Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement

Water is vital to human life and healthy ecosystems. Here we outline the current state of national‐scale water resources trend assessments, identify key gaps, and suggest advancements to better address critical issues related to changes in water resources that may threaten human development or the environment. Questions like, “Do we have less suitable drinking water now than we had 20 years ago? ” or “Are flood events more common now than they were in the past? ” prompted improvements in data, trend estimation methods, and modeling frameworks to track changes in, and better understand how land use and climate influence four water resources domains: surface and groundwater quantity and quality. However, continued advancement in trend assessments to better address issues related to changes in water availability is needed. Areas of need include more timely and efficient delivery of water resources trend results and improved capacity to estimate trends at unmonitored locations. Additional integration pieces include increased understanding of groundwater–surface water interactions, incorporation of both quantity and quality trends into water availability estimates, and the refinement of trend metrics to account for the competing needs of society and ecological integrity. Coupled with improved driver attribution studies, these components will better inform current and future water resources management.
mReFinED: An Efficient End-to-End Multilingual Entity Linking System

End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Prior efforts assume that entity mentions are given and skip the entity mention detection step due to a lack of high-quality multilingual training corpora. To overcome this limitation, we propose mReFinED, the first end-to-end MEL model. Additionally, we propose a bootstrapping mention detection framework that enhances the quality of training corpora. Our experimental results demonstrated that mReFinED outperformed the best existing work in the end-to-end MEL task while being 44 times faster.
Adaptive Textual Label Noise Learning based on Pre-trained Models

The label noise in real-world scenarios is unpredictable and can even be a mixture of different types of noise. To meet this challenge, we develop an adaptive textual label noise learning framework based on pre-trained models, which consists of an adaptive warm-up stage followed by a hybrid training stage. Specifically, an early stopping method, relying solely on the training set, is designed to dynamically terminate the warm-up process based on the model’s fit level to different noise scenarios. The hybrid training stage incorporates several generalization strategies to gradually correct mislabeled instances, thereby making better use of noisy data. Experiments on multiple datasets demonstrate that our approach performs on-par with or even better than the state-of-the-art methods in various noise scenarios, including scenarios with the mixture of multiple types of noise.
Etiology and immediate drug hypersensitivity in acute urticaria: a retrospective study

Background: Acute urticaria is an immune mediated or non-immune reaction lasting for less than 6 weeks. This can be caused by food, drugs, infections, physical contact, insect bite but majority are reported to be idiopathic. Theoretically, almost any drug can cause urticaria especially antibiotics like penicillin. We carried out this study to assess the various etiologial factors and to assess the immediate hypersensitivity reaction to various drugs in acute urticaria.Methods: We reviewed the medical records of patients with acute urticaria in dermatology department from 2016 to 2018 (2 years) and recorded the demographic details, history of suspected cause, medications and intradermal drug test report in a standard proforma.Results: There were 154 cases of acute urticaria. Mean age of these cases were 36.48±11.37 years. 13.6% had associated angioedema. Mean duration of urticaria was 21.74±7.92 days. 40.9% cases were labelled idiopathic. Food accounted for 29.2% of the cases, drugs for 23.4% and infection for 5.2%. The rest 1.3% were due to insect bite. Intradermal drug test report showed the following frequency of positive reaction: diclofenac>ciprofloxacin= piroxicam>paracetamol=ketamine=diazepam>promethazine=atracurium.Conclusions: Most cases of acute urticaria were idiopathic whereas food, drugs, infections and insect bite were the specific causes. Fish among food, acetaminophen among drugs and streptococci among infections were most responsible agents. Immediate hypersensitivity to drugs in intradermal test were maximum for NSAIDs especially diclofenac and piroxicam followed by antibiotic namely ciprofloxacin in patients with acute urticaria.
Antifungal susceptibility testing following the CLSI M27 document, along with the measurement of MFC/MIC ratio, could be the optimal approach to detect amphotericin B resistance in Clavispora (Candida) lusitaniae. Susceptibility patterns of contemporary isolates of this species

