Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli

A core goal of visual neuroscience is to predict human perceptual performance from natural signals. Performance in any natural task can be limited by at least three sources of uncertainty: stimulus variability, internal noise, and suboptimal computations. Determining the relative importance of these factors has been a focus of interest for decades but requires methods for predicting the fundamental limits imposed by stimulus variability on sensory-perceptual precision. Most successes have been limited to simple stimuli and simple tasks. But perception science ultimately aims to understand how vision works with natural stimuli. Successes in this domain have proven elusive. Here, we develop a model of humans based on an image-computable (images in, estimates out) Bayesian ideal observer. Given biological constraints, the ideal optimally uses the statistics relating local intensity patterns in moving images to speed, specifying the fundamental limits imposed by natural stimuli. Next, we propose a theoretical link between two key decision-theoretic quantities that suggests how to experimentally disentangle the impacts of internal noise and deterministic suboptimal computations. In several interlocking discrimination experiments with three male observers, we confirm this link and determine the quantitative impact of each candidate performance-limiting factor. Human performance is near-exclusively limited by natural stimulus variability and internal noise, and humans use near-optimal computations to estimate speed from naturalistic image movies. The findings indicate that the partition of behavioral variability can be predicted from a principled analysis of natural images and scenes. The approach should be extendable to studies of neural variability with natural signals. SIGNIFICANCE STATEMENT Accurate estimation of speed is critical for determining motion in the environment, but humans cannot perform this task without error. Different objects moving at the same speed cast different images on the eyes. This stimulus variability imposes fundamental external limits on the human ability to estimate speed. Predicting these limits has proven difficult. Here, by analyzing natural signals, we predict the quantitative impact of natural stimulus variability on human performance given biological constraints. With integrated experiments, we compare its impact to well-studied performance-limiting factors internal to the visual system. The results suggest that the deterministic computations humans perform are near optimal, and that behavioral responses to natural stimuli can be studied with the rigor and interpretability defining work with simpler stimuli.
The size-resolved cloud condensation nuclei (CCN) activity and its prediction based on aerosol hygroscopicity and composition in the Pearl Delta River (PRD) region during wintertime 2014

Abstract. A hygroscopic tandem differential mobility analyzer (HTDMA), a scanning mobility cloud condensation nuclei (CCN) analyzer (SMCA), and an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) were used to, respectively, measure the hygroscopicity, condensation nuclei activation, and chemical composition of aerosol particles at the Panyu site in the Pearl River Delta region during wintertime 2014. The distribution of the size-resolved CCN at four supersaturations (SSs of 0.1 %, 0.2 %, 0.4 %, and 0.7 %) and the aerosol particle size distribution were obtained by the SMCA. The hygroscopicity parameter κ (κCCN, κHTDMA, and κAMS) was, respectively, calculated based upon the SMCA, HTDMA, and AMS measurements. The results showed that the κHTDMA value was slightly smaller than the κCCN one at all diameters and for particles larger than 100 nm, and the κAMS value was significantly smaller than the others (κCCN and κHTDMA), which could be attributed to the underestimated hygroscopicity of the organics (κorg). The activation ratio (AR) calculated from the growth factor – probability density function (Gf-PDF) without surface tension correction was found to be lower than that from the CCN measurements, due most likely to the uncorrected surface tension (σs∕a) that did not consider the surfactant effects of the organic compounds. We demonstrated that better agreement between the calculated and measured ARs could be obtained by adjusting σs∕a. Various schemes were proposed to predict the CCN number concentration (NCCN) based on the HTDMA and AMS measurements. In general, the predicted NCCN agreed reasonably well with the corresponding measured ones using different schemes. For the HTDMA measurements, the NCCN value predicted from the real-time AR measurements was slightly smaller (∼6.8 %) than that from the activation diameter (D50) method due to the assumed internal mixing in the D50 prediction. The NCCN values predicted from bulk chemical composition of PM1 were higher (∼11.5 %) than those from size-resolved composition measured by the AMS because a significant fraction of PM1 was composed of inorganic matter. The NCCN values calculated from AMS measurement were underpredicted at 0.1 % and 0.2 % supersaturations, which could be due to underestimation of κorg and overestimation of σs∕a. For SS values of 0.4 % and 0.7 %, slight overpredicted NCCN values were found because of the internal mixing assumption. Our results highlight the need for accurately evaluating the effects of organics on both the hygroscopic parameter κ and the surface tension σ in order to accurately predict CCN activity.
The size resolved cloud condensation nuclei ( CCN ) 1 activity and its prediction based on aerosol 2 hygroscopicity and composition in the Pearl Delta River 3 ( PRD ) Region during wintertime 2014 4

