Miracle-dz/UniFAR-FTUs huggingface dataset:
of Experimental Psychology: General Summarized Attribute Preferences Have Unique Antecedents and Consequences

People have ideas about the attributes (i.e., traits or characteristics that vary along a dimension) that they like in others (e.g., “ I like intelligence in a romantic partner ” ), and these ideas about liking are called summarized attribute preferences (Ledgerwood et al., 2018). But where do summarized preferences come from, and what do they predict? Across four studies, we examined how people form summarized attribute preferences and whether they predict situation selection. We showed participants a series of photographs of faces and assessed both their experienced liking for an attribute (or functional attribute preference ) as well as their inference about how much they liked the attribute in the abstract (their summarized attribute preference). Our results suggest that summarized attribute preferences — despite being (weakly) grounded in functional attribute preferences — were affected by incidental aspects of the context in which people learn about them (i.e., the overall likeability of the pool of faces). Furthermore, we observed a double dissociation in the predictive validity of summarized and functional attribute preferences: Whereas summarized attribute preferences predicted situation selection at a distance (e.g., whether to join a new dating website based on a description of it), functional attribute preferences predicted situation selection with experience (e.g., whether to join a new dating website after sampling it). We discuss theoretical and methodological implications for the interdisciplinary science of human evaluation.
Volatile transport into the Earth's mantle – Experimental studies on the deep nitrogen cycle and on the (F,OH) site in topaz

The ammonium analogues of the high-pressure potassium-bearing silicate phases K-hollandite, K-Si-wadeite, K-cymrite, and phengite were synthesized in the system (NH4)2O(−MgO)−Al2O3 − SiO2 −H2O (N(M)ASH) using multi-anvil and piston-cylinder equipment. Syntheses included NH4-hollandite [NH4AlSi3O8] at 12.3 GPa, 700 ◦C; NH4-Si-wadeite [(NH4)2Si4O9] at 10 GPa, 700 ◦C; NH4-cymrite [NH4AlSi3O8 ·H2O] at 7.8 GPa, 800 ◦C; and NH4-phengite [NH4(Mg0.5Al1.5)(Al0.5Si3.5)O10(OH)2] at 4 GPa, 700 ◦C. Run products were characterized by SEM, FTIR, and powder XRD with Rietveld refinements. Cell parameters of the new NH4 end-members were determined to a = 9.4234(9) Å, c = 2.7244(3) Å, V = 241.93(5) Å3 (NH4-hollandite); a = 6.726(1) Å, c = 9.502(3) Å, V = 372.3(1) Å3 (NH4-Si-wadeite); a = 5.3595(3) Å, c = 7.835(1) Å, V = 194.93(5) Å3 (NH4-cymrite). NH4-phengite consisted of a mixture of 1M, 2M1, 2M2, 3T , and 2Or polytypes. The most abundant polytype 2M1 has the cell dimensions a = 5.2195(9) Å, b = 9.049(3) Å, c = 20.414(8) Å, β = 95.65(3)◦, V = 959.5(5) Å3. All unit cell volumes are enlarged in comparison to the potassium analogues. Substitution of NH4 for K does not cause changes in space group symmetry. NH4 incorporation was confirmed by the appearance of NH4 vibration modes ν4 and ν3 occurring in the ranges of 1397–1459 and 3223–3333 cm−1, respectively. Ammonium in eclogite facies metasediments is mainly bound in micas and concentrations may reach up to a few thousand ppm. It can be stored to greater depths in high-pressure potassium silicates during ongoing subduction. This possibly provides an important mechanism for nitrogen and hydrogen transport into the deeper mantle.
An Optimized Clustering Algorithm based on Integration of Genetic Algorithm and Ant Colony Algorithm

Genetic algorithm (GA) has good fast global searching ability and ant colony algorithm has good distributed parallelism and positive feedback ability. In this paper, an optimized clustering algorithm was proposed by integrating these two algorithms and making full use of their advantages and characteristics. The proposed algorithm could make clustering analysis more effectively through the automatic evolution rate matching optimization algorithm of the GA. According to simulation experiment, this algorithm was far superior to common clustering algorithms in term of optimization capability and time performance
Monitoring MCS patients on the intensive care unit: integrating haemodynamic assessment, laboratory data, and imaging techniques for timely detection of deterioration and recovery