ABSTRACT Antifungal susceptibility testing (AST) is crucial in clinical settings to guide appropriate therapy. Nevertheless, discrepancies between treatment response and some results still persist, particularly in detecting resistance to amphotericin B (AMB) in Clavispora (Candida) lusitaniae. This study aimed to assess the susceptibility patterns of 48 recent isolates of C. lusitaniae to 9 antifungal agents and explore the feasibility of using a CLSI reference-based method to identify AMB resistance. Microdilution techniques revealed a wide range of minimal inhibitory concentration (MIC) values for azole antifungals, while echinocandins and AMB exhibited a narrow range of MIC values, with all strains considered wild-type for the tested polyene and echinocandins. However, when agar diffusion (ellipsometry) was employed for AST, certain strains displayed colonies within the inhibition ellipse, indicating potential resistance. Interestingly, these strains did not respond to AMB treatment and were isolated during AMB treatment (breakthrough). Moreover, the evaluation of AMB minimum fungicidal concentrations (MFCs) indicated that only the strains with colonies inside the ellipse had MFC/MIC ratios ≥ 4, suggesting reduced fungicidal activity. In conclusion, this study confirms the effectiveness of ellipsometry with RPMI-1640 2% glucose agar for detecting AMB resistance in C. lusitaniae. Additionally, the proposed approach of culturing “clear” wells in the microdilution method can aid in uncovering resistant strains. The findings highlight the importance of appropriate AST methods to guide effective treatment strategies for deep-seated candidiasis caused by C. lusitaniae. Further collaborative studies are warranted to validate these findings and improve the detection of AMB clinical resistance.
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation

Graph-to-text (G2T) generation takes a graph as input and aims to generate a fluent and faithful textual representation of the information in the graph. The task has many applications, such as dialogue generation and question answering. In this work, we investigate to what extent the G2T generation problem is solved for previously studied datasets, and how proposed metrics perform when comparing generated texts. To help address their limitations, we propose a new metric that correctly identifies factual faithfulness, i.e., given a triple (subject, predicate, object), it decides if the triple is present in a generated text. We show that our metric FactSpotter achieves the highest correlation with human annotations on data correctness, data coverage, and relevance. In addition, FactSpotter can be used as a plug-in feature to improve the factual faithfulness of existing models. Finally, we investigate if existing G2T datasets are still challenging for state-of-the-art models. Our code is available online: https: //github.com/guihuzhang/FactSpotter .
Relationship between bulk mineralogy and induced polarisation responses in iron oxide-copper-gold and porphyry copper mineralisation, northern Chile

We have studied the correlation between bulk mineralogy and induced polarisation (IP) responses in iron oxide-copper-gold (IOCG) and porphyry copper mineralised systems in northern Chile. Twelve drillholes (> 5000 m) that intersect IP sections were mapped and sampled to obtain geological characteristics and to quantify metallic minerals concentration in ore bodies. Geological parameters and modelled geophysical responses (electrical chargeability and resistivity) were compared using qualitative and quantitative criteria. Data analyses show that bulk sulphide concentration is the major factor that explains variations in the IP effect, even for IOCG ore bodies associated with magnetite mineralisation (up to 5 vol. %). Electrical chargeability exhibits a direct, but non-linear proportionality with sulphides content, while no clear trend is observed when chargeability is compared to magnetite concentration. In drillhole segments where macroscopic sulphides were not mapped, but IP results suggested high chargeabilities, petrographic studies revealed at least 1 vol. % of pyrite occurring as micro crystals (< 0.125 mm). The apparent resistivity data do not show any direct relationship with chargeability or sulphide contents. We have studied the correlation between bulk mineralogy and induced polarisation (IP) responses in iron oxide-copper-gold (IOCG) and porphyry copper concealed mineralised systems in northern Chile. Data analyses show that bulk sulphide concentration is the first order factor that explains variation in the IP effect, even for the case of magnetite-related IOCG mineralisation.
Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data

Code-switching is a common phenomenon in multilingual communities and is often used on social media. However, sentiment analysis of code-switched data is a challenging yet less explored area of research. This paper aims to develop a sentiment analysis sys-tem for code-switched data. In this paper, we present a novel approach combining two trans-formers using logits of their output and feeding them to a neural network for classification. We show the efficacy of our approach using two benchmark datasets, viz. , English-Hindi (En-Hi), and English-Spanish (En-Es) availed by Microsoft GLUECoS. Our approach results in an F 1 score of 73 . 66 % for En-Es and 61 . 24 % for En-Hi, significantly higher than the best model reported for the GLUECoS benchmark dataset.
Lightweight Acquisition and Ranging of Flows in the Data Plane

As networks get more complex, the ability to track almost all the flows is becoming of paramount importance. This is because we can then detect transient events impacting only a subset of the traffic. Solutions for flow monitoring exist, but it is getting very difficult to produce accurate estimations for every tuple given the memory constraints of commodity programmable switches. Indeed, as networks grow in size, more flows have to be tracked, increasing the number of tuples to be recorded. At the same time, end-host virtualization requires more specific flowIDs, enlarging the memory cost for every single entry. Finally, the available memory resources have to be shared with other important functions as well (e.g., load balancing, forwarding, ACL). To address those issues, we present FlowLiDAR (Flow Lightweight Detection and Ranging), a new solution that is capable of tracking almost all the flows in the network while requiring only a modest amount of data plane memory which is not dependent on the size of flowIDs. We implemented the scheme in P4, tested it using real traffic from ISPs and compared it against four state-of-the-art solutions: FlowRadar, NZE, PR-sketch, and Elastic Sketch. While those can only reconstruct up to 60% of the tuples, FlowLiDAR can track 98.7% of them with the same amount of memory.
Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation

