Miracle-dz/UniFAR-FTUs huggingface dataset:
Inspiratory muscle training in children and adolescents living with neuromuscular diseases: A pre-experimental study

Background Children with neuromuscular diseases (NMD) are at risk of morbidity and mortality because of progressive respiratory muscle weakness and ineffective cough. Inspiratory muscle training (IMT) aims to preserve or improve respiratory muscle strength, thereby reducing morbidity and improving health-related quality of life (HRQoL). Objectives To describe the safety and feasibility of a 6-week IMT programme using an electronic threshold device (Powerbreathe®). Any adverse events and changes in functional ability, spirometry, peak expiratory cough flow (PECF), inspiratory muscle strength and HRQoL (Pediatric Quality of Life [PedsQL]) were recorded. Methods A convenience sample of eight participants (n = 4 boys; median [interquartile range {IQR}] age: 12.21 [9.63–16.05] years) with various NMD were included in a pre-experimental, observational pre-test post-test feasibility study. Training consisted of 30 breaths, twice daily, 5 days a week, for 6 weeks. Results There were significant pre- to post-intervention improvements in upper limb function and coordination (p = 0.03) and inspiratory muscle strength: maximum inspiratory mouth pressure (Pimax) (p = 0.01); strength-index (p = 0.02); peak inspiratory flow (PIF) (p = 0.02), with no evidence of change in spirometry, PECF or HRQoL. No adverse events occurred and participant satisfaction and adherence levels were high. Conclusion Inspiratory muscle training (at an intensity of 30% Pimax) appears safe, feasible and acceptable, in a small sample of children and adolescents with NMD and was associated with improved inspiratory muscle strength, PIF and upper limb function and coordination. Clinical implications Larger, longer-term randomised controlled trials are warranted to confirm the safety and efficacy of IMT as an adjunct respiratory management strategy in children with NMD.
Dynamics and Structural Transformations of Carbon Onion-Like under High-Velocity Impacts

Carbon nano-onions (CNO) are multi-shell fullerenes. In the present work, we used fully atomistic reactive (ReaxFF) molecular dynamics simulations to study the dynamics and structural transformations of CNO structures under high-velocity impacts against a fixed and rigid substrate. We considered single and multi-shell CNO (up to six shells) and at different impact velocities (from 2 up to 7 Km/s). Our results indicated three regimes formed after the CNO impact: slightly deformed CNO (quasi-elastic collision, below 2.0 Km/s), collapsed CNO (inelastic collisions, between 3.0 and 5.0 Km/s) forming a diamondoid-like core, and fragmented CNO yielding linear atomic carbon chains (above 5.0 Km/s). We also discussed the dynamical reconfiguration of carbon-carbon bonds during the collision process. The impact of CNO against the substrate yielded sp3-like bond types for all the used initial velocities. At intermediate velocities (between 3.0 and 5.0 Km/s), the inelastic collision forms diamondoid-like cores by converting a substantial quantity of sp2 bonds into sp3 ones. In the high velocities regime, the total number of sp1, sp2, and sp3 bonds tend to be similar. *Corresponding author. E-mail: ribeirojr@unb.br (Luiz Antônio Ribeiro Júnior). Tel: +55 61 31077700. Preprint submitted to Elsevier September 15, 2021 ar X iv :2 10 9. 06 83 4v 1 [ co nd -m at . m tr lsc i] 1 4 Se p 20 21
Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.
Detecting Safety Problems of Multi-Sensor Fusion in Autonomous Driving

Autonomous driving (AD) systems have been thriving in recent years. In general, they receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor inputs, AD systems usually leverage multi-sensor fusion (MSF) to fuse the sensor inputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade Advanced Driver-Assistance System (ADAS) can mislead the car control and result in serious safety hazards. Misbehavior can happen regardless of the used fusion methods and the accurate data from at least one sensor. To attribute the safety hazards to a MSF method, we formally deﬁne the fusion errors and propose a way to distinguish safety violations causally induced by such errors. Further, we develop a novel evolutionary-based domain-speciﬁc search framework, FusionFuzz , for the efﬁcient detection of fusion errors. We evaluate our framework on two widely used MSF methods. Experimental results show that FusionFuzz identi-ﬁes more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods under study.
Key elements of disaster mitigation education in inclusive school setting in the Indonesian context