A hygroscopicity-tandem differential mobility analyzer (H-TDMA), a scanning mobility 19 CCN analyzer (SMCA), and an aerodyne high resolution time-of-flight aerosol mass spectrometer 20 (HR-ToF-AMS) were used to respectively measure the hygroscopicity, condensation nuclei activation, 21 and chemical composition of aerosol particles at the Panyu site in the Pearl River Region during 22 wintertime 2014. The distribution of the size-resolved cloud condensation nuclei (CCN) at four 23 supersaturations (SS=0.1%, 0.2%, 0.4%, and 0.7%) and the aerosol particle size distribution were 24 obtained by the SMCA. The hygroscopicity parameter κ (κCCN, κH-TDMA, and κAMS) was respectively 25 2 calculated based upon the SMCA, H-TMDA, and AMS measurements. The results showed that the 1 κH-TDMA value was slightly smaller than the κCCN one at all diameters and for particles larger than 100 2 nm and the κAMS value was significantly smaller than the others (κCCN, and κH-TDMA), which could be 3 attributed to the underestimated hygroscopicity of the organics (κorg). The activation ratio (AR) 4 calculated from the growth factor – probability density function (Gf-PDF) without surface tension 5 correction was found to be lower than that from the CCN measurements, due most likely to the 6 uncorrected surface tension (σs/a) that did not consider the surfactant effects of the organic compounds. 7 We demonstrated that better agreement between the calculated and measured AR could be obtained by 8 adjusting σs/a. Various schemes were proposed to predict the CCN number concentration (NCCN) based 9 on the H-TDMA and AMS measurements. In general, the predicted NCCN agreed reasonably well with 10 the corresponding measured ones using different schemes. For the H-TDMA measurements, the NCCN 11 value predicted from the real time AR measurements was slightly smaller (~6.8%) than that from the 12 activation diameter (D50) method due to the assumed internal mixing in the D50 prediction. The NCCN 13 values predicted from bulk chemical composition of PM1 were higher (~11.5%) than those from 14 size-resolved composition measured by the AMS because a significant fraction of PM1 was composed 15 of inorganic matter. The NCCN values calculated from AMS measurement were under-predicted at 0.1% 16 and 0.2% supersaturations, which could be due to underestimate of κorg and overestimate of σs/a. For 17 SS=0.4% and 0.7%, slight over-predicted NCCN values were found because of the internal mixing 18 assumption. Our results highlight the need for accurately evaluating the effects of organics on both the 19 hygroscopic parameter κ and the surface tension σ in order to accurately predict CCN activity. 20 21
Radiative feedbacks of dust in snow over eastern Asia in CAM4-BAM

Abstract. Dust in snow on the Tibetan Plateau (TP) could reduce the visible snow albedo by changing surface optical properties and removing the snow cover through increased snowmelt, which leads to a significant positive radiative forcing and remarkably alters the regional energy balance and the eastern Asian climate system. This study extends our previous investigation in dust–radiation interactions to investigate the dust-in-snow radiative forcing (SRF) and its feedbacks on the regional climate and the dust cycle over eastern Asia through the use of the Community Atmosphere Model version 4 with a Bulk Aerosol Model parameterizations of the dust size distribution (CAM4-BAM). Our results show that SRF increases the eastern Asian dust emissions significantly by 13.7 % in the spring, countering a 7.6 % decrease in the regional emissions by the dust direct radiative forcing (DRF). SRF also remarkably affects the whole dust cycle, including transport and deposition of dust aerosols over eastern Asia. The simulations indicate an increase in dust emissions of 5.1 %, due to the combined effect of DRF and SRF. Further analysis reveals that these results are mainly due to the regional climatic feedbacks induced by SRF over eastern Asia. By reducing the snow albedo over the TP, the dust in snow mainly warms the TP and influences its thermal effects by increasing the surface sensible and latent heat flux, which in turn increases the aridity and westerly winds over northwestern China and affects the regional dust cycle. Additionally, the dust in snow also accelerates the snow-melting process, reduces the snow cover and then expands the eastern Asian dust source region, which results in increasing the regional dust emissions. Hence, a significant feature of SRF on the TP is the creation of a positive feedback loop that affects the dust cycle over eastern Asia.
Where Should I Park? Real-time Detection of Parking Space Occupancy

