Miracle-dz/UniFAR-FTUs huggingface dataset:
S0025315419001218jra 115..120

For many benthic marine invertebrates, inversion (being turned upside-down) is a common event that can increase vulnerability to predation, desiccation and unwanted spatial transport, and requires behavioural ‘self-righting’ to correct. While self-righting behaviour has been studied for more than a century, the repeatability (R) – the portion of behavioural variance due to inter-individual differences – of this trait is not well understood. Heritability and the evolution of animal behaviour rely on behavioural repeatability. Here, we examined the self-righting technique of a cold-water holothurid, Parastichopus tremulus, and assessed the repeatability of this behaviour. Under laboratory conditions, P. tremulus consistently used muscle contractions to curl its body and roll itself back to an upright position, which provided for rapid (x̅ ± SD = 96.7 ± 49.8 s) and highly repeatable (R = 0.75) self-righting in the short term that varied between individuals (range of individual average righting times = 34.8–217.0 s). Righting time tended to increase with animal size; however, substantial variation was evident at comparable sizes, as average righting time ranged from 34.8–155.5 s for animals ∼20 cm in body length. Contrary to previous studies on other echinoderms, we found no evidence of improved righting times for P. tremulus over time. This study ultimately provides the first detailed documentation of self-righting behaviour for P. tremulus and suggests that this species displays a high degree of repeatability for this trait in the short term.
S0025315419000778jra 1779..1786

Limpets and barnacles are important components of intertidal assemblages worldwide. This study examines the effects of barnacles on the foraging behaviour of the limpet Patella vulgata, which is the main algal grazer in the North-west Atlantic. The behaviour of limpets on a vertical seawall on the Isle of Man (UK) was investigated using autonomous radio-telemetry, comparing their activity patterns on plots characterized by dense barnacle cover and plots from which the barnacles had been removed. Limpet behaviour was investigated at midshore level, but two different elevations were considered. This experiment revealed a significant effect of barnacle cover on the activity of P. vulgata. Limpets on smooth surfaces spent a greater proportion of total time active than did limpets on barnacles. Movement activity was also greater in areas that were lower down in the tidal range. In general, limpets were either predominantly active during diurnal high or nocturnal low tides and always avoided nocturnal high tides. Individuals on barnacles at the higher elevation concentrated their activity during nocturnal low water. All the other groups of limpets (smooth surfaces on the upper level and all individuals on the lower shore) had more excursions centred around daylight hours with an equal distribution of activity between periods of low and high water. Inter-individual variability was, however, pronounced.
S0025315419000456jra 1507..1517

The South China Sea (SCS) is a biodiversity hotspot, however, most biodiversity surveys in the region are confined to shallow water reefs. Here, we studied the benthic habitat and fish assemblages in the upper mesophotic coral ecosystems (MCEs; 30–40 m) and SWRs (8–22 m) at three geographic locations (Luzon Strait; Palawan; and the Kalayaan Group of Islands) in the eastern SCS (also called the West Philippine Sea) using diver-based survey methods. Mean coral genera and fish species richness ranged from 17–25 (per 25 m) and 11–17 (per 250 m) in MCEs, respectively; although none of these were novel genera/species. Coral and fish assemblages were structured more strongly by location than by depth. Location differences were associated with the variability in benthic composition, wherein locations with higher hard coral cover had higher coral genera richness and abundance. Locations with higher algae and sand cover had higher diversity and density of fish herbivores and benthic invertivores. Fishing efforts may also have contributed to among-location differences as the highly exploited location had the lowest fish biomass. The low variation between depths may be attributed to the similar benthic composition at each location, the interconnectivity between depths due to hydrological conditions, fish motility, and the common fishing gears used in the Philippines that can likely extend beyond SWRs. Results imply that localscale factors and anthropogenic disturbances probably dampen across-depth structuring in coral genera and fish species assemblages.
A metal-free, carbon-based catalytic system for the oxidation of lignin model compounds and lignin

