Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling ADHD in Drosophila: Investigating the Effects of Glucose on Dopamine Production Demonstrated by Locomotion

Hyperactivity is one of the hallmarks of attentiondeficit/hyperactivity disorder (ADHD), which is a common, highly heritable neuropsychiatric disorder. Aberrant dopamine signaling is a major theme in ADHD, and dopamine production is directly linked to the intensity and persistence of hyperactive conduct. The strength and persistence of hyperactivity responses in Drosophila to startle stimuli were measured in a study to determine the effects of sugar on dopamine development. The effects of sugar on dopamine production were modeled using Drosophila melanogaster. A total of four experimental groups, namely 1%, 3%, and 5% glucose, as well as a control group (without glucose) were taken for the diet of Drosophila, and these four different amounts of glucose were introduced to the growth medium where Drosophila was cultured. The movements of Drosophila in the four treatment groups were captured using a camera. This experiment was carried out five times, each time using a different batch of Drosophila. Each group's average velocity over time was also reported. The web adaptation of the Drosophila Activity Monitor (DAM) device was used to analyze the captured movies from the camera. The discrepancy between the test groups' resting velocities and the control group's resting velocities was statistically significant (p0.05), while the ΔV values of the 5% glucose and 3% glucose groups were statistically distinct from the control group and one another (p0.05) in terms of hyperactivity intensity but the difference in ΔV values between the 1% glucose and control groups, however, was not statistically significant (p0.05). Furthermore, when it came to hyperactivity persistence, all four treatment classes were statistically different (p0.05). Since the strength and persistence of hyperactive behavior are directly correlated to dopamine output, this study shows that higher glucose intake is associated with more hyperactivity, for both the intensity (ΔV) and persistence (cool down time).
Robotic Waste Sorter With Agile Manipulation and Quickly Trainable Detector

Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neural–network model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.
Temporal Deep Unfolding for Nonlinear Maximum Hands-off Control

This paper proposes a computational technique based on"deep unfolding"to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems. In particular, we seek a sparse control input sequence that stabilizes the system such that the expected value of the square of the final states is small by training a deep neural network. The proposed technique is demonstrated by a numerical experiment.
Calibration of the Hα Age–Activity Relation for M Dwarfs

In this work, we calibrate the relationship between Hα emission and M dwarf ages. We compile a sample of 892 M dwarfs with Hα equivalent width (Hα EW) measurements from the literature that are either comoving with a white dwarf of known age (21 stars) or in a known young association (871 stars). In this sample we identify 7 M dwarfs that are new candidate members of known associations. By dividing the stars into active and inactive categories according to their Hα EW and spectral type (SpT), we find that the fraction of active dwarfs decreases with increasing age, and the form of the decline depends on SpT. Using the compiled sample of age calibrators, we find that Hα EW and fractional Hα luminosity (L Hα /L bol) decrease with increasing age. Hα EW for SpT ≤ M7 decreases gradually up until ∼1 Gyr. For older ages, we found only two early M dwarfs that are both inactive and seem to continue the gradual decrease. We also found 14 mid-type M dwarfs, out of which 11 are inactive and present a significant decrease in Hα EW, suggesting that the magnetic activity decreases rapidly after ∼1 Gyr. We fit L Hα /L bol versus age with a broken power law and find an index of for ages ≲776 Myr. The index becomes much steeper at older ages, but a lack of field age-calibrators (≫1 Gyr) leaves this part of the relation far less constrained. Finally, from repeated independent measurements for the same stars, we find that 94% of them have a level of Hα EW variability ≤5 Å at young ages (<1 Gyr).
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model. Citrinet is deep residual neural model which uses 1D time-channel separable convolutions combined with sub-word encoding and squeeze-and-excitation. The resulting architecture significantly reduces the gap between non-autoregressive and sequence-to-sequence and transducer models. We evaluate Citrinet on LibriSpeech, TED-LIUM2, AISHELL-1 and Multilingual LibriSpeech (MLS) English speech datasets. Citrinet accuracy on these datasets is close to the best autoregressive Transducer models.
FixMyPose: Pose Correctional Captioning and Retrieval

Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend, and people globally enjoy such exercises at home/office via video streaming platforms. However, such exercises are hard to follow without expert guidance. Even if experts can help, it is almost impossible to give personalized feedback to every trainee remotely. Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect natural language descriptions of correcting a “current” pose to look like a “target” pose. To support a multilingual setup, we collect descriptions in both English and Hindi. The collected descriptions have interesting linguistic properties such as egocentric relations to the environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our FixMyPose dataset, we introduce two tasks: the pose-correctional-captioning task and its reverse, the target-pose-retrieval task. During the correctional-captioning task, models must generate the descriptions of how to move from the current to the target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and the correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. Finally, to verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images. Data and code are available: https://fixmypose-unc.github. io.
Automating Transfer Credit Assessment in Student Mobility - A Natural Language Processing-based Approach

Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate this process. Given the unique structure, domain specificity, and complexity of learning outcomes (LOs), a need for designing a tailor-made model arises. The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs. The similarity between LOs is further aggregated to form course to course similarity. Due to the lack of quality benchmark datasets, a new benchmark dataset containing seven course-to-course similarity measures is proposed. Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different scenarios. While providing an efficient model to assess the similarity between courses with existing resources, this research work steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.
On random compact sets, equidecomposition, and domains of expansion in R^3

We study random compact subsets of R3 which can be described as ”random Menger sponges”. We use those random sets to construct a pair of compact sets A and B in R3 which are of the same positive measure, such that A can be covered by finitely many translates of B, B can be covered by finitely many translates of A, and yet A and B are not equidecomposable. Furthermore, we construct the first example of a compact subset of R3 of positive measure which is not a domain of expansion. This answers a question of Adrian Ioana.
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grade RGB-D cameras suffers from low spatial resolution. Guided depth map super-resolution (DSR) is a popular approach to address this problem, which attempts to restore a high-resolution (HR) depth map from the input low-resolution (LR) depth and its coupled HR RGB image that serves as the guidance. The most challenging issue for guided DSR is how to correctly select consistent structures and propagate them, and properly handle inconsistent ones. In this paper, we propose a novel attention-based hierarchical multi-modal fusion (AHMF) network for guided DSR. Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhancement block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics. Furthermore, we propose a bi-directional hierarchical feature collaboration (BHFC) module to fully leverage low-level spatial information and high-level structure information among multi-scale features. Experimental results show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
Using Convolutional Neural Networks to Develop Starting Models for 2D Full Waveform Inversion

Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change engineering site characterization by enabling the recovery of high resolution 2D/3D maps of subsurface stiffness. Yet, the accuracy of FWI remains quite sensitive to the choice of the initial starting model due to the complexity and non-uniqueness of the inverse problem. In response, we present the novel application of convolutional neural networks (CNNs) to transform an experimental seismic wavefield acquired using a linear array of surface sensors directly into a robust starting model for 2D FWI. We begin by describing three key steps used for developing the CNN, which include: selection of a network architecture, development of a suitable training set, and performance of network training. The ability of the trained CNN to predict a suitable starting model for 2D FWI was compared against other commonly used starting models for a classic near-surface imaging problem; the identification of an undulating, two-layer, soil-bedrock interface. The CNN developed during this study was able to predict complex 2D subsurface images of the testing set directly from their seismic wavefields with an average mean absolute percent error of 6%. When compared to other common approaches, the CNN approach was able to produce starting models with smaller seismic image and waveform misfits, both before and after FWI. The ability of the CNN to generalize to subsurface models which were dissimilar to the ones upon which it was trained was assessed using a more complex, three-layered model. While the predictive ability of the CNN was slightly reduced, it was still able to achieve seismic image and waveform misfits comparable to the other commonly used starting models. This study demonstrates that CNNs have great potential as a tool for developing good starting models for FWI .
Synergies between Vera C. Rubin Observatory, Nancy Grace Roman Space Telescope, and Euclid Mission: Constraining Dark Energy with Type Ia Supernovae

We review the needs of the supernova community for improvements in survey coordination and data sharing that would significantly boost the constraints on dark energy using samples of Type Ia supernovae from the Vera C. Rubin Observatories, the \textit{Nancy Grace Roman Space Telescope}, and the \textit{Euclid} Mission. We discuss improvements to both statistical and systematic precision that the combination of observations from these experiments will enable. For example, coordination will result in improved photometric calibration, redshift measurements, as well as supernova distances. We also discuss what teams and plans should be put in place now to start preparing for these combined data sets. Specifically, we request coordinated efforts in field selection and survey operations, photometric calibration, spectroscopic follow-up, pixel-level processing, and computing. These efforts will benefit not only experiments with Type Ia supernovae, but all time-domain studies, and cosmology with multi-messenger astrophysics.
High pCO2 Reduces Sensitivity to CO2 Perturbations on Temperate, Earth-Like Planets Throughout Most of Habitable Zone.