Parameter-efficient fine-tuning (PEFT) methods have provided an effective way for adapting large vision-language models to specific tasks or scenarios. Typically, they learn a very small scale of parameters for pre-trained models in a white-box formulation, which assumes model architectures to be known and parameters to be accessible. However, large models are often not open-source due to considerations of preventing abuse or commercial factors, hence posing a barrier to the deployment of white-box PEFT methods. To alleviate the dependence on model accessibility, we introduce collaborative black-box tuning (CBBT) for both textual prompt optimization and output feature adaptation for black-box models. Specifically, considering that the backpropagation gradients are blocked, we approximate the gradients of textual prompts by analyzing the predictions with perturbed prompts. Secondly, a lightweight adapter is deployed over the output feature of the inaccessible model, further facilitating the model adaptation process. Empowered with these designs, our CBBT is extensively evaluated on eleven downstream benchmarks and achieves remarkable improvements compared to existing black-box VL adaptation methods. Code is released at https://github.com/guozix/cbbt.
Evolution of the effectiveness of stone bunds and trenches in reducing runoff and soil loss in the semi-arid Ethiopian highlands

Soil and Water Conservation (SWC) structures, in particular stone bunds and conservation trenches, have been extensively installed in Tigray since the 1980's. As the effectiveness of stone bunds and trenches in reducing runoff and soil loss depends on their retention capacities, it can be expected that this effectiveness declines over time due to infilling with sediment. However, little is known about the rate of this decline during subsequent years. We therefore assessed the effectiveness of SWC structures for two land use types, three slope classes and during three consecutive rainy seasons. Rainfall, runoff and soil loss were measured using 21 large (600-1,000 m(2)) runoff plots at Mayleba catchment. Results show that all studied SWC structures are more effective in reducing soil loss than runoff. Conservation trenches are generally more effective in reducing runoff and soil loss than stone bunds. However, due to their infilling with sediment, their effectiveness quickly declines over time. By the end of the third rainy season, their effectiveness was reduced to about one third of their initial effectiveness. The effectiveness of stone bunds remained fairly constant during three consecutive rainy seasons. These findings have important implications, as they demonstrate that many of the installed SWC structures (especially in rangelands) are only very effective for short periods (one to two years). Regular sediment removal from conservation trenches is therefore crucial to preserve their effectiveness over longer periods.
The Mycobacterium tuberculosis MmpL3 inhibitor MSU-43085 is active in a mouse model of infection

ABSTRACT Addressing drug resistance in Mycobacterium tuberculosis (Mtb) requires the development of new drugs with new targets. We previously identified 13 MmpL3 inhibitors from a screen of a small library of Mtb growth inhibitors. In this report, we describe the biological activities of a set of HC2099 analogs active against Mtb in vitro, intracellular Mtb, as well as M. abscessus and M. avium. Pharmacokinetic (PK) studies identified MSU-43085 as orally bioavailable with a short half-life. High dosing (100 mg/kg) saturates clearance processes, enabling efficacy studies. The results of an in vivo efficacy study found that MSU-43085 was active against Mtb in an acute murine TB infection model but lacked activity in a chronic murine TB infection model. The results of this study serve as a proof of concept for this series and support further optimizations of the HC2099 series of MmpL3 inhibitors. IMPORTANCE MmpL3 is a protein that is required for the survival of bacteria that cause tuberculosis (TB) and nontuberculous mycobacterial (NTM) infections. This report describes the discovery and characterization of a new small molecule, MSU-43085, that targets MmpL3 and is a potent inhibitor of Mycobacterium tuberculosis (Mtb) and M. abscessus survival. MSU-43085 is shown to be orally bioavailable and efficacious in an acute model of Mtb infection. However, the analog is inactive against Mtb in chronically infected mice. Pharmacokinetic and metabolite identification studies identified in vivo metabolism of MSU-43085, leading to a short half-life in treated mice. These proof-of-concept studies will guide further development of the MSU-43085 series for the treatment of TB or NTM infections. MmpL3 is a protein that is required for the survival of bacteria that cause tuberculosis (TB) and nontuberculous mycobacterial (NTM) infections. This report describes the discovery and characterization of a new small molecule, MSU-43085, that targets MmpL3 and is a potent inhibitor of Mycobacterium tuberculosis (Mtb) and M. abscessus survival. MSU-43085 is shown to be orally bioavailable and efficacious in an acute model of Mtb infection. However, the analog is inactive against Mtb in chronically infected mice. Pharmacokinetic and metabolite identification studies identified in vivo metabolism of MSU-43085, leading to a short half-life in treated mice. These proof-of-concept studies will guide further development of the MSU-43085 series for the treatment of TB or NTM infections.
New avenue of diagnostic stewardship: procedural stewardship for recurrent urinary tract infections in female patients