Children with special needs are one of the most vulnerable groups when disasters occur. They are often excluded from any risk reduction conducted during such situations; therefore, introducing disaster mitigation education at the early stage has numerous benefits. This study aims to explore the critical elements of disaster mitigation education, limiting the scope to primary schools in an inclusive setting in Yogyakarta. A qualitative methodology involving focus group discussions and interviews was applied for in-depth exploration and insight into stakeholders’ perspectives on education. This study identified six key elements of inclusive disaster mitigation education in schools: (1) strong initiative to conduct self-initiated disaster risk reduction (DRR) education for all students; (2) modification of infrastructure and learning environment to accommodate children with special needs and other students; (3) broadening learning methods in DRR; (4) child empowerment and meaningful participation; (5) school management awareness and strategies for conducting DRR; (6) extensive stakeholder involvement within disaster mitigation education. These elements are expected to improve implementation of such programmes, thereby increasing the quality and accessibility of children’s disaster mitigation education, as well as increasing their capacity in the risk reduction process through teacher support.
A national‐scale dataset for threats impacting Australia’s imperiled flora and fauna

Abstract Australia is in the midst of an extinction crisis, having already lost 10% of terrestrial mammal fauna since European settlement and with hundreds of other species at high risk of extinction. The decline of the nation's biota is a result of an array of threatening processes; however, a comprehensive taxon‐specific understanding of threats and their relative impacts remains undocumented nationally. Using expert consultation, we compile the first complete, validated, and consistent taxon‐specific threat and impact dataset for all nationally listed threatened taxa in Australia. We confined our analysis to 1,795 terrestrial and aquatic taxa listed as threatened (Vulnerable, Endangered, or Critically Endangered) under Australian Commonwealth law. We engaged taxonomic experts to generate taxon‐specific threat and threat impact information to consistently apply the IUCN Threat Classification Scheme and Threat Impact Scoring System, as well as eight broad‐level threats and 51 subcategory threats, for all 1,795 threatened terrestrial and aquatic threatened taxa. This compilation produced 4,877 unique taxon–threat–impact combinations with the most frequently listed threats being Habitat loss, fragmentation, and degradation (n = 1,210 taxa), and Invasive species and disease (n = 966 taxa). Yet when only high‐impact threats or medium‐impact threats are considered, Invasive species and disease become the most prevalent threats. This dataset provides critical information for conservation action planning, national legislation and policy, and prioritizing investments in threatened species management and recovery.
Dodging Attack Using Carefully Crafted Natural Makeup

Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e.g., airports). Previous studies have demonstrated the use of adversarial machine learning (AML) attacks to successfully evade identification by such systems, both in the digital and physical domains. Attacks in the physical domain, however, require significant manipulation to the human participant's face, which can raise suspicion by human observers (e.g. airport security officers). In this study, we present a novel black-box AML attack which carefully crafts natural makeup, which, when applied on a human participant, prevents the participant from being identified by facial recognition models. We evaluated our proposed attack against the ArcFace face recognition model, with 20 participants in a real-world setup that includes two cameras, different shooting angles, and different lighting conditions. The evaluation results show that in the digital domain, the face recognition system was unable to identify all of the participants, while in the physical domain, the face recognition system was able to identify the participants in only 1.22% of the frames (compared to 47.57% without makeup and 33.73% with random natural makeup), which is below a reasonable threshold of a realistic operational environment.
Martingale solutions of the stochastic Hall-magnetohydrodynamics equations on $\mathbb{R}^3$

We prove the existence of a global martingale solution of a stochastic Hall-magnetohydrodynamics equations on R with multiplicative noise. Using the Fourier analysis we construct a sequence of approximate solutions. The existence of a solution is proved via the stochastic compactness method and the Jakubowski generalization of the Skorokhod theorem for nonmetric spaces, in particular, the spaces with weak topologies. The main difficulty is caused by the Hall term which makes the equations strongly nonlinear.
Learnable Discrete Wavelet Pooling (LDW-Pooling) for Convolutional Networks