Abstract Monitoring of the patient supported with a temporary mechanical circulatory support (tMCS) is crucial in achieving the best possible outcome. Monitoring is a continuous and labour-intensive process, as cardiogenic shock (CS) patients can rapidly deteriorate and may require new interventions within a short time period. Echocardiography and invasive haemodynamic monitoring form the cornerstone of successful tMCS support. During monitoring, it is particularly important to ensure that adequate end-organ perfusion is achieved and maintained. Here, we provide a comprehensive overview of best practices for monitoring the CS patient supported by a micro-axial flow pump, veno-arterial extracorporeal membrane oxygenation, and both devices simultaneously (ECMELLA approach). It is a complex process that encompasses device control, haemodynamic control and stabilization, monitoring of interventions, and assessment of end-organ function. The combined, continuous, and preferably protocol-based approach of echocardiography, evaluation of biomarkers, end-organ assessment, and haemodynamic parameters is crucial in assessing this critically ill CS patient population.
Only human after all? a pre-registered study on gaze behavior and humanity attributions to people with facial difference

There is a great deal of indirect evidence suggesting that people with facial difference (FD) may be dehumanized. This research aimed to provide direct evidence of the dehumanization of people with FD based on the stigmatizing reactions they elicit. More precisely, previous findings revealed that the specific way people with FD are looked upon is related to the feelings of disgust they elicit. Since disgust fosters dehumanization, our aim was to confirm the modified pattern of visual attention towards people with FD and to determine whether it was also related to humanness perception. For that purpose, a preregistered eye-tracking study (N = 97) using a former experimental design extended to humanity attributions was conducted. This research replicates findings showing that the face of people with FD is explored differently in comparison with other human faces. However, the hypothesis that people with FD were given fewer humanity attributions was not supported. Therefore, the hypothesis of a "dehumanizing gaze" towards people with FD–beyond humanity-related attributions–is discussed in light of these findings.
“Should I Say Something?”: A Simulation Curriculum on Addressing Lapses in Professionalism to Improve Patient Safety

Introduction Medical students may witness lapses in professionalism but lack tools to effectively address such episodes. Current professionalism curricula lack opportunities to practice communication skills in addressing professionalism lapses. Methods We designed a simulation curriculum to introduce professionalism expectations, provide communication tools using elements of the Agency for Healthcare Research and Quality TeamSTEPPS program, and address observed professionalism lapses involving patient safety in hierarchical patient care teams. Students were surveyed on knowledge, skills, and attitude regarding professionalism before, immediately after, and 6 months after participation. Results Of 253 students, 70 (28%) completed baseline and immediate postsurveys, and 39 (15%) completed all surveys. In immediate postsurveys, knowledge of communication tools (82% to 94%, p = .003) and empowerment to address residents (19% to 44%, p = .001) and attendings (15% to 39%, p < .001) increased. At 6 months, 96% of students reported witnessing a professionalism lapse. Discussion The curriculum was successful in reported gains in knowledge of communication tools and empowerment to address professionalism lapses, but few students reported using the techniques to address witnessed lapses in real life.
Inflammatory cytokines as mediators of retinal endothelial barrier dysfunction in non‐infectious uveitis

Characterised by intraocular inflammation, non‐infectious uveitis includes a large group of autoimmune and autoinflammatory diseases that either involve the eye alone or have both ocular and systemic manifestations. When non‐infectious uveitis involves the posterior segment of the eye, specifically the retina, there is substantial risk of vision loss, often linked to breakdown of the inner blood‐retinal barrier. This barrier is formed by non‐fenestrated retinal vascular endothelial cells, reinforced by supporting cells that include pericytes, Müller cells and astrocytes. Across the published literature, a group of inflammatory cytokines stand out as prominent mediators of intraocular inflammation, with effects on the retinal endothelium that may contribute to breakdown of the inner blood‐retinal barrier, namely tumour necrosis factor (TNF)‐α, interleukin (IL)‐1β, IL‐6, IL‐8, IL‐17 and chemokine C‐C motif ligand (CCL)2. This article reviews the function of each cytokine and discusses the evidence for their involvement in retinal endothelial barrier dysfunction in non‐infectious uveitis, including basic laboratory investigations, studies of ocular fluids collected from patients with non‐infectious uveitis, and results of clinical treatment trials. The review also outlines gaps in knowledge in this area. Understanding the disease processes at a molecular level can suggest treatment alternatives that are directed against appropriate biological targets to protect the posterior segment of eye and preserve vision in non‐infectious uveitis.
Recognizing patients as candidates for temporary mechanical circulatory support along the spectrum of cardiogenic shock

Abstract A growing body of evidence indicates that the benefits of temporary mechanical circulatory support (tMCS) in patients with cardiogenic shock (CS) is dependent on its aetiology and timing of implantation. As such, appropriate diagnosis, screening, selection, and treatment is crucial to achieving good outcomes with tMCS. Here, the latest guidance on CS phenotypes and diagnostics for correctly identifying tMCS candidates is discussed. This includes comprehensive overviews of patient screening with attention given to differences in CS presentation between the sexes, and contraindications for mechanical circulatory support.
Dike emplacement, footwall rotation, and the transition from magmatic to tectonic extension in the Whipple Mountains metamorphic core complex, southeastern California

