Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study.

PURPOSE To determine improvement in breast cancer detection by using supplemental three-dimensional (3D) automated breast (AB) ultrasonography (US) with screening mammography versus screening mammography alone in asymptomatic women with dense breasts. MATERIALS AND METHODS Institutional review board approval and written informed consent were obtained for this HIPAA-compliant study. The SomoInsight Study was an observational, multicenter study conducted between 2009 and 2011. A total of 15 318 women (mean age, 53.3 years ± 10 [standard deviation]; range, 25-94 years) presenting for screening mammography alone with heterogeneously (50%-75%) or extremely (>75%) dense breasts were included, regardless of further risk characterization, and were followed up for 1 year. Participants underwent screening mammography alone followed by an AB US examination; results were interpreted sequentially. McNemar test was used to assess differences in cancer detection. RESULTS Breast cancer was diagnosed at screening in 112 women: 82 with screening mammography and an additional 30 with AB US. Addition of AB US to screening mammography yielded an additional 1.9 detected cancers per 1000 women screened (95% confidence interval [CI]: 1.2, 2.7; P < .001). Of cancers detected with screening mammography, 62.2% (51 of 82) were invasive versus 93.3% (28 of 30) of additional cancers detected with AB US (P = .001). Of the 82 cancers detected with either screening mammography alone or the combined read, 17 were detected with screening mammography alone. Of these, 64.7% (11 of 17) were ductal carcinoma in situ versus 6.7% (two of 30) of cancers detected with AB US alone. Sensitivity for the combined read increased by 26.7% (95% CI: 18.3%, 35.1%); the increase in the recall rate per 1000 women screened was 284.9 (95% CI: 278.0, 292.2; P < .001). CONCLUSION Addition of AB US to screening mammography in a generalizable cohort of women with dense breasts increased the cancer detection yield of clinically important cancers, but it also increased the number of false-positive results.
Detecting Changes in Precipitation and Temperature Extremes over China Using a Regional Climate Model with Water Table Dynamics Considered

Abstract Simulations were conducted with the regional climate model RegCM incorporating water table dynamics from 1 September 1982 to 28 August 2002 to detect precipitation and temperature extremes. Compared with observed rlO (number of days with precipitation ≥ 10 mm d−1), RegCM3_Hydro (the regional climate model with water table dynamics considered) simulated rain belts, including those in southern China and the middle and lower reaches of the Yangtze River, and provided data for arid to semi-arid areas such as the Heihe River Basin in northwestern China. RegCM3_Hydro indicated a significant increasing trend of r95p (days with daily precipitation greater than the 95th percentile of daily amounts) for the Yangtze, Yellow, and Pearl River basins, consistent with r95p observations. The Haihe River Basin was also chosen as a specific case to detect the effect of ground-water on extreme precipitation using peaks over threshold (POT)-based generalized Pareto distribution (GPD) with parameters estimated by the L-moment method. Quantile plots showed that all but a few of the plotted points were distributed near diagonal lines and the modeled data fitted well with the samples. Finally, the effects of water table dynamics on temperature extremes were also evaluated. In the Yellow River Basin and Songhuajiang River Basin, the trends of the number of warm days (TX95n) from RegCM3_Hydro matched observed values more closely when water table dynamics were considered, and clearly increasing numbers of warm days from 1983 to 2001 were detected.
for “Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild”

Table A shows the effectiveness of 1) usage of VPoser [6] and 2) weight of L2 regularizer during the training of the annotation network f . The combination of VPoser and L2 regularizer is the third recipe, introduced in Section 2.2 of the main manuscript. Regardless of the usage of VPoser, setting the non-zero weight of the L2 regularizer produces lower 3D errors of g. This indicates that despite its simplicity, the L2 regularizer helps to prevent anatomically implausible 3D meshes and produce beneficial 3D pseudoGTs. In addition, using VPoser achieves lower 3D errors of g compared to not using it. This is also because VPoser can effectively limit the 3D mesh to anatomically plausible space. Training sets of all annotation networks f in the table are H36M+MI+MSCOCO+3DPW. The ResNet backbone [1] of all annotation networks in the table are initialized with ResNet, pre-trianed on ImageNet [7] classification dataset. Fig. A shows the effectiveness of 1) using VPoser and 2) applying L2 regularizer. Without VPoser and L2 regularizer, the 3D pseudo-GT has an anatomically implausible 3D mesh although its 2D pose is fit to the image. Using VPoser makes the 3D pseudo-GT anatomically plausible; however, it still produces the wrong 3D mesh. The right leg is too much bent to the left side. Finally, using both VPoser and L2 regularizer makes the 3D pseudo-GT anatomically plausible and correct. In particular, additionally using the L2 regularizer enforces the 3D mesh in the latent space of VPoser.
SHAPING FUTURE AUTISM