Pooling is a simple but essential layer in modern deep CNN architectures for feature aggregation and extraction. Typical CNN design focuses on the conv layers and activation functions, while leaving the pooling layers with fewer options. We introduce the Learning Discrete Wavelet Pooling (LDW-Pooling) that can be applied universally to replace standard pooling operations to better extract features with improved accuracy and efficiency. Motivated from the wavelet theory, we adopt the low-pass (L) and high-pass (H) filters horizontally and vertically for pooling on a 2D feature map. Feature signals are decomposed into four (LL, LH, HL, HH) subbands to retain features better and avoid information dropping. The wavelet transform ensures features after pooling can be fully preserved and recovered. We next adopt an energy-based attention learning to fine-select crucial and representative features. LDW-Pooling is effective and efficient when compared with other state-of-the-art pooling techniques such as WaveletPooling and LiftPooling. Extensive experimental validation shows that LDW-Pooling can be applied to a wide range of standard CNN architectures and consistently outperform standard (max, mean, mixed, and stochastic) pooling operations.
A Temporal Variational Model for Story Generation

Recent language models can generate interesting and grammatically correct text in story generation but often lack plot development and long-term coherence. This paper experiments with a latent vector planning approach based on a TD-VAE (Temporal Difference Variational Autoencoder), using the model for conditioning and reranking for text generation. The results demonstrate strong performance in automatic cloze and swapping evaluations. The human judgments show stories generated with TD-VAE reranking improve on a GPT-2 medium baseline and show comparable performance to a hierarchical LSTM reranking model. Conditioning on the latent vectors proves disappointing and deteriorates performance in human evaluation because it reduces the diversity of generation, and the models don't learn to progress the narrative. This highlights an important difference between technical task performance (e.g. cloze) and generating interesting stories.
DIISC-I: The Discovery of Kinematically Anomalous H i Clouds in M 100

We report the discovery of two kinematically anomalous atomic hydrogen (H i) clouds in M 100 (NGC 4321), which was observed as part of the Deciphering the Interplay between the Interstellar medium, Stars, and the Circumgalactic medium (DIISC) survey in H i 21 cm at 3.3 km s−1 spectroscopic and 44″ × 30″ spatial resolution using the Karl G. Jansky Very Large Array. 15 15 The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. These clouds were identified as structures that show significant kinematic offsets from the rotating disk of M 100. The velocity offsets of 40 km s−1 observed in these clouds are comparable to the offsets seen in intermediate-velocity clouds (IVCs) in the circumgalactic medium (CGM) of the Milky Way and nearby galaxies. We find that one anomalous cloud in M 100 is associated with star-forming regions detected in Hα and far-ultraviolet imaging. Our investigation shows that anomalous clouds in M 100 may originate from multiple mechanisms, such as star formation feedback-driven outflows, ram pressure stripping, and tidal interactions with satellite galaxies. Moreover, we do not detect any cool CGM at 38.8 kpc from the center of M 100, giving an upper limit of N(H i) ≤1.7 × 1013 cm−2 (3σ). Since M 100 is in the Virgo cluster, the nonexistence of neutral/cool CGM is a likely pathway for turning it into a red galaxy. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Learning Constraints and Descriptive Segmentation for Subevent Detection

Event mentions in text correspond to real-world events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes. Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EventSeg) as an auxiliary task to improve the learning for subevent detection. To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction, as well as guiding the model to make globally consistent inference. Specifically, we adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model. Experimental results show that the proposed method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for subevent detection, HiEve and IC, respectively, while achieving a decent performance on EventSeg prediction.
Role of prostaglandin E2 in tissue repair and regeneration

Tissue regeneration following injury from disease or medical treatment still represents a challenge in regeneration medicine. Prostaglandin E2 (PGE2), which involves diverse physiological processes via E-type prostanoid (EP) receptor family, favors the regeneration of various organ systems following injury for its capabilities such as activation of endogenous stem cells, immune regulation, and angiogenesis. Understanding how PGE2 modulates tissue regeneration and then exploring how to elevate the regenerative efficiency of PGE2 will provide key insights into the tissue repair and regeneration processes by PGE2. In this review, we summarized the application of PGE2 to guide the regeneration of different tissues, including skin, heart, liver, kidney, intestine, bone, skeletal muscle, and hematopoietic stem cell regeneration. Moreover, we introduced PGE2-based therapeutic strategies to accelerate the recovery of impaired tissue or organs, including 15-hydroxyprostaglandin dehydrogenase (15-PGDH) inhibitors boosting endogenous PGE2 levels and biomaterial scaffolds to control PGE2 release.
Dynamic selection for forage quality and quantity in response to phenology and insects in an Arctic ungulate