Nitrogen-containing graphene material (LCN) has been identified as an effective catalyst for the oxidation of b-O-4 and a-O- 4 types of lignin model compounds in the presence of tertbutyl hydroperoxide, to provide aromatic aldehydes, acids and other organic chemicals in high yield. The transformations of five lignin model compounds over LCN were investigated systematically. Instrumentation analysis, kinetic study and radical trapping experiments highlight the mechanistic features of the reaction, including: 1) the reaction pathway starts by benzylic CH or COH bond activation, followed by CaCb or CaO bond cleavage, and finally further oxidation of intermediate aromatics; and 2) the reaction follows a free-radical mechanism with all the key steps involving radical species. In addition, the LCN proved to be a highly stable catalyst; no significant activity decrease was observed for four consecutive runs, and X-ray photoelectron spectroscopy analysis indicates negligible decrease in the content of the active nitrogen species in the catalyst. Notably, this new catalytic system can be extended to the oxidative depolymerisation of real lignin, to produce a significant portion of liquefied, low-molecular-mass products.
Neural Incremental Speech Recognition Through Attention Transfer

One of the challenges that have to be confronted to achieve a simultaneous speech translation system is the incremental ASR (ISR) development. Hidden Markov model (HMM) ASR [10, 5] performs a low-delay recognition, but it cannot do an end-toend modeling. The state-of-the-art end-to-end ASR [8, 4] with an attention-based sequence-to-sequence (seq2seq) deep learning framework has a long delay due the necessity to attend a complete input. Neural transducer (NT) framework [7] previously proposed for the seq2seq incremental recognition by segment-based recognition. It is done by learning the segment-level alignments from an external system such as forced-alignment by HMM ASR or from the alignment approximation based on the training states. The existing ISR framework has a more complicated mechanism than the standard ASR because of the incremental step learning and its preparation. In this work, we constructed incremental ASR (ISR) for a low-latency recognition by exploiting an attentionbased non-incremental ASR framework. Here we refer our approach as attention-transfer ISR (AT-ISR). The non-incremental ASR is treated as a teacher to teach the ISR through attention transfer. Our experiment shows that our ISR CER with a delay 0.54 sec is only 2% behind the non-incremental model with a delay more than 6 sec.
Title: Additional Queen Square (QS) screening items improve the test accuracy of the Montreal Cognitive Assessment (MoCA) after acute stroke

Edgar Chan , Eva Garritsen, Samantha Altendorff, David Turner, Robert Simister, David J. Werring, Lisa Cipolotti a. National Hospital for Neurology and Neurosurgery, Dept. of Neuropsychology, London, United Kingdom. b. UCL Queen Square Institute of Neurology, Stroke Research Centre, London, United Kingdom. c. Comprehensive Stroke Service, University College London Hospital, London, United Kingdom.
Enhancement of Rotator Cuff Tendon-bone Healing with Graphene Oxide/PRP Composite Scaffold in a Rabbit Model: A Histological and Biomechanical Study

Bo Qin A liated Traditional Chinese Medicine Hospital of Southwest Medical University https://orcid.org/0000-0002-4283-4041 Dingsu Bao A liated Traditional Chinese Medicine Hospital of Southwest Medical University Shengqiang Zeng A liated Traditional Chinese Medicine Hospital of Southwest Medical University Kai Deng A liated Traditional Chinese Medicine Hospital of Southwest Medical University Gang Liu A liated Traditional Chinese Medicine Hospital of Southwest Medical University Zhengrong Xiong A liated Traditional Chinese Medicine Hospital of Southwest Medical University Min Gong Chengdu University of Traditional Chinese Medicine A liated Hospital Siyuan He Southern Medical University Jie Shi A liated Traditional Chinese Medicine Hospital of Southwest Medical University Xun Wang A liated Traditional Chinese Medicine Hospital of Southwest Medical University Zhiqiang Lu A liated Traditional Chinese Medicine Hospital of Southwest Medical University Shijie Fu (  fushijieggj@126.com ) A liated Traditional Chinese Medicine Hospital of Southwest Medical University https://orcid.org/0000-0002-2168-6094
Effect of the self-interaction error for three-electron bonds: On the development of new exchange-correlation functionals

