Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fictitious-play Finite-difference Method for Linearly Solvable Mean Field Games

An iterative finite difference scheme for mean field games (MFGs) is proposed. The target MFGs are derived from control problems for multidimensional systems with advection terms. For such MFGs, linearization using the Cole-Hopf transformation and iterative computation using fictitious play are introduced. This leads to an implementation-friendly algorithm that iteratively solves explicit schemes. The convergence properties of the proposed scheme are mathematically proved by tracking the error of the variable through iterations. Numerical calculations show that the proposed method works stably for both one- and two-dimensional control problems.
PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

Accurate segmentation of power lines in various aerial images is very important for UAV flight safety. The complex background and very thin structures of power lines, however, make it an inherently difficult task in computer vision. This paper presents PLGAN, a simple yet effective method based on generative adversarial networks, to segment power lines from aerial images with different backgrounds. Instead of directly using the adversarial networks to generate the segmentation, we take their certain decoding features and embed them into another semantic segmentation network by considering more context, geometry, and appearance information of power lines. We further exploit the appropriate form of the generated images for high-quality feature embedding and define a new loss function in the Hough-transform parameter space to enhance the segmentation of very thin power lines. Extensive experiments and comprehensive analysis demonstrate that our proposed PLGAN outperforms the prior state-of-the-art methods for semantic segmentation and line detection.
Hierarchical Embedded Bayesian Additive Regression Trees

We propose a simple yet powerful extension of Bayesian Additive Regression Trees which we name Hierarchical Embedded BART (HE-BART). The model allows for random effects to be included at the terminal node level of a set of regression trees, making HE-BART a non-parametric alternative to mixed effects models which avoids the need for the user to specify the structure of the random effects in the model, whilst maintaining the prediction and uncertainty calibration properties of standard BART. Using simulated and real-world examples, we demonstrate that this new extension yields superior predictions for many of the standard mixed effects models' example data sets, and yet still provides consistent estimates of the random effect variances. In a future version of this paper, we outline its use in larger, more advanced data sets and structures.
Condition-invariant and compact visual place description by convolutional autoencoder

Abstract Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are convolutional neural network (CNN)-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: (a) their high dimension and (b) lack of generalization, leading to low efficiency and poor performance in real robotic applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in four challenging real-world datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. The code of our work is publicly available at https://github.com/MedlarTea/CAE-VPR.
Image Captioning In the Transformer Age

Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not share the basic network component, such a heterogeneous pipeline is hard to be trained end-to-end where the visual encoder will not learn anything from the caption supervision. This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline. Meantime, self-supervised learning releases the power of the Transformer architecture that a pre-trained large-scale one can be generalized to various tasks including IC. The success of these large-scale models seems to weaken the importance of the single IC task. However, we demonstrate that IC still has its specific significance in this age by analyzing the connections between IC with some popular self-supervised learning paradigms. Due to the page limitation, we only refer to highly important papers in this short survey and more related works can be found at https://github.com/SjokerLily/awesome-image-captioning.
Towards Fine-grained Causal Reasoning and QA

Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despite their importance, have been largely ignored in the literature. This paper introduces a novel fine-grained causal reasoning dataset and presents a series of novel predictive tasks in NLP, such as causality detection, event causality extraction, and Causal QA. Our dataset contains human annotations of 25K cause-effect event pairs and 24K question-answering pairs within multi-sentence samples, where each can have multiple causal relationships. Through extensive experiments and analysis, we show that the complex relations in our dataset bring unique challenges to state-of-the-art methods across all three tasks and highlight potential research opportunities, especially in developing"causal-thinking"methods.
On the Stability of Low-mass Planets with Supercritical Hydrospheres

Short-period, low-mass water-rich planets are subject to strong irradiation from their host star, resulting in hydrospheres in a supercritical state. In this context, we explore the role of irradiation on small terrestrial planets that are moderately wet in the low-mass regime (0.2–1 M ⊕). We investigate their bulk properties for water content in the 0.01–5% range by making use of an internal structure model that is coupled to an atmosphere model. This coupling allows us to take into account both the compression of the interior due to the weight of the hydrosphere and the possibility of atmospheric instability in the low-mass regime. We show that, even for low masses and low water content, these planets display inflated atmospheres. For extremely low planetary masses and high irradiation temperatures, we find that steam atmospheres become gravitationally unstable when the ratio η of their scale height to planetary radius exceeds a critical value of ∼0.1. This result is supported by observational data, as all currently detected exoplanets exhibit values of η smaller than 0.013. Depending on their water content, our results show that highly irradiated, low-mass planets up to 0.9 M ⊕ with significative hydrospheres are not in a stable form and should lose their volatile envelope.
Analytical Model of Disk Evaporation and State Transitions in Accreting Black Holes