Abstract Spatiotemporal variation in forage is a primary driver of ungulate behavior, yet little is known about the nutritional components they select, and how selection varies across the growing season with changes in forage quality and quantity. We addressed these uncertainties in barren‐ground caribou (Rangifer tarandus), which experience their most important foraging opportunities during the short Arctic summer. Recent declines in Arctic caribou populations have raised concerns about the influence of climate change on summer foraging opportunities, given shifting vegetation conditions and insect harassment, and their potential effects on caribou body condition and demography. We examined Arctic caribou selection of summer forage by pairing locations from females in the Central Arctic Herd of Alaska with spatiotemporal predictions of biomass, digestible nitrogen (DN), and digestible energy (DE). We then assessed selection for these nutritional components across the growing season at landscape and patch scales, and determined whether foraging opportunities were constrained by insect harassment. During early summer, at the landscape scale, caribou selected for intermediate biomass and high DN and DE, following expectations of the forage maturation hypothesis. At the patch scale, however, caribou selected for high values of all forage components, particularly DN, suggesting that protein may be limiting. During late summer, after DN declined below the threshold for protein gain, caribou exhibited a switch at both spatial scales, selecting for higher biomass, likely enabling mass and fat deposition. Mosquito activity strongly altered caribou selection of forage and increased their movement rates, while oestrid fly activity had little influence. Our results demonstrate that early and late summer periods afford Arctic caribou distinct foraging opportunities, as they prioritize quality earlier in the summer and quantity later. Climate change may further constrain caribou access to DN as earlier, warmer Arctic summers may be associated with reduced DN and increased mosquito harassment.
Tunneling dynamics in cosmological bounce models

Quasiclassical methods are used to define dynamical tunneling times in models of quantum cosmological bounces. These methods provide relevant new information compared with the traditional treatment of quantum tunneling by means of tunneling probabilities. As shown here, the quantum dynamics in bounce models is not secure from reaching zero scale factor, re-opening the question of how the classical singularity may be avoided. Moreover, in the examples studied here, tunneling times remain small even for large barriers, highlighting the quantum instability of underlying bounce models.
Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

Background Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are urgently needed. Objectives Predicting CD using electronic health record (EHR) data would allow for earlier diagnosis and treatment of the condition, thus improving overall cardiovascular outcomes. The Guideline Advantage (TGA) is an American Heart Association ambulatory quality clinical data registry of EHR data representing 70 clinics distributed throughout the US, and has been used to monitor outpatient prevention and disease management outcome measures across populations and for longitudinal research on the impact of preventative care. Methods For this study, we represented all time-series cardiovascular health (CVH) measures and the corresponding data collection time points for each patient by numerical embedding vectors. We then employed a deep learning technique–long-short term memory (LSTM) model–to predict CD from the vector of time-series CVH measures by 5-fold cross validation and compared the performance of this model to the results of deep neural networks, logistic regression, random forest, and Naïve Bayes models. Results We demonstrated that the LSTM model outperformed other traditional machine learning models and achieved the best prediction performance as measured by the average area under the receiver operator curve (AUROC): 0.76 for LSTM, 0.71 for deep neural networks, 0.66 for logistic regression, 0.67 for random forest, and 0.59 for Naïve Bayes. The most influential feature from the LSTM model were blood pressure. Conclusions These findings may be used to prevent CD in the outpatient setting by encouraging appropriate surveillance and management of CVH.
Exploration in Deep Reinforcement Learning: A Comprehensive Survey

—Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved signiﬁcant successes across a wide range of domains, including game AI, autonomous vehicles, robotics and so on. However, DRL and deep MARL agents are widely known to be sample-inefﬁcient that millions of interactions are usually needed even for relatively simple problem settings, thus preventing the wide application and deployment in real-industry scenarios. One bottleneck challenge behind is the well-known exploration problem, i.e., how to efﬁciently explore the environment and collect informative experiences that could beneﬁt policy learning towards the optimal ones. This problem becomes more challenging in complex environments with sparse rewards, noisy distractions, long horizons, and non-stationary co-learners. In this paper, we conduct a comprehensive survey on existing exploration methods for both single-agent and multi-agent RL. We start the survey by identifying several key challenges to efﬁcient exploration. Then we provide a systematic survey of existing approaches by classifying them into two major categories: uncertainty-oriented exploration and intrinsic motivation-oriented exploration. Beyond the above two main branches, we also include other notable exploration methods with different ideas and techniques. In addition to algorithmic analysis, we provide a comprehensive and uniﬁed empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. According to our algorithmic and empirical investigation, we ﬁnally summarize the open problems of exploration in DRL and deep MARL and point out a few future directions.