The dissociation behavior as well as the equilibrium properties of radical cations with three-electron bonds, namely He2+˙, N2H6+˙, O2H4+˙, F2H2+˙, and Ne2+˙ are investigated using standard and self-interaction-corrected density functional theory (SIC-DFT) in connection with a variety of pure and hybrid exchange-correlation (XC) functionals. The impact of the self-interaction error (SIE) on the results of standard DFT is analyzed considering the individual orbital contributions to the SIE, the dependence of the SIE on the separation distance between the dissociation fragments, and its impact on the equilibrium properties of 2–6. A local analysis of the SIE in terms of exact and DFT exchange holes reveals that the SIE mimics not only non-dynamic but also an increasing amount of dynamic electron correlation effects as the number of valence electrons is enlarged. Standard DFT describes the dissociation of three-electron bonds qualitatively incorrectly. This can be traced back in the first instance to the SIE of the bonding β electron, which mimics a spurious long-range correlation with a non-existing delocalized α electron in the same bond. A comparison of the covalent (symmetric) and ionic (symmetry-broken) state of radical cations 2–6 at large interaction distances provides further insight in the inconsistencies of the DFT description: (i) Not only the SIE but also the approximate description of the interelectronic exchange contributes to the incorrect description of the dissociation. (ii) Dissociating three-electron bonds show a specific form of long-range correlation effects, which is neither accounted for by standard DFT, SIC-DFT nor Hartree–Fock theory. Indeed, SIC-DFT provides a qualitatively better description of the dissociation of radical cations, however in general a poor performance when describing equilibrium properties. There is no need for SIC-DFT methods. Instead, there is need for XC functionals with exact exchange and long-range correlation effects (e.g. mimicked by the exchange SIE) absorbed in the correlation functional. Implications of our findings for the construction of new density functionals are discussed.
Active Tree Search in Large POMDPs

The ability to plan ahead efficiently is central for both living organisms and artificial systems. Model-based planning and prospection are widely studied in both cognitive neuroscience and artificial intelligence (AI), but from different perspectives—and with different desiderata in mind (biological realism versus scalability) that are difficult to reconcile. Here, we introduce a novel method to plan in large POMDPs— Active Tree Search —that combines the normative character and biological realism of a leading planning theory in neuroscience (Active Inference) and the scalability of Monte-Carlo methods in AI. This unification is beneficial for both approaches. On the one hand, using Monte-Carlo planning permits scaling up the biologically grounded approach of Active Inference to large-scale problems. On the other hand, the theory of Active Inference provides a principled solution to the balance of exploration and exploitation, which is often addressed heuristically in Monte-Carlo methods. Our simulations show that Active Tree Search successfully navigates binary trees that are challenging for sampling-based methods, problems that require adaptive exploration, and the large POMDP problem ‘Rocksample’. Furthermore, we illustrate how Active Tree Search can be used to simulate neurophysiological responses (e.g., in the hippocampus and prefrontal cortex) of humans and other animals that contain large planning problems. These simulations show that Active Tree Search is a principled realisation of neuroscientific and AI theories of planning, which offers both biological realism and scalability.
Nocturnal Seizure Detection Using Off-the-Shelf WiFi

The detection of nocturnal seizures in epilepsy patients is essential, both for the quick management of the seizure complications, and for the assessment of the ongoing seizure treatment. Traditional seizure detection products (e.g., wearables), however, are either very costly, uncomfortable, or unreliable. In this article, we then propose to utilize everyday WiFi signals for robust, fast, and noninvasive detection of nocturnal seizures. We first present a new and rigorous mathematical characterization for the spectral content/bandwidth of the WiFi signal, measured on a WiFi device placed near a sleeping patient, during different kinds of sleep motions: seizures, normal movements (e.g., posture adjustments), and breathing. Based on this mathematical modeling, we propose a novel pipeline for processing the received WiFi signals to robustly detect all nocturnal nonbreathing movements, and then classify them into normal body movements or seizures. In order to validate this, we carry out extensive experiments in seven different typical bedroom locations, where a set of 20 actors simulate the state of having seizures (a total of 260 instances), as well as normal sleep movements (a total of 410 instances). Our proposed system detects 93.85% of the seizures with a mean response time (MRT) of only 5.69 s since the onset of the seizure. Moreover, our proposed system achieves a probability of false alarm of only 0.0097, when classifying normal sleep movements. Overall, our new mathematical modeling and experimental results show the great potential the ubiquitous WiFi signals have for detecting nocturnal seizures, which can provide better support for epilepsy patients and their caregivers.
Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.
Verifying Compositional Refinement of Assume/Guarantee Contracts using Linear Programming