State transitions in black hole X-ray binaries are likely caused by gas evaporation from a thin accretion disk into a hot corona. We present a height-integrated version of this process, which is suitable for analytical and numerical studies. With radius r scaled to Schwarzschild units and coronal mass accretion rate ṁc to Eddington units, the results of the model are independent of black hole mass. State transitions should thus be similar in X-ray binaries and an active galactic nucleus. The corona solution consists of two power-law segments separated at a break radius r b ∼ 103(α/0.3)−2, where α is the viscosity parameter. Gas evaporates from the disk to the corona for r > r b , and condenses back for r < r b . At r b , ṁc reaches its maximum, ṁc,max≈0.02(α/0.3)3 . If at r ≫ r b the thin disk accretes with ṁd<ṁc,max , then the disk evaporates fully before reaching r b , giving the hard state. Otherwise, the disk survives at all radii, giving the thermal state. While the basic model considers only bremsstrahlung cooling and viscous heating, we also discuss a more realistic model that includes Compton cooling and direct coronal heating by energy transport from the disk. Solutions are again independent of black hole mass, and r b remains unchanged. This model predicts strong coronal winds for r > r b , and a T ∼ 5 × 108 K Compton-cooled corona for r < r b . Two-temperature effects are ignored, but may be important at small radii.
A possible solution for the faint young Sun paradox: Clues from the exoplanetary data

Faint young Sun paradox (FYSP) is one of the interesting problems in solar physics. The present study aims to get a possible solution for the FYSP through sun-like G stars and their exoplanetary systems. Using physical properties of exoplanetary data, an empirical relationship between the rate of mass loss ( dt ) with stellar mass (M? ) and age (t) is obtained. We found mass loss rate varies with stellar mass as ∝ (M? /M )−3.788 and proportional to the age as ∝ t−1.25, which indicates rate of mass loss is higher during early evolutionary stages. Then we applied mass loss corrections to stellar masses of G-type stars with planets and obtained their initial masses at the early evolutionary stages. Subsequently, we applied these relationships to calculate the mass loss rate and mass of Sun at the early evolutionar stage, which is found to be ∼ 10−11 solar mass per year and ∼ (1.061±0.006) solar mass respectively. The higher solar mass can probably alleviate the problem of the faint young Sun paradox. Then the estimated initial stellar masses of the host stars are used to obtain a best power law relationship with the planetary masses that supports the hypothesis that the massive stars harbour massive planets. Finally, by using the same empirical power law, planetary mass in the vicinity of Sun is estimated to be ∼ (0.84±0.19) Jupiter mass, which is much higher compared to the present solar terrestrial planetary mass. Hence, this study also suggests that there is a missing planetary mass in the vicinity of the Sun, which can solve the FYSP
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points. Merging large-scale point clouds and images raises several challenges, such as constructing a mapping between points and pixels, and aggregating features between multiple views. Current methods require mesh reconstruction or specialized sensors to recover occlusions, and use heuristics to select and aggregate available images. In contrast, we propose an end-to-end trainable multi-view aggregation model leveraging the viewing conditions of 3D points to merge features from images taken at arbitrary positions. Our method can combine standard 2D and 3D networks and outperforms both 3D models operating on colorized point clouds and hybrid 2D/3D networks without requiring colorization, meshing, or true depth maps. We set a new state-of-the-art for large-scale indoor/outdoor semantic segmentation on S3DIS (74.7 mIoU 6-Fold) and on KITTI-360 (58.3 mIoU). Our full pipeline is accessible at https://github.com/drprojects/DeepViewAgg, and only requires raw 3D scans and a set of images and poses.
How HLA diversity is apportioned: influence of selection and relevance to transplantation