The Chambers Well dike swarm and associated plutonic/volcanic rocks in the western footwall of the Whipple Detachment Fault (WDF) provide key insight into the evolution of this metamorphic core complex. New structural and geochronologic data suggest that the western 12–15 km of exposed footwall is steeply tilted to the SW, providing a cross‐sectional view of the upper crust, from the Miocene erosion surface to the top of the coeval mylonitization. Ages and compositions of dikes are indistinguishable from adjacent thick volcanic successions. Several kilometers of early Miocene extension (~20.5 to 19.0 Ma) were accommodated by magmatic accretion but transitioned to rapid extensional faulting and tilting at 19.0–18.5 Ma. The subhorizontal WDF in this area initiated as a northeast dipping high‐angle (50–60°) normal fault that breached the surface locally, not in a breakaway tens of kilometers to the west. Large‐scale tilting and differential uplift of the western footwall was in part coeval with mylonitization and dike emplacement and was accomplished by block rotation in the hanging wall of additional normal faults, isostatic uplift, and flow of lower crust from beneath less extended regions to the west. The WDF is likely a composite surface with a western segment that had ceased moving by ~18.5 Ma, cut by successively younger and steeper fault(s) to the east. Perhaps, the most important difference between seismogenic high‐angle normal faults and low‐angle “detachment faults” characteristic of metamorphic core complexes is one of magnitude and rate of total accumulated slip, not of initial failure conditions.
Derivation, validation, and comparison of a new prognostic scoring system for acute lower gastrointestinal bleeding

Abstract Objectives Lower gastrointestinal bleeding is a common presentation with little data concerning risk factors for adverse outcomes. The aim was to derive and validate a scoring system to stratify risk in lower gastrointestinal bleeding and compare it to the Oakland score. Methods A total of 2385 consecutive patients (mean age 65 years, 1140 males) were used to derive the score using multivariate logistic regression modeling then internally and externally validated. The Oakland score was applied and area under receiver operating characteristic (AUROC) curves were calculated and compared. A score of <1 was compared with an Oakland score of <9 to assess 30‐day rebleeding and mortality rates. Results Rebleeding was associated with age, inpatient bleeding, syncope, malignancy, tachycardia, hypotension, lower hemoglobin and mortality with age, inpatient bleeding, liver/gastrointestinal disease, tachycardia, and hypotension. The area under the receiver operating characteristic curves was 0.742 for rebleeding and 0.802 for mortality. A score <1 was associated with rebleeding (0.0%–2.2%) and mortality (0%). The Oakland score had a significantly lower area under the receiver operating characteristic curve for rebleeding of 0.687 but not for mortality; 0.757. A score <1 was associated with a lower 30‐day rebleeding risk compared to an Oakland score <9 (4/379 vs. 15/355, p = 0.009) but not mortality (0/365 vs. 1/355, p = 0.493). Conclusions Our score predicts 30‐day rebleeding and mortality rate with low scores associated with very low risk. The Aberdeen score is superior to the Oakland score for predicting rebleeding. Prospective evaluation of both scores is required.
Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

In response to the ongoing global health problem caused by malaria, especially in resource-limited settings, digital microscopy must be improved to overcome the limitations associated with manual microscopy. In order to present a malaria diagnosis method that is not only accurate at the cell level but also clinically performs well, improvements in deep learning algorithms and consistent staining results are necessary. The device employs a solid hydrogel staining method for consistent blood film preparation, eliminating the need for complex equipment and liquid reagent maintenance. By leveraging deformable staining patches, the miLab ensures consistent, high-quality reproducible blood films can be made across various hematocrits. The optical system captures autofocus images of stained blood cells, enabling the embedded deep-learning algorithm to detect and classify malaria parasites, achieving a high correlation with manual microscopy results. This innovation not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission, revolutionizing the landscape of on-site malaria diagnostics. Clinical validation in Malawi demonstrates an overall percent agreement of 91.82%, highlighting the miLab's potential as a reliable and efficient tool for decentralized malaria diagnosis.
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches such as differential privacy (DP). Methods of the first class achieve perfect output accuracy but reveal some private information, while methods from the second class provide privacy against the strongest adversary at the cost of a loss of accuracy. In this paper, we propose a general approach named adaptive differentially quantized subspace perturbation (ADQSP) which combines quantization schemes with so-called subspace perturbation. Although not relying on cryptographic primitives, the proposed approach enjoys the benefits of both accuracy-prioritized and privacy-prioritized methods and is able to unify them. More specifically, we show that by varying a single quantization parameter the proposed method can vary between SMPC-type performances and DP-type performances. Our results show the potential of exploiting traditional distributed signal processing tools for providing cryptographic guarantees. In addition to a comprehensive theoretical analysis, numerical validations are conducted to substantiate our results.
Mathematical Language Models: A Survey