Verifying specifications for large-scale modern engineering systems can be a time-consuming task, as most formal verification methods are limited to systems of modest size. Recently, contract-based design and verification has been proposed as a modular framework for specifications, and linear-programming-based techniques have been presented for verifying that a given system satisfies a given contract. In this paper, we extend this assume/guarantee framework by presenting necessary and sufficient conditions for a collection of contracts on individual components to refine a contract on the composed system. These conditions can be verified by solving linear programs, whose number grows linearly with the number of specifications defined by the contracts. We exemplify the tools developed using a case study considering safety in a car-following scenario, where noise and time-varying delay are considered.
Evaluation of Sampling-Based Optimizing Planners for Outdoor Robot Navigation

Sampling-Based Optimal(SBO) path planning has been mainly used for robotic arm manipulation tasks. Several research works have been carried out in order to evaluate performances of various SBO planners for arm manipulation. However, not much of work is available that highlights performances of SBO planners in context of mobile robot navigation in outdoor 3D environments. This paper evaluates performances of major SBO planners in Open Motion Planning Library(OMPL) for that purpose. Due to large number of existing SBO planners, experimenting and selecting a proper planner for a planning problem can be burdensome and ambiguous. SBO planner's probabilistic nature can also add a bias to this procedure. To address this, we evaluate performances of all available SBO planners in OMPL with a randomized planning problem generation method iteratively. Evaluations are done in various state spaces suiting for different differential constraints of mobile robots. The planning setups are focused for navigation of mobile robots in outdoor environments. The outdoor environment representation is done with prebuilt OctoMaps, collision checks are performed between a 3D box representing robot body and OctoMap for validation of sampled states. Several evaluation metrics such as resulting path's length, smoothness and status of acquired final solutions are selected. According to selected metrics, performances from different SBO planners are presented comparatively. Experimental results shows the significance of parallel computing towards quicker convergence rates for optimal solutions. Several SBO methods that takes advantage of parallel computing produced better results consistently in all state spaces for different planning inquiries.
Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints that encode heterogeneous behaviors continuously. In this article, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of Long-Short Term Memory (LSTM) and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.
Enabling Incremental Training with Forward Pass for Edge Devices

Deep Neural Networks (DNNs) are commonly deployed on end devices that exist in constantly changing environments. In order for the system to maintain it's accuracy, it is critical that it is able to adapt to changes and recover by retraining parts of the network. However, end devices have limited resources making it challenging to train on the same device. Moreover, training deep neural networks is both memory and compute intensive due to the backpropagation algorithm. In this paper we introduce a method using evolutionary strategy (ES) that can partially retrain the network enabling it to adapt to changes and recover after an error has occurred. This technique enables training on an inference-only hardware without the need to use backpropagation and with minimal resource overhead. We demonstrate the ability of our technique to retrain a quantized MNIST neural network after injecting noise to the input. Furthermore, we present the micro-architecture required to enable training on HLS4ML (an inference hardware architecture) and implement it in Verilog. We synthesize our implementation for a Xilinx Kintex Ultrascale Field Programmable Gate Array (FPGA) resulting in less than 1% resource utilization required to implement the incremental training.
Predicting Directionality in Causal Relations in Text

In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content. Our preliminary results show that predicting direction for inter-sentence and implicit causal relations is more challenging. And, SpanBERT performs better than BERT on causal samples with longer span length. We also introduce CREST which is a framework for unifying a collection of scattered datasets of causal relations.
Probable observation of the nuclear Cooper pair mean square radius in superfluid nuclei

R. A. Broglia,1, 2 F. Barranco,3 G. Potel,4 and E. Vigezzi5 1The Niels Bohr Institute, University of Copenhagen, DK-2100 Copenhagen, Blegdamsvej 17, Denmark 2Dipertimento di Fisica, Università degli Studi di Milano, Via Celoria 16, I-20133 Milano, Italy 3Departamento de Fı́sica Aplicada III, Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, Sevilla, Spain 4Lawrence Livermore National Laboratory, Livermore, California 94550, USA 5INFN Sezione di Milano, Via Celoria 16, I-20133 Milano, Italy (Dated: March 26, 2021)