- A neurodevelopmental condition known as autism spectrum disorder can make a person exhibit repetitive behaviour and lack of social contact. It must be diagnosed and treated at an early stage; otherwise it may adversely affect the life of that individual. However, ASD affected individuals will be of different age group, we are aimed to focus mainly on children affected with ASD of age group 5-16. The main challenges faced by autism affected children(individuals) are, they may lack in speaking and listening skills, also difficulty in understanding facial expressions meaning, not having good concentration skills, and many minute problems are accompanying ASD affected individuals(children). As a result, the goal of this initiative is to serve as a link between ASD affected children and their guardians. The technology offers two options 1) improving the speaking skills through reading and 2) improves concentration through making them listen to. Core aim of our system is to provide treatment to the affected child, but through our system we also offer detection of autism by using training data set. Algorithm used to achieve the system is Efficient Net B3
Proliferation of atmospheric datasets can hinder policy making: a data blending technique offers a solution

The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyze several datasets representing rainfall over Nepal. We show that estimates of extreme rainfall are highly variable depending on which dataset you choose to look at. This leads to confusion and inaction from policy-focused decision makers. Scientifically, we should use datasets that sample a range of creation methodologies and prioritize the use of data science techniques that have the flexibility to incorporate these multiple sources of data. We demonstrate the use of a statistically interpretable data blending technique to help discern and communicate a consensus result, rather than imposing a priori judgment on the choice of dataset, for the benefit of policy decision making.
A New Dataset and Approach for Timestamp Supervised Action Segmentation Using Human Object Interaction

This paper focuses on leveraging Human Object Interaction (HOI) information to improve temporal action segmentation under timestamp supervision, where only one frame is annotated for each action segment. This information is obtained from an off-the-shelf pre-trained HOI detector, that requires no additional HOI-related annotations in our experimental datasets. Our approach generates pseudo labels by expanding the annotated timestamps into intervals and allows the system to exploit the spatio-temporal continuity of human interaction with an object to segment the video. We also propose the (3+1)Real-time Cooking (ReC)1 dataset as a realistic collection of videos from 30 participants cooking 15 breakfast items. Our dataset has three main properties: 1) to our knowledge, the first to offer synchronized third and first person videos, 2) it incorporates diverse actions and tasks, and 3) it consists of high resolution frames to detect fine-grained information. In our experiments we benchmark state-of-the-art segmentation methods under different levels of supervision on our dataset. We also quantitatively show the advantages of using HOI information, as our framework improves its baseline segmentation method on several challenging datasets with varying viewpoints, providing improvements of up to 10.9% and 5.3% in F1 score and frame-wise accuracy respectively.
Social Media Data Analysis for Traffic Incident Detection and Management

Social media and online services with user-posted content have generated a staggering amount of information that has potential applications in various areas such as incident analysis and emergency management. In this research, the authors explored the feasibility of using social media data for detecting traffic incidents and collecting supplemental incident information. A comprehensive approach has been developed to extract and analyze real-time traffic related twitter data for incident management purpose. The developed approach consists of three steps: (1) Development of traffic incident related key words and their association rules; (2) Extraction of real-time tweets with influential word sets; and (3) Ranking and selection of traffic related tweets. The developed approach was implemented at District of Columbia Department of Transportation for incident management. Data validation has been conducted against the real-world incident database. The preliminary results of analysis have shown that social media data are promising for early incident detection and can be used as supplemental source for incident data collection.
Revista de Gestão Ambiental e Sustentabilidade – GeAS

This study analyzed the case of manufacturing of Personal Protective Equipment (PPE) using as raw material biopolymers produced from ethanol from sugar cane, known as green polypropylene, produced since 2008 by BRASKEM. This article studied the PPE for the employee’s head protection, named helmet by NR 6, which is used in situations of exposure to weather and work scenarios in places where there is risk of impact from falling or projecting objects, burns, electric shock, and solar radiation. The MSA, green helmet manufacturer, made an inventory of greenhouse gas emissions into the atmosphere by comparing the two manufacturing processes of the helmet shell, covering the January 1 to December 31, 2011 period. It concluded that the sustainable helmet (green polyethylene and pigments) robs 231g of CO2 from the atmosphere per produced unit, while the helmet’s production with traditional raw materials (polyethylene and petrochemical pigments) found that, for each unit produced, 1029g of CO2 are emitted into the atmosphere. The study showed that substitution of raw materials has led to reduction in the impact generated in the helmets’ production.
The Interaction between Inputs and Configurations fed to Software Systems: an Empirical Study