In his 1972 paper ‘The apportionment of human diversity’, Lewontin showed that, when averaged over loci, genetic diversity is predominantly attributable to differences among individuals within populations. However, selection can alter the apportionment of diversity of specific genes or genomic regions. We examine genetic diversity at the human leucocyte antigen (HLA) loci, located within the major histocompatibility complex (MHC) region. HLA genes code for proteins that are critical to adaptive immunity and are well-documented targets of balancing selection. The single-nucleotide polymorphisms (SNPs) within HLA genes show strong signatures of balancing selection on large timescales and are broadly shared among populations, displaying low FST values. However, when we analyse haplotypes defined by these SNPs (which define ‘HLA alleles’), we find marked differences in frequencies between geographic regions. These differences are not reflected in the FST values because of the extreme polymorphism at HLA loci, illustrating challenges in interpreting FST. Differences in the frequency of HLA alleles among geographic regions are relevant to bone-marrow transplantation, which requires genetic identity at HLA loci between patient and donor. We discuss the case of Brazil's bone marrow registry, where a deficit of enrolled volunteers with African ancestry reduces the chance of finding donors for individuals with an MHC region of African ancestry. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
The apportionment of citations: a scientometric analysis of Lewontin 1972

‘The apportionment of human diversity’ (1972) is the most highly cited research article published by geneticist Richard Lewontin in his career. This study's primary result—that most genetic diversity in humans can be accounted for by within-population differences, not between-population differences—along with Lewontin's outspoken, politically charged interpretations thereof, has become foundational to the scientific and cultural discourse pertaining to human genetic variation. The article has an unusual bibliometric trajectory in that it is much more salient in the bibliographic record today compared to the first 20 years after its publication. Here, we highlight four factors that may have played a role in shaping the paper's fame: (i) citations in influential publications across several disciplines; (ii) Lewontin's own popular books and media appearances; (iii) the renaissance of population genetics research of the early 1990s; and (iv) the serendipitous collision of scientific progress, influential books and papers, and heated controversies around the year 1994. We conclude with an analysis of Twitter data to characterize the communities and conversations that continue to keep this study at the centre of discussions about race and genetics, prompting new challenges for scientists who have inherited Lewontin's legacy. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
A 14-bit, 33-Mpixel, 120-fps Image Sensor with DMOS Capacitors in 90-nm/65-nm CMOS

We fabricated a 33-Megapixel and 120-fps CMOS image sensor with 14-bit analog-to-digital converters (ADCs) using depletion-mode MOS (DMOS) capacitors. The DMOS capacitor has high capacitance density, whereas its capacitance depends on input voltage. Hence it has been not thought to be suitable for ADCs. We used two-stage cyclic ADC architecture with a split-sampling-capacitor method to reduce the differential non-linearity (DNL) of ADC. As a result, the fabricated image sensor exhibited a DNL of +0.95/-0.80 LSB, realizing genuine 14-bit resolution.
International clinician perspectives on pandemic-associated stress in supporting people with intellectual and developmental disabilities

Background People living with intellectual and developmental disabilities (IDD) have suffered disproportionately in health outcomes and general well-being during the COVID-19 pandemic. There is emerging evidence of increased psychological distress. Increased strain has also fallen on clinicians managing the psychological needs of people with IDD, in the context of learning new technologies, staff shortages, reduced services and paused training opportunities. Aims To examine clinicians’ experiences of patient care, clinical management and the impact of care delivery. Method A mixed fixed-response and free-text survey comprising 28 questions covering four areas (responder demographics, clinical practice, changes to local services and clinician experiences) was developed, using the STROBE guidance. It was disseminated through an exponential snowballing technique to clinicians in seven high-income countries. Quantitative data were analysed and presented with Microsoft Excel. Qualitative data were coded and thematically analysed, and presented with in-text quotations. Results There were 139 respondents, mostly senior physicians (71%). Two-thirds reported over 10 years working in the field. Quantitative findings include increased clinician stress (77%), referrals (53%), patient distress presentations (>70%), patient isolation (73%) and carer burden (89%), and reduced patient participation in daily activities (86%). A third reported increased psychotropic prescribing. Qualitative analysis outlined changes to clinical practice, particularly the emergence and impact of telehealth. Conclusions In the countries surveyed, the pandemic has not only had a significant impact on people with IDD, but also their carers and clinicians. A proactive, holistic international response is needed in preparedness for future public health emergencies.
Performance of loop-mediated isothermal amplification (LAMP) for detection of Schistosoma mansoni infection compared with Kato–Katz and real-time PCR