In the face of escalating urbanization and its associated parking challenges, this paper proposes an innovative approach to enhance parking lot management and optimize space utilization, thereby mitigating urban parking difficulties. We initially experimented with the Yolo-Fastest V2 algorithm for real-time parking space detection. However, we observed that Yolo-Fastest V2 had shortcomings, particularly in terms of incomplete detection accuracy. This led us to develop a more robust approach using a modified ResNet model with transfer learning. This approach, diverging from traditional sensor-based methods, offers improved accuracy and cost-efficiency with less data need. Our comparative analysis between ResNet and YOLOv8 demonstrates the former’s superior adaptability and performance in urban settings. This development significantly contributes to the smart city framework, improving parking efficiency and potentially easing traffic congestion.
Rapid Di ﬀ erentiation of Host and Parasitic Exosome Vesicles Using Micro ﬂ uidic Photonic Crystal Biosensor

: Parasite extracellular vesicles (EVs) are potential biomarkers that could be exploited for the diagnosis of infectious disease. This paper reports a rapid bioassay to discriminate parasite and host EVs. The EV detection assay utilizes a label-free photonic crystal (PC) biosensor to detect the EVs using a host-speci ﬁ c transmembrane protein (CD63), which is present on EV secreted by host cells (modeled by murine macrophage cell line J774A.1) but is not expressed on EV secreted by parasitic nematodes such as the gastrointestinal nematode Ascaris suum . The surface of PC is functionalized to recognize CD63, and is sensitive to the changes in refractive index caused by the immobilization of EVs. The biosensor demonstrates a detection limit of 2.18 × 10 9 EVs/mL and a capability to characterize the a ﬃ nity constants of antibody-host EV bindings. The discrimination of murine host EVs from parasite EVs indicates the capability of the sensor to di ﬀ erentiate EVs from di ﬀ erent origins. The label-free, rapid EV assay could be used to detection parasite infection and facilitate the exosome-based clinic diagnosis and exosome research.
Evaluating in-memory caching strategies for distributed web services

Caching is an incredibly common component in modern computer engineering. Present everywhere, caches beneﬁt greatly from domain-speciﬁc knowledge. This thesis, in collaboration with Antura AB, targets caching between distributed web services and their database. The state-of-the-art in this caching domain is to have a shared, networked, in-memory, key-value store, such as Redis. This thesis had two goals. Firstly, to implement an alternative to this shared cache system using caches distributed on each server of the web application. Secondly, to implement a test suite for comparing these cache systems. A common interface was created for these cache systems and both were implemented using F#. A test suite was then created using a sample distributed web application and simulated user requests. The user simulation covered diﬀerent read-write ratios, domain sizes, and behavior with respect to a commonly requested “shared” domain. The results of the tests found cases where either cache system performed better on average. Although the shared cache system performed signiﬁcantly better as the degree of distribution increased.
Interplay of Human and AI Solvers on a Planning Problem

With the rapidly growing use of Multi-Agent Systems (MASs), which can exponentially increase the system complexity, the problem of planning a mission for MASs became more intricate. In some MASs, human operators are still involved in various decision-making processes, including manual mission planning, which can be an ineffective approach for any non-trivial problem. Mission planning and re-planning can be represented as a combinatorial optimization problem. Computing a solution to these types of problems is notoriously difficult and not scalable, posing a challenge even to cutting-edge solvers. As time is usually considered an essential resource in MASs, automated solvers have a limited time to provide a solution. The downside of this approach is that it can take a substantial amount of time for the automated solver to provide a sub-optimal solution. In this work, we are interested in the interplay between a human operator and an automated solver and whether it is more efficient to let a human or an automated solver handle the planning and re-planning problems, or if the combination of the two is a better approach. We thus propose an experimental setup to evaluate the effect of having a human operator included in the mission planning and re-planning process. Our tests are performed on a series of instances with gradually increasing complexity and involve a group of human operators and a metaheuristic solver based on a genetic algorithm. We measure the effect of the interplay on both the quality and structure of the output solutions. Our results show that the best setup is to let the operator come up with a few solutions, before letting the solver improve them.
Apr 11, 2022 An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration
HOLISTIC APPLICATION OF QUALITY BY DESIGN (QbD) FOR PHARMA PRODUCT DEVELOPMENT EXCELLENCE AND REGULATORY COMPLIANCE