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics. This paper conducts a comprehensive survey of mathematical LMs, systematically categorizing pivotal research endeavors from two distinct perspectives: tasks and methodologies. The landscape reveals a large number of proposed mathematical LLMs, which are further delineated into instruction learning, tool-based methods, fundamental CoT techniques, and advanced CoT methodologies. In addition, our survey entails the compilation of over 60 mathematical datasets, including training datasets, benchmark datasets, and augmented datasets. Addressing the primary challenges and delineating future trajectories within the field of mathematical LMs, this survey is positioned as a valuable resource, poised to facilitate and inspire future innovation among researchers invested in advancing this domain.
Approximation algorithms for k-median problems on complex networks: theory and practice

Finding the k-medianin a network involves identifying a subset of k vertices that minimize the total distance to all other vertices in a graph. This problem has been extensively studied in computer science, graph theory, operations research, and numerous areas due to its significance in a wide range of applications. While known to be computationally challenging (NP-hard) several approximation algorithms have been proposed, most with high-order polynomial-time complexity. However, the graph topology of complex networks with heavy-tailed degree distributions present characteristics that can be exploited to yield custom-tailored algorithms. We compare eight algorithms specifically designed for complex networks and evaluate their performance based on accuracy and efficiency for problems of varying sizes and application areas. Rather than relying on a small number of problems, we conduct over 16,000 experiments covering a wide range of network sizes and k-median{} values. While individual results vary, a few methods provide consistently good results. We draw general conclusions about how algorithms perform in practice and provide general guidelines for solutions.
Lagrangian Liouville models of multiphase flows with randomly forced inertial particles

Eulerian-Lagrangian models of particle-laden (multiphase) flows describe fluid flow and particle dynamics in the Eulerian and Lagrangian frameworks respectively. Regardless of whether the flow is turbulent or laminar, the particle dynamics is stochastic because the suspended particles are subjected to random forces. We use a polynomial chaos expansion (PCE), rather than a postulated constitutive law, to capture structural and parametric uncertainties in the particles' forcing. The stochastic particle dynamics is described by a joint probability density function (PDF) of a particle's position and velocity and random coefficients in the PCE. We deploy the method of distributions (MoD) to derive a deterministic (Liouville-type) partial-differential equation for this PDF. We reformulate this PDF equation in a Lagrangian form, obtaining PDF flow maps and tracing events and their probability in the phase space. That is accomplished via a new high-order spectral scheme, which traces, marginalizes and computes moments of the high-dimensional joint PDF and comports with high-order carrier-phase solvers. Our approach has lower computational cost than either high-order Eulerian solvers or Monte Carlo methods, is not subjected to a CFL condition, does not suffer from Gibbs oscillations and does not require (order-reducing) filtering and regularization techniques. These features are demonstrated on several test cases.
Restorebot: Towards an Autonomous Robotics Platform for Degraded Rangeland Restoration

Degraded rangelands undergo continual shifts in the appearance and distribution of plant life. The nature of these changes however is subtle: between seasons seedlings sprout up and some flourish while others perish, meanwhile, over multiple seasons they experience fluctuating precipitation volumes and can be grazed by livestock. The nature of these conditioning variables makes it difficult for ecologists to quantify the efficacy of intervention techniques under study. To support these observation and intervention tasks, we develop RestoreBot: a mobile robotic platform designed for gathering data in degraded rangelands for the purpose of data collection and intervention in order to support revegetation. Over the course of multiple deployments, we outline the opportunities and challenges of autonomous data collection for revegetation and the importance of further effort in this area. Specifically, we identify that localization, mapping, data association, and terrain assessment remain open problems for deployment, but that recent advances in computer vision, sensing, and autonomy offer promising prospects for autonomous revegetation.
Uncertainty Visualization via Low-Dimensional Posterior Projections

In ill-posed inverse problems, it is commonly desirable to obtain insight into the full spectrum of plausible solutions, rather than extracting only a single reconstruction. Information about the plausible solutions and their likelihoods is encoded in the posterior distribution. However, for high-dimensional data, this distribution is challenging to visualize. In this work, we introduce a new approach for estimating and visualizing posteriors by employing energy-based models (EBMs) over low-dimensional subspaces. Specifically, we train a conditional EBM that receives an input measurement and a set of directions that span some low-dimensional subspace of solutions, and outputs the probability density function of the posterior within that space. We demonstrate the effectiveness of our method across a diverse range of datasets and image restoration problems, showcasing its strength in uncertainty quantification and visualization. As we show, our method outperforms a baseline that projects samples from a diffusion-based posterior sampler, while being orders of magnitude faster. Furthermore, it is more accurate than a baseline that assumes a Gaussian posterior.