Abstract The performance of loop-mediated isothermal amplification (LAMP) for detection of Schistosoma mansoni DNA from stool and urine samples in comparison with Kato–Katz and real-time polymerase chain reaction (PCR) was studied. After obtaining informed consent, 50 children participated in the present study and agreed to submit stool and urine samples. Stool samples were examined by Kato–Katz. Both real-time PCR and LAMP techniques were applied on stool and urine samples. The overall prevalence of S. mansoni was 46% in stool and urine samples as detected by the employed techniques, and 90% of cases had light infection intensity. The highest percentage of infection was diagnosed by real-time PCR (44%), followed by Kato–Katz (42%) and LAMP in the stool (36%), while the lowest percentages of infection were diagnosed by real-time PCR and LAMP in urine samples (24% and 14%, respectively). Kato–Katz, real-time PCR and LAMP showed 100% specificity where the sensitivity was 91.3%, 95.7% and 78.3%, respectively, in stool samples. Real-time PCR and LAMP showed lower sensitivity in urine samples. The LAMP assay is a promising technique for S. mansoni diagnosis in endemic countries of moderate and high-intensity infection. Yet, it needs further optimization, particularly in urine samples.
Effect of maternal employment on child nutritional status in Bale Robe Town, Ethiopia: a comparative cross-sectional analysis

Abstract Adequate nutrition is essential for early childhood to ensure healthy growth, proper organ formation, and function, a strong immune system, neurological and cognitive development. The main aim of the present study was to assess the effect of maternal employment on nutritional status among children aged 6–23 months in the town of Bale Robe, Ethiopia. A community-based comparative cross-sectional study was conducted on about 597 (293 unemployed and 304 employed) having children aged 6–23-month-old children sampled were employed with a multistage sampling technique. A face-to-face interview was conducted using a structured pretested questionnaire. Descriptive statistics, binary and multivariable logistic regression analyses were used for the statistical analysis. The magnitude of stunting (39.9 %), underweight (39⋅9 %) and wasting (22⋅2 %) was greater in 6–23-month-old children born to employed mothers than their counterparts in unemployed ones [stunted (31⋅3 %), underweight (24⋅0 %) and wasted (11⋅8 %)]. Being a girl [AOR 0⋅31; 95 % CI (0⋅17, 0⋅54)] in employed mothers and [AOR 0⋅29; 95 % CI (0⋅16, 0⋅51)] in unemployed people significantly protected stunting. This study demonstrated that the nutritional status of 6–23-month-old children is better among unemployed mothers than among employed mothers. Therefore, concerted efforts may decrease child undernutrition in a study area.
Fuzzy Comprehensive Evaluation of College Students' Internet Use Risk Identification and Control

In recent years, with the continuous development of network technology, network life has become an indispensable part of college student's life. However, the popular network technology is questioned by people, increasingly developed network brings college students to many different risk problems. Through the study, it is found that there exists three main risks that Internet addiction, network cheated[1], network moral anomie[2] in the current college students' using Internet. In response to numerous risk, this paper uses the risk probability, risk degree, recoverability, recurrence rate, value of the vulnerability to do the comprehensive evaluation of the main current risk value at risk. Based on analyzing three risk fuzzy comprehensive evaluation results, this paper discusses the risk causes and gives society and school the suggestions that how to reduce the risk of the Internet using among contemporary college students normally provide favorable conditions.
A Novel Risk Prediction Model for Severe Acute Kidney Injury in Intensive Care Unit Patients Receiving Fluid Resuscitation

Background To develop a risk prediction model for the occurrence of severe acute kidney injury (AKI) in intensive care unit (ICU) patients receiving fluid resuscitation. Methods We conducted a secondary analysis of the Crystalloid vs. Hydroxyethyl Starch Trial (CHEST) trial, a blinded randomized controlled trial that enrolled ICU patients who received intravenous fluid resuscitation. The primary outcome was the first event in a composite outcome of doubling of serum creatinine and/or treatment with renal replacement treatment (RRT) within 28 days of randomization. The final model developed using multivariable logistic regression with backwards elimination was validated internally and then translated into a predictive equation. Results Six thousand seven hundred twenty-seven ICU participants were studied, among whom 745 developed the study outcome. The final model having six variables, including admission diagnosis of sepsis, illness severity score, mechanical ventilation, tachycardia, baseline estimated glomerular filtration rate and emergency admission. The model had good discrimination (c-statistic = 0.72, 95% confidence interval 0.697–0.736) and calibration (Hosmer-Lemeshow test, χ2 = 14.4, p = 0.07) for the composite outcome, with a c-statistic after internal bootstrapping validation of 0.72, which revealed a low degree of over-fitting. The positive predictive value and negative predictive value were 58.8 and 89.1%, respectively. The decision curve analysis indicates a net benefit in prediction of severe AKI using the model across a range of threshold probabilities between 5 and 35%. Conclusions Our model, using readily available clinical variables to identify ICU patients at high risk of severe AKI achieved good predictive performance in a clinically relevant population.