The realm of optimizing the drug formulations has gained significant momentum towards more systematic approach of “Quality by Design (QbD)” based strategies employing “Design of Experiments (DoE)” from the erstwhile traditional short-gun approach of changing “One Factor at a Time (OFAT)”. These traditional approaches are generally associated with multiple intricacies including utilization of greater magnitude of time, money and energy, inconduciveness to plug errors, unpredictability and inability to reveal interactions and only “just workable” solutions. In this regard, the new holistic QbD-based paradigm, i.e., “Formulation by Design (FbD)”, applicable especially in the development of drug delivery systems brings about complete understanding of the product and processes based on the sound knowledge of science and quality risk management. Further, the recent regulatory guidance’s issued by the key federal agencies to practice QbD has coerced the researchers in industrial milieu to employ these rational approaches during drug product development. Beyond the pharmaceutical formulation development, QbD has diverse applications in API synthesis, analytical method
Cholinergic and dopaminergic eﬀects on prediction error and uncertainty responses during sensory associative learning

used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological eﬀects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not aﬀected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between diﬀerent hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been aﬀected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.
Sensitivity of urban ﬂ ood simulations to stormwater infrastructure and soil in ﬁ ltration

Flooding from intense rainfall is a major hazard in many urban areas. One of the major challenges in urban ﬂ ood simulations is lack of data about the location and properties of stormwater infrastructure and land cover. In this paper, we investigate the sensitivity of urban ﬂ ood simulations to inputs of stormwater infrastructure and soil characteristics. We use a 2D hydrodynamic model (MIKE URBAN) to simulate ﬂ ood events on the University of Alabama (UA) campus in Tuscaloosa U.S.A. In ﬁ ltration rate, soil moisture, and soil texture were measured in the ﬁ eld. Soil texture was found to be homogeneous across campus (sandy loam) but with a high degree of spatial and temporal variation in in ﬁ ltration rate and soil moisture. Comparison between di ﬀ erent storm event return periods (10, 25, 50, and 100 years) shows that the same ﬂ ooding hotspots are persistent but with considerable variation in water depth and ﬂ ood extent. To investigate the sensitivity of the simulations to stormwater infrastructure, simulations without the stormwater infrastructure input were conducted. The results show that stormwater infrastructure decreases ﬂ ooding volume of RP 10, 25, 50, and 100 by factors of 20, 14, 12, and 8, respectively. This shows that urban ﬂ ood simulation is highly sensitive to the inclusion of stormwater infrastructure, though with decreasing relative impact for larger events. To investigate the sensitivity of the simulations to soil characteristics, four land-cover simulations (actual, uniform, entirely pervious, entirely impervious) were compared. The results show ﬂ ood simulation predictions are sensitive to both the value and spatial explicitness of the soil input data. We discuss challenges in urban ﬂ ood simulation and their potential solutions in the context of emerging frameworks for national and global hyper-resolution ﬂ ood forecasting and analysis.
Microleakage Assessment of Hydrophilic Pit and Fissure Sealants Fortified with Green Synthesized Silver Nanoparticles-An in Vitro Study

Background and Aim: Pit and fissure sealants play a significant role in preventive dentistry. The sealant's physical properties lend a notable contribution to its long-term success. The invention of nanoscience has led to breakthrough discoveries in improving the mechanical properties of sealants. The current work aimed to evaluate the microleakage of Hydrophilic sealant fortified using silver nanoparticles compared to the Hydrophilic sealant and conventional sealant on permanent molars. Materials and methods: Silver nanoparticles were green synthesized using beetroot extract and incorporated into hydrophilic Ultraseal XT Hydro sealant. A sample of extracted third molars, thirty in number, were allocated randomly to three different groups as follows: Group I: conventional Clinpro sealant, Group II: hydrophilic Ultraseal XT hydro, and Group III: Ultraseal XT hydro fortified with silver nanoparticles. Microleakage assessment was performed using a stereomicroscope. The Kruskal-Wallis test was employed to compare the microleakage values of the three groups. Results: Group III had the lowest values about mean microleakage score , and the highest scores were noted for Group I ( . A statistically significant difference was observed in comparing the three groups employing the Kruskal-Wallis test. Conclusion: The results of the current study have established that the microleakage was the lowest in fortified UltraSeal XT Hydro (Group III) when compared to Ultraseal XT Hydro (Group II) and Clinpro sealant (Group I).
Microbial detection in seroma fluid preceding the diagnosis of breast implant-associated anaplastic large cell lymphoma: a case report and review of the literature

Abstract The Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) represents a topic of great concern. We report the case of a patient with late-onset seroma, who was initially diagnosed with an implant-related infection of the breast due to microbial detection in the seroma fluid, thus delaying the diagnosis of BIA-ALCL.