (ECM) is a critical determinant of tissue mechanobiology, but ECM is poorly understood in osteoarthritic joint tissues beyond cartilage in human OA and animal models of OA. Therefore, we aimed to define the structural composition and architecture of non-cartilage soft joint tissue ECM in human OA, and to compare the ECM changes observed in humans to those seen in animal models of OA. Methods A systematic search strategy, devised using relevant matrix, tissue, and disease nomenclature, was run through the MEDLINE, EMBASE, and Scopus databases. Demographic, clinical, and biological data were extracted from eligible studies. Bias analysis was performed. Results 142 studies were included, which covered capsule, ligaments, meniscus, skeletal muscle, synovium, and tendon in both humans and animals, and fat pad and intervertebral disc in humans only. Overall, included studies show that the expression of structural ECM components changes in disease within an ECM that becomes disorganised with increasing joint degeneration. Conclusions This systematic review consolidates existing knowledge of a poorly defined aspect of OA pathophysiology. Changes in ECM composition and architecture occur across soft joint tissues in OA, but most of these remain poorly defined due to the low number of studies and lack of healthy comparator groups. Further research to better understand the context within which cartilage is damaged in OA may enable a better understanding of OA and its potential treatments.
Exposing and Addressing Security Vulnerabilities in Browser Text Input Fields

In this work, we perform a comprehensive analysis of the security of text input fields in web browsers. We find that browsers' coarse-grained permission model violates two security design principles: least privilege and complete mediation. We further uncover two vulnerabilities in input fields, including the alarming discovery of passwords in plaintext within the HTML source code of the web page. To demonstrate the real-world impact of these vulnerabilities, we design a proof-of-concept extension, leveraging techniques from static and dynamic code injection attacks to bypass the web store review process. Our measurements and case studies reveal that these vulnerabilities are prevalent across various websites, with sensitive user information, such as passwords, exposed in the HTML source code of even high-traffic sites like Google and Cloudflare. We find that a significant percentage (12.5\%) of extensions possess the necessary permissions to exploit these vulnerabilities and identify 190 extensions that directly access password fields. Finally, we propose two countermeasures to address these risks: a bolt-on JavaScript package for immediate adoption by website developers allowing them to protect sensitive input fields, and a browser-level solution that alerts users when an extension accesses sensitive input fields. Our research highlights the urgent need for improved security measures to protect sensitive user information online.
Influence of the symmetry energy on the nuclear binding energies and the neutron drip line position

A clear connection can be established between properties of nuclear matter and finite-nuclei observables, such as the correlation between the slope of the symmetry energy and dipole polarizability, or between compressibility and the isoscalar monopole giant resonance excitation energy. Establishing a connection between realistic atomic nuclei and an idealized infinite nuclear matter leads to a better understanding of underlying physical mechanisms that govern nuclear dynamics. In this work, we aim to study the dependence of the binding energies and related quantities (e.g. location of drip lines, the total number of bound even-even nuclei) on the symmetry energy $S_2(\rho)$. The properties of finite nuclei are calculated by employing the relativistic Hartree-Bogoliubov (RHB) model, assuming even-even axial and reflection symmetric nuclei. Calculations are performed by employing two families of relativistic energy density functionals (EDFs), based on different effective Lagrangians, constrained to a specific symmetry energy at saturation density $J$ within the interval of $30$--$36$ MeV. Nuclear binding energies and related quantities of bound nuclei are calculated between $8 \leq Z \leq 104$ from the two-proton to the two-neutron drip line. As the neutron drip line is approached, the interactions with stiffer $J$ tend to predict more bound nuclei, resulting in a systematic shift of the two-neutron drip line towards more neutron-rich nuclei. Consequentially, a correlation between the number of bound nuclei $N_{nucl}$ and $S_2(\rho)$ is established for a set of functionals constrained using the similar optimization procedures. The direction of the relationship between the number of bound nuclei and symmetry energy highly depends on the density under consideration.
Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Models

We propose augmenting the empathetic capacities of social robots by integrating non-verbal cues. Our primary contribution is the design and labeling of four types of empathetic non-verbal cues, abbreviated as SAFE: Speech, Action (gesture), Facial expression, and Emotion, in a social robot. These cues are generated using a Large Language Model (LLM). We developed an LLM-based conversational system for the robot and assessed its alignment with social cues as defined by human counselors. Preliminary results show distinct patterns in the robot's responses, such as a preference for calm and positive social emotions like 'joy' and 'lively', and frequent nodding gestures. Despite these tendencies, our approach has led to the development of a social robot capable of context-aware and more authentic interactions. Our work lays the groundwork for future studies on human-robot interactions, emphasizing the essential role of both verbal and non-verbal cues in creating social and empathetic robots.
Active Surveillance of Engineered Stone Workers Facilitates Early Identification of Silicosis: A Discussion of Surveillance of Occupational Lung Diseases

Silicosis in workers exposed to respirable crystalline silica while fabricating engineered stone products is an emerging respiratory health issue. We describe silicosis in engineered stone workers in California and examine clinical features by the source of identification. Cases were identified passively using hospital-based patient discharge data or actively through outreach and medical testing following enforcement investigation. Outcomes were examined based on the source of case identification. We identified 18 cases diagnosed between 2006 and 2020. Cases identified passively compared to other identification methods were associated with lower percent predicted forced vital capacity (FVC) (P ≤ .01), forced expiratory volume in 1 s (FEV1) (P ≤ .01), and diffusing capacity of the lungs for carbon monoxide (DLCO) (P < .01) at the time of diagnosis and were more likely to be identified following death or lung transplant (P = .01). Our experience demonstrates delays in diagnosis and case identification when relying on passive surveillance methods. Enhanced public health surveillance systems can improve the early detection of occupational lung disease and inform future prevention policies.
Latent Painter

Latent diffusers revolutionized the generative AI and inspired creative art. When denoising the latent, the predicted original image at each step collectively animates the formation. However, the animation is limited by the denoising nature of the diffuser, and only renders a sharpening process. This work presents Latent Painter, which uses the latent as the canvas, and the diffuser predictions as the plan, to generate painting animation. Latent Painter also transits one generated image to another, which can happen between images from two different sets of checkpoints.
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. To improve over this limitation, in this paper, we introduce MuTAP for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases PUTs.