Miracle-dz/UniFAR-FTUs huggingface dataset:
Linguistic markers of autism spectrum conditions in narratives: A comprehensive analysis

Background & Aims: Narratives are regularly elicited as part of standardized assessments for autism spectrum conditions (ASC) such as the ADOS, but have rarely been utilized as linguistic data in their own right. We here aimed for a specific and comprehensive quantitative linguistic profile of such narratives across nominal, verbal, and clausal domains of grammatical organization, and error patterns. Methods: We manually transcribed and annotated narratives elicited from the ADOS from a sample of bilingual autistic Spanish-Catalan children (n = 18), matched with typically developing controls (n = 18) on vocabulary-based verbal IQ. Results: Results revealed fewer relative clauses and more frequent errors in referential specificity and non-relational content-word choice in ASC. Frequent error types are also discussed qualitatively. Conclusions & Implications: These findings, based on more finegrained linguistically defined variables, help to disentangle previous inconsistencies in the literature, and to better situate language changes in the spectrum of neurocognitive changes in this population.
Heterostructures improve IR detector performance

IR imagers are important for a variety of applications, including (but not limited to) noninvasive medical diagnostics and technological functions in security and defense. However, cost is a major limitation because of the cooling systems required to keep such devices at the required low operating temperatures. Thermal-imaging devices exist that operate at room temperature, but they lack speed and do not acquire any spectral information. Rather, they only detect differences in emissivity. Commercial photodetector technologies like mercury cadmium telluride (MCT) are available, but they are expensive because of low production yield. To overcome the requirement for low operating temperatures that restrict use of IR photodetectors, semiconductor-heterostructure designs have been developed. We are working on III-V semiconductor-heterostructure designs to achieve improved IR device performance at increased operating temperatures.1–4 We are focusing on a heterostructure that incorporates a design known as ‘nBn,’ where the sequence of letters refers to different layers of the device.5–7 Some problems that remain with this structure are related to parameter optimization of the different layers for various material systems and, more fundamentally, lack of a basic understanding of what is happening at the interface of the different layers. Our group recently investigated indium arsenide antimonide (InAsSb)-based nBn detectors with different material compositions of the B layer. The nBn detector architecture operates like a regular semiconductor p-i-n photodiode but with lower dark current (i.e., the current present when no radiation is incident on the aperture), which is a limiting factor for IR detectors. The reason for this decrease is the absence of a depletion region (the built-in potential of a standard photodiode), which contributes to a dark-current component known as generation-recombination (G-R) current. By removing this dark-current source, nBn detectors ideally operate with superior performance compared to standard Figure 1. Arrhenius plot of dark current as a function of charge for nBn and p-i-n (PIN) detector structures. nBn: Refers to the different layers of the device. G-R: Generation-recombination current. q/kT: Characteristic charge. T0: Temperature at onset of G-R-limited range. T, T1, T2: Temperature and temperature markers. I1, I2: Current markers.
Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

ABSTRACT Skillful quantitative precipitation nowcasting (QPN) is important for predicting precipitation in the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN studies have actively adopted deep learning (DL) to generate precipitation maps using sequences of ground radar data. Although high skill scores in forecasting precipitation areas of weak intensity (~1 mm/h) have been achieved, the horizontal movement of precipitation areas could not be accurately simulated, exhibiting poor forecasting skills for stronger intensities. For lead times up to 120 min, this study suggests using an improved radar-based QPN model that utilizes a state-of-the-art DL model termed simpler yet better video prediction (SimVP). An independent evaluation using ground radar data in South Korea from June to September 2022 demonstrated that the proposed model outperformed the existing DL models in terms of critical score index (CSI) with a lead time of 120 min (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively). Three case analyses were conducted to reflect various precipitation conditions: heavy rainfall, typhoons, and fast-moving narrow convection events. The proposed SimVP-based QPN model yielded robust performance for all cases, producing a comparable or highest CSI at the lead time of 120 min with a 1 mm/h threshold (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the model indicated better results in terms of displacement movement and reduced underestimation than other models under the high variability of precipitation patterns of the three cases. A comparison of model complexity among DL-QPN models was conducted, taking into consideration operational applications across various study areas and environments. The proposed approach is expected to provide a new baseline for DL-based QPN, and the improved prediction using the proposed model can lead to reduced socioeconomic damage incurred as a result of short-term intense precipitation.
Should Trazodone Be First-Line Therapy for Insomnia? A Clinical Suitability Appraisal