Abstract Introduction: Societal guidelines offer a weak recommendation to perform cystoscopy for female patients with recurrent urinary tract infections (rUTI) of advanced age and/or with high-risk features. These guidelines lack the support of robust data and are instead based on expert opinion. In this retrospective cohort study, we aim to determine the utility of cystoscopy in patients with and without high-risk features for rUTI. Materials and methods: We identified 476 women who underwent cystoscopy for the evaluation of rUTI at a single tertiary academic medical center from May 1, 2015 and March 15, 2021. Patients were excluded if they had a competing indication for cystoscopy. Risk factors, demographic information, cystoscopic findings, and patient outcomes were analyzed. Results: 192 (41.1%) were classified as having complicated UTI. We identified six patients (1.3%) with findings that prompted management to significantly impact patient outcomes. All six patients had high-risk features. 14 patients (3.0%) were found to have mucosal abnormalities prompting biopsy, three of which required general anesthesia. All 14 biopsies were ultimately benign. Conclusions: Our findings demonstrate a low diagnostic yield and increased risk exposure for women undergoing cystoscopy for the evaluation of complicated rUTI. Additionally, our observations support prior studies indicating that cystoscopy has limited utility in the evaluation of rUTI without high-risk features.
Neural Collision Fields for Triangle Primitives

We present neural collision fields as an alternative to contact point sampling in physics simulations. Our approach is built on top of a novel smoothed integral formulation for the contact surface patches between two triangle meshes. By reformulating collisions as an integral, we avoid issues of sampling common to many collision-handling algorithms. Because the resulting integral is difficult to evaluate numerically, we store its solution in an integrated neural collision field — a 6D neural field in the space of triangle pair vertex coordinates. Our network generalizes well to new triangle meshes without retraining. We demonstrate the effectiveness of our method by implementing it as a constraint in a position-based dynamics framework and show that our neural formulation successfully handles collisions in practical simulations involving both volumetric and thin-shell geometries.
Human–Artificial Intelligence Collaboration in Prediction: A Field Experiment in the Retail Industry

ABSTRACT This study investigates the role of human intervention in artificial intelligence/machine learning (AIML)-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. We theorize that prediction uncertainty and time horizon represent two critical determinants of forecast accuracy. Based on a field experiment involving AIML-driven demand forecasts approximately 1,888 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy. Our findings show that human intervention complements AIML-driven forecasts most effectively (augmentation) in predictions with long time horizons and low uncertainty. However human intervention is least likely to contribute to the effectiveness of AIML predictions (automation) in environments with short time horizons and high uncertainty. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations.
Structural, dielectric, impedance, and electric modulus characteristics of Y2CaCuO5 high-k dielectric ceramics

Abstract With the increasing trends of integrated circuit densification, device miniaturization, and exponential growth of microelectronic components there is a huge responsibility on the part of material researchers to derive, design, and develop new materials. Materials that can overcome the existing limitations in the present compounds available in the market or can expedite the process of material fabrication are of great interest. This feat can be achieved by the fabrication of new materials, and analysis of various characterizations viz. (structural, dielectric, transport behavior, and impedance spectroscopy). With this perspective, in this communication, a high permittivity dielectric material belonging to the Y-Ba-Cu-O ternary systems where barium is replaced by calcium (Y2CaCuO5) is synthesized using solid-state reaction route. X-ray diffraction, scanning electron micrograph, and energy-dispersive X-ray spectroscopic study confirm the phase and microstructure of the sample. The observed higher value of the dielectric constant is understood from Koop’s phenomenological theory and internal barrier layer capacitance model. The dispersive behavior of conductivity is interpreted by Jonscher’s power law. Overlapping large polaron tunneling model for conduction is observed. Impedance spectroscopy analysis reveals non-Debye type of relaxation with variable time constants. According to Nyquist plots; up to 250 °C only grain effect (bulk) is dominantly present; however, at higher temperatures, the contribution from both grain and grain boundary is seen.