Widely used software systems such as video encoders are by necessity highly conﬁgurable, with hundreds or even thousands of options to choose from. Their users often have a hard time ﬁnding suitable values for these options ( i.e. , ﬁnding a proper conﬁguration of the software system) to meet their goals for the tasks at hand, e.g. , compress a video down to a certain size. One dimension of the problem is of course that performance depends on the input data: e.g. , a video as input to an encoder like x264 or a ﬁle system fed to a tool like xz . To achieve good performance, users should therefore take into account both dimensions of (1) software variability and (2) input data. In this problem-statement paper, we conduct a large study over 8 conﬁgurable systems that quantiﬁes the existing interactions between input data and con-ﬁgurations of software systems. The results exhibit that (1) inputs fed to software systems interact with their conﬁguration options in non monotonous ways , signiﬁcantly impacting their performance properties (2) tuning a software system for its input data makes it possible to multiply its performance by up to ten (3) input variability can jeopardize the relevance of performance predictive models for a ﬁeld deployment.
Partitioning the LIS/OTD Lightning Climatological Dataset into Separate Ground and Cloud Flash Distributions

Presently, it is not well understood how to best model nitrogen oxides (NOx) emissions from lightning because lightning is highly variable. Peak current, channel length, channel altitude, stroke multiplicity, and the number of flashes that occur in a particular region (i.e., flash density) all influence the amount of lightning NOx produced. Moreover, these 5 variables are not the same for ground and cloud flashes; e.g., cloud flashes normally have lower peak currents, higher altitudes, and higher flash densities than ground flashes [see (Koshak, 2009) for additional details]. Because the existing satellite observations of lightning (Fig. 1) from the Lightning Imaging Sensor/Optical Transient Detector (LIS/OTD) do not distinguish between ground and cloud fashes, which produce different amounts of NOx, it is very difficult to accurately account for the regional/global production of lightning NOx. Hence, the ability to partition the LIS/OTD lightning climatology into separate ground and cloud flash distributions would substantially benefit the atmospheric chemistry modeling community. NOx indirectly influences climate because it controls the concentration of ozone and hydroxyl radicals in the atmosphere. The importance of lightning-produced NOx is empasized throughout the scientific literature (see for example, Huntrieser et al. 1998). In fact, lightning is the most important NOx source in the upper troposphere with a global production rate estimated to vary between 2 and 20 Tg (N)yr(sup -1) (Lee et al., 1997), with more recent estimates of about 6 Tg(N)yr(sup -1) (Martin et al., 2007). In order to make accurate predictions, global chemistry/climate models (as well as regional air quality modells) must more accurately account for the effects of lightning NOx. In particular, the NASA Goddard Institute for Space Studies (GISS) Model E (Schmidt et al., 2005) and the GEOS-CHEM global chemical transport model (Bey et al., 2001) would each benefit from a partitioning of the LIS/OTD lightning climatology. In this study, we introduce a new technique for retrieving the ground flash fraction in a set of N lightning observed from space and that occur within a specific latitude/longitude bin. The method is briefly described and applied to CONUS lightning that have already been partitioned into ground and cloud flashes using independent ground-based observations, in order to assess the accuracy of the retrieval method. The retrieval errors are encouragingly small.
Advanced Methods For Building Information Extraction From Very High Resolution SAR Data To Support Emergency Response

Rapid damage assessment after natural disasters (e.g. earthquakes, floods) and violent conflicts (e.g. war-related destruction) is crucial for initiating effective emergency response actions. Remote sensing satellites equipped with multispectral and Synthetic Aperture Radar (SAR) imaging sensors can provide vital information due to their ability to map affected areas of interest with high geometric precision and in an uncensored manner. The new spaceborne Very High Resolution (VHR) SAR sensors onboard the TerraSAR-X and COSMO-SkyMed satellites can achieve spatial resolutions in the order of 1 m. In VHR SAR data, features from individual urban structures (like buildings) can be identified in their characteristic settings in urban settlement patterns. This thesis presents novel techniques to support emergency response after catastrophic events using latest generation earth observation imagery. In this context, the potential and limits of VHR SAR imagery for extracting information about individual buildings in an (semi-) automatic manner is investigated. The following main novel contributions are presented. First, we investigate the potential of the characteristic double bounce of a building in VHR SAR imagery to be exploited in automatic damage assessment techniques. In particular, we analyze empirically the relation between the double bounce effect and the aspect angle. Then, we propose a radar imaging simulator for urban structures, which is based on an adapted ray tracing procedure and a Lambertian-specular mixture model, emphasizing the geometrical effects of the scattering. Furthermore, we propose an approach to the height estimation of buildings from single detected SAR data. It is based on a "hypothesis generation - rendering - matching" procedure, where a series of hypotheses are generated and rendered by the previously introduced radar imaging simulator in order to compare the simulations with the actual VHR SAR data. Moreover, we present a method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery. This technique evaluates the similarity between the predicted signature of the intact building in the post-event SAR scene and the actual scene to distinguish between damaged and undamaged buildings. Finally, we address the practical requirements of rapid emergency response scenarios by proposing an IT system infrastructure that enables collaborative and distributed geospatial data processing and on-demand map visualization. The effectiveness of all proposed techniques is confirmed by quantitative and qualitative experimental results obtained on airborne and spaceborne VHR SAR imagery.
Psychedelics and neonihilism: connectedness in a meaningless world