Trazodone is one of the most commonly used prescription medications for insomnia; however, some recent clinical guidelines do not recommend its use for treating insomnia. This clinical appraisal critically reviews the scientific literature on trazodone as a first-line treatment for insomnia, with the focus statement “Trazodone should never be used as a first-line medication for insomnia. ” In addition, field surveys were sent to practicing physicians, psychiatrists, and sleep specialists to assess general support for this statement. Subsequently, a meeting with a seven-member panel of key opinion leaders was held to discuss published evidence in support and against the statement. This paper reports on the evidence review, the panel discussion, and the panel’s and healthcare professionals’ ratings of the statement’s acceptability. While the majority of field survey responders disagreed with the statement, the majority of panel members agreed with the statement based on the limited published evidence supporting trazodone as a first-line agent as they understood the term “first-line agent”.
Critical Issues and Trends in Innovation and Entrepreneurship Education in Higher Education in the Post-COVID-19 Era in China and Spain

In the post-COVID-19 era, where innovation and entrepreneurship as an engine for economic development is a top priority for each country, acquiring various skills through innovation and entrepreneurship education is a trend for higher education students. We focus our analysis on two public universities in China and Spain with some similarities in the implementation of innovation and entrepreneurship education. To obtain our information, we conducted interviews with multiple experts in both universities. The study shows that both countries provide funding and policies to promote innovation and entrepreneurship education as innovation and entrepreneurship education is seen as making the university more visible, both at the national and international levels, and as providing society with employment opportunities. We conclude that although innovation and entrepreneurship education design and definition in both universities are different, their development characteristics and trends are the same. Moreover, both universities face common difficulties, namely financial support, management support, and integration with different disciplines. Finally, innovation and entrepreneurship education promote also internationalization and regional development, which requires both countries to strengthen foreign language education and the provision of more entrepreneurial support services.
Pulmonary tuberculosis risks and challenges

Pulmonary Tuberculosis (PTB) is an airborne bacterial infection that can be deadly if not treated. PTB is most common in developing countries, ecologically problematic, where it kills about 1 million people each year. Pulmonary tuberculosis is a dreaded disease that can have a devastating impact on its victims. Tuberculosis is a serious respiratory infection caused by the bacterium Mycobacterium tuberculosis. It commonly affects the lungs but can also affect other parts of the body, such as the kidney, spine and brain. This review article discusses the PTB risks and challenges word wide. The global burden of TB remains high, with approximately one-third of the world's population being infected with the bacterium. Overall, more needs to be done to improve the management of TB worldwide in order to ensure that no person is left to suffer from the devastating effects of this disease.
The Effect of Periprocedural Clinical Factors Related to the Course of STEMI in Men and Women Based on the National Registry of Invasive Cardiology Procedures (ORPKI) between 2014 and 2019

Background: There are several sex-related differences in the course, management, and outcomes of ST-elevation myocardial infarction (STEMI). This study aimed to identify the risk factors that may affect the odds of procedure-related death in patients with STEMI. Methods: The observational cohort study group consisted of 118,601 participants recruited from the National Registry of Invasive Cardiology Procedures (ORPKI). Results: Procedure-related death occurred in 802 (1.0%) men and in 663 (1.7%) women. The odds of procedure-related death among women were significantly higher than among men (OR, 1.76; 95% CI, 1.59–1.95; p < 0.001). The probability of procedure-related mortality was highest in both men and women with cardiac arrest in the cath lab, critical stenosis of the left main coronary artery, and direct transfer to the cath lab. The factors that reduced the probability of procedure-related mortality in both men and women were thrombolysis in myocardial infarction (TIMI) flow grade and the use of P2Y12 inhibitors in the peri-infarct period. Psoriasis was associated with increased odds of procedure-related death among men, whereas cigarette smoking reduced the odds among women. Conclusions: Procedure-related deaths occurred more frequently in women than men with STEMI. Additional scrutiny needs to be undertaken to identify factors influencing survival regarding gender differences.
Point-source Contribution to the Diffuse X-Ray Background below 1 keV and Its Effect on Our Understanding of the Circumgalactic Medium

We studied the spectral signature of different components of the diffuse X-ray background (DXB), including Local Hot Bubble, solar wind charge exchange, galactic halo, and typically unresolved point sources (galaxies and active galactic nuclei), in the direction of the Chandra Deep Field South using the 4 Ms XMM-Newton survey and Chandra 4 Ms Source Catalog. In this paper, we present our results showing how the different components contribute to the DXB below 1 keV. In particular, we have found that ∼6% of the emission at 34 keV (all-sky average value ≈3 × 10−3 cm−6 pc), which is typically associated with galactic halo (GH) and circumgalactic medium (CGM), is, in fact, due to emission from typically unresolved galaxies. We will discuss the effect that this has on our understanding of GH and CGM and to our understanding of the missing CGM baryons.
Assessing Malaysian Firms’ Intention to Use Recombinant Collagen-Like Protein in Collagen Products

The study aims to assess the prospect of using recombinant collagen-like protein (RCLP) from producers’ point of view. Using cross-sectional data from a survey, the study attempts to validate the proposed hypothesis within Technology Acceptance Model (TAM) by using the Partial Least Square (PLS) path modeling approach (a variance-based SEM technique). Results show a direct positive and significant relations between ‘perceive ease of use’ and ‘attitude toward’, ‘perceived ease of use’ and ‘perceived usefulness’, ‘attitude toward’ and ‘behavioural intention’, and ‘perceived usefulness’ and ‘behavioural intention’ of producing collagen products sourced from recombinant collagen-like protein among firms. The results imply the high potential end collagen products to be marketed using this alternative source of collagen which is environmentally friendly and promotes sustainable development.
Supersonic Retropropulsion Experimental Results from the NASA Ames 9- x 7-Foot Supersonic Wind Tunnel

Supersonic retropropulsion was experimentally examined in the Ames Research Center 9x7-Foot Supersonic Wind Tunnel at Mach 1.8 and 2.4. The experimental model, previously designed for and tested in the Langley Research Center Unitary Plan Wind Tunnel at Mach 2.4, 3.5 and 4.6, was a 5-in diameter 70-deg sphere-cone forebody with a 9.55-in long cylindrical aftbody. The forebody was designed to accommodate up to four 4:1 area ratio nozzles, one on the model centerline and the other three on the half radius spaced 120-deg apart. Surface pressure and flow visualization were the primary measurements, including high-speed data to investigate the dynamics of the interactions between the bow and nozzle shocks. Three blowing configurations were tested with thrust coefficients up to 10 and angles of attack up to 20-deg. Preliminary results and observations from the test are provided.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs

Large language models (LLMs) are increasingly becoming all-powerful and pervasive via deployment in sociotechnical systems. Yet these language models, be it for classification or generation, have been shown to be biased, behave irresponsibly, causing harm to people at scale. It is crucial to audit these language models rigorously before deployment. Existing auditing tools use either or both humans and AI to find failures. In this work, we draw upon literature in human-AI collaboration and sensemaking, and interview research experts in safe and fair AI, to build upon the auditing tool: AdaTest [36], which is powered by a generative LLM. Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing. To evaluate the effectiveness of AdaTest++, the augmented tool, we conduct user studies with participants auditing two commercial language models: OpenAI’s GPT-3 and Azure’s sentiment analysis model. Qualitative analysis shows that AdaTest++ effectively leverages human strengths such as schematization, hypothesis testing. Further, with our tool, users identified a variety of failures modes, covering 26 different topics over 2 tasks, that have been shown in formal audits and also those previously under-reported.
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis

How can we teach a computer to recognize 10,000 different actions? Deep learning has evolved from supervised and unsupervised to self-supervised approaches. In this paper, we present a new contrastive learning-based framework for decision tree-based classification of actions, including human-human interactions (HHI) and human-object interactions (HOI). The key idea is to translate the original multi-class action recognition into a series of binary classification tasks on a pre-constructed decision tree. Under the new framework of contrastive learning, we present the design of an interaction adjacent matrix (IAM) with skeleton graphs as the backbone for modeling various action-related attributes such as periodicity and symmetry. Through the construction of various pretext tasks, we obtain a series of binary classification nodes on the decision tree that can be combined to support higher-level recognition tasks. Experimental justification for the potential of our approach in real-world applications ranges from interaction recognition to symmetry detection. In particular, we have demonstrated the promising performance of video-based autism spectrum disorder (ASD) diagnosis on the CalTech interview video database.
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF11Visit our project page at http://geometrylearning.com/neudf/, that can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. To gain the flexibility of representing arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as surface representation. While a naive extension of an SDF-based neural renderer cannot scale to UDF, we propose two new formulations of weight function specially tailored for UDF-based volume rendering. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity. We extensively evaluate our method over a number of challenging datasets, including DTU [21], MGN [5], and Deep Fashion 3D [61]. Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art method in the task of multi-view surface reconstruction, especially for complex shapes with open boundaries.
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area. Our dataset can be accessed from https://bzhao.me/OOD-CV/
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering

The rendering scheme in neural radiance field (NeRF) is effective in rendering a pixel by casting a ray into the scene. However, NeRF yields blurred rendering results when the training images are captured at non-uniform scales, and produces aliasing artifacts if the test images are taken in distant views. To address this issue, Mip-NeRF proposes a multiscale representation as a conical frustum to encode scale information. Nevertheless, this approach is only suitable for offline rendering since it relies on integrated positional encoding (IPE) to query a multilayer perceptron (MLP). To overcome this limitation, we propose mip voxel grids (Mip-VoG), an explicit multiscale representation with a deferred architecture for real-time anti-aliasing rendering. Our approach includes a density Mip-VoG for scene geometry and a feature Mip-VoG with a small MLP for view-dependent color. Mip-VoG represents scene scale using the level of detail (LOD) derived from ray differentials and uses quadrilinear interpolation to map a queried 3D location to its features and density from two neighboring down-sampled voxel grids. To our knowledge, our approach is the first to offer multiscale training and real-time anti-aliasing rendering simultaneously. We conducted experiments on multiscale dataset, results show that our approach outperforms state-of-the-art real-time rendering baselines.
Compact Steep Spectrum Radio Sources with Enhanced Star Formation Are Smaller Than 10 kpc

Compact steep spectrum (CSS) radio sources are active galactic nuclei (AGN) that have radio jets propagating only on galactic scales, defined as having projected linear size (LS) of up to 20 kpc. CSS sources are generally hosted by massive early-type galaxies with little ongoing star formation; however, a small fraction are known to have enhanced star formation. Using archival data from the Faint Images of the Radio Sky at Twenty cm survey, the Very Large Array Sky Survey, and the Sloan Digital Sky Survey, we identify a volume-limited sample of 166 CSS sources at z < 0.2 with L 1.4 GHz > 1024 W Hz−1. Comparing the star formation rates and linear sizes of these CSS sources, we find that the ≈14% of CSS sources with specific star formation rates above 0.01 Gyr−1 all have LS < 10 kpc. We discuss the possible mechanisms driving this result, concluding that it is likely the excess star formation in these sources occurred in multiple bursts and ceased prior to the AGN jet being triggered.
Heterogeneous-Agent Reinforcement Learning

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours heavily rely on parameter sharing among agents, which confines them to only homogeneous-agent setting and leads to training instability and lack of convergence guarantees. To achieve effective cooperation in the general heterogeneous-agent setting, we propose Heterogeneous-Agent Reinforcement Learning (HARL) algorithms that resolve the aforementioned issues. Central to our findings are the multi-agent advantage decomposition lemma and the sequential update scheme. Based on these, we develop the provably correct Heterogeneous-Agent Trust Region Learning (HATRL), and derive HATRPO and HAPPO by tractable approximations. Furthermore, we discover a novel framework named Heterogeneous-Agent Mirror Learning (HAML), which strengthens theoretical guarantees for HATRPO and HAPPO and provides a general template for cooperative MARL algorithmic designs. We prove that all algorithms derived from HAML inherently enjoy monotonic improvement of joint return and convergence to Nash Equilibrium. As its natural outcome, HAML validates more novel algorithms in addition to HATRPO and HAPPO, including HAA2C, HADDPG, and HATD3, which generally outperform their existing MA-counterparts. We comprehensively test HARL algorithms on six challenging benchmarks and demonstrate their superior effectiveness and stability for coordinating heterogeneous agents compared to strong baselines such as MAPPO and QMIX.