The resurgence of psychedelic research explicitly targets treating mental health conditions largely through psychedelics-assisted psychotherapy. Current theories about mechanisms of change in psychedelics-assisted psychotherapy focus on mystical experiences as the main driver of symptom improvement. During these mystical experiences, participants report an enhanced sense of salience, connectedness, and meaning. Simultaneously, a growing psychedelic culture is also cultivating the use of psychedelics as medicine for relieving symptoms of anxiety and depression and promoting cognitive functions. We argue that an integral part of the excitement around the resurgence in psychedelics is in response to a meaning and alienation crisis that correlates with rising rates of anxiety and depression. Framing the absence of meaning as neonihilism, a contemporary correlate to the 19th-century phenomenon with unique features present in a neoliberal cultural context, we explore whether psychedelics combined with group therapy can provide answers to modern experiences of meaninglessness. Based on this exploration, we suggest concrete next steps both in the theory and practice of psychedelic psychotherapy toward what we are calling neonihilistic psychedelic group psychotherapy.
Analysis and Design of UWB Modified Two-Sections Wilkinson Power Splitter

In this paper, the analysis and design of a compact Ultra-Wideband (UWB) Wilkinson Power Splitter (WPS) is presented. The proposed splitter consists of two sections Wilkinson power splitter of equal characteristic impedances and unequal electrical lengths with a single stub on each section. The proposed power splitter examined analytically using the “Even Odd Mode” analysis method and the ABCD matrix. The simulation is done using Agilent ADS circuit and EM simulator. The proposed structure achieves an input return loss, S11 better than 12.2 dB, an output return loss, S22 and S33 better than 19.59 dB, output ports isolation, S23 and S32 better than 12.75 dB and insertion loss, S21 and S31 from 3.15 to 3.41dB through the whole UWB range from 3.1 GHz to 10.6 GHz. Furthermore, the power splitter has a compact size of 18.38 * 10.25 mm2 compared to other similar networks.
Effects of 8 months of high-intensity interval training on physical fitness and health-related quality of life in substance use disorder

Objective This study aimed to investigate the effect of 8 months of high-intensity interval training (HIIT) on physical fitness and health-related quality of life in substance use disorder. Methods Sixty substance use disorder were randomly assigned to either the HIIT group or the control group according to a random sampling method. The HIIT group received 8 months of four 60-min sessions per week under supervision. Weight, waist circumference, body fat percentage, heart rate, blood pressure, VO2max, reaction time, grip strength, standing on one foot with eyes closed, sitting forward flexion, and quadrant jumping, standing on one foot with eyes closed, the number of push-ups, quality of life (SF-36) score, and craving (VAS) scored were monitored in the HIIT and control groups at baseline, 4 months, and 8 months. SPSS 22.0 was used to conduct repeated measurement analysis of variance and Pearson correlation analysis on the collected subject data. Results Compared with baseline, weight (p < 0.001), waist circumference (p < 0.001), body fat percentage (p < 0.001), heart rate (p < 0.05), Systolic blood pressure (p < 0.01), systolic blood pressure (p < 0.05), reaction time (p < 0.001),PSQI (p < 0.001), Total cholesterol (p < 0.001), Triglyceride (p < 0.001), Blood sugar (p < 0.001) and VAS score (p < 0.001) were significantly decreased after 8 months of exercise intervention. Contrastingly, VO2max (p < 0.05), grip strength (p < 0.05), eyes closed and one foot Standing (p < 0.001), sitting forward flexion (p < 0.001), quadrant jumping (p < 0.001), push-ups (p < 0.001), PCS (p < 0.001), and MCS (p < 0.001) were significantly increased. VO2max was significantly negatively correlated with VAS (r = −0.434, p < 0.001), and significantly positively correlated with PCS (r = 0.425, p < 0.001). There was a positive correlation between standing on one foot with closed eyes and MCS (r = 0.283, p < 0.05). Conclusion Eight months of HIIT can comprehensively improve the physical health level and health-related quality of life of men with substance use disorders, reduce the desire for drugs, and lay the foundation for better starting a happy life.