The nearly logarithmic radiative impact of CO2 means that planets near the outer edge of the liquid water habitable zone (HZ) require ∼106 × more CO2 to maintain temperatures that are conducive to standing liquid water on the planetary surface than their counterparts near the inner edge. This logarithmic radiative response also means that atmospheric CO2 changes of a given mass will have smaller temperature effects on higher pCO2 planets. Ocean pH is linked to atmospheric pCO2 through seawater carbonate speciation and calcium carbonate dissolution/precipitation, and the response of pH to changes in pCO2 also decreases at higher initial pCO2. Here, we use idealized climate and ocean chemistry models to demonstrate that CO2 perturbations large enough to cause catastrophic changes to surface temperature and ocean pH on temperate, low-pCO2 planets in the innermost region of the HZ are likely to have much smaller effects on planets with higher pCO2, as may be the case for terrestrial planets with active carbonate-silicate cycles receiving less instellation than the Earth. Major bouts of extraterrestrial fossil fuel combustion or volcanic CO2 outgassing on high-pCO2 planets in the mid-to-outer HZ should have mild or negligible impacts on surface temperature and ocean pH. Owing to low pCO2, Phanerozoic Earth's surface environment may be unusually volatile compared with similar planets receiving lower instellation.
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffer from modeling superfluous interactions and neglect of trajectory motion tendency, and thus inevitably result in a considerable deviance from the reality. To cope with these issues, we present a Sparse Graph Convolution Network (SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction. Finally, parameters of a bi-Gaussian distribution for trajectory prediction are estimated by fusing the above two sparse graphs. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods by 9% in Average Displacement Error (ADE) and 13% in Final Displacement Error (FDE). Notably, visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
Explanatory models in neuroscience: Part 1 - taking mechanistic abstraction seriously

Despite the recent success of neural network models in mimicking animal performance on visual perceptual tasks, critics worry that these models fail to illuminate brain function. We take it that a central approach to explanation in systems neuroscience is that of mechanistic modeling, where understanding the system is taken to require fleshing out the parts, organization, and activities of a system, and how those give rise to behaviors of interest. However, it remains somewhat controversial what it means for a model to describe a mechanism, and whether neural network models qualify as explanatory. We argue that certain kinds of neural network models are actually good examples of mechanistic models, when the right notion of mechanistic mapping is deployed. Building on existing work on model-to-mechanism mapping (3M), we describe criteria delineating such a notion, which we call 3M++. These criteria require us, first, to identify a level of description that is both abstract but detailed enough to be"runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals. Perhaps surprisingly, the abstractions required are those already in use in experimental neuroscience, and are of the kind deployed in the construction of more familiar computational models, just as the principles of inter-brain mappings are very much in the spirit of those already employed in the collection and analysis of data across animals. In a companion paper, we address the relationship between optimization and intelligibility, in the context of functional evolutionary explanations. Taken together, mechanistic interpretations of computational models and the dependencies between form and function illuminated by optimization processes can help us to understand why brain systems are built they way they are.
Pre-impact dynamics of a droplet impinging on a deformable surface

The non-linear interaction between air and a water droplet just prior to high-speed impingement on a surface is a phenomenon that has been researched extensively and occurs in a number of industrial settings. The role that surface deformation plays in an air cushioned impact of a liquid droplet is considered here. In a two-dimensional framework, assuming small density and viscosity ratios between the air and the liquid, a reduced system of integro-differential equations is derived governing the liquid droplet free-surface shape, the pressure in the thin air film and the deformation of the surface, assuming the effects of surface tension, compressibility and gravity to be negligible. The deformation of the surface is first described in a rather general form, based on previous membrane-type models. The coupled system is then investigated in two cases: a soft viscoelastic case where the substrate stiffness and (viscous) damping are considered and a more general flexible surface where all relevant parameters are retained. Numerical solutions are presented, highlighting a number of key consequences of substrate deformability on the pre-impact phase of droplet impact, such as reduction in pressure buildup, increased air entrapment and considerable delay to touchdown. Connections (including subtle dependence of the size of entrapped air on the droplet velocity, reduced pressure peaks and droplet gliding) with recent experiments and a large deformation analysis are also presented.
Exploring Transformers in Emotion Recognition: a comparison of BERT, DistillBERT, RoBERTa, XLNet and ELECTRA

This paper investigates how Natural Language Understanding (NLU) could be applied in Emotion Recognition, a specific task in affective computing. We finetuned different transformers language models (BERT, DistilBERT, RoBERTa, XLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in terms of performance (f1-score) and time to complete.
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties

Models pre-trained on multiple languages have shown significant promise for improving speech recognition, particularly for low-resource languages. In this work, we focus on phoneme recognition using Allosaurus, a method for multilingual recognition based on phonetic annotation, which incorporates phonological knowledge through a language-dependent allophone layer that associates a universal narrow phone-set with the phonemes that appear in each language. To evaluate in a challenging real-world scenario, we curate phone recognition datasets for Bukusu and Saamia, two varieties of the Luhya language cluster of western Kenya and eastern Uganda. To our knowledge, these datasets are the first of their kind. We carry out similar experiments on the dataset of an endangered Tangkhulic language, East Tusom, a Tibeto-Burman language variety spoken mostly in India. We explore both zero-shot and few-shot recognition by fine-tuning using datasets of varying sizes (10 to 1000 utterances). We find that fine-tuning of Allosaurus, even with just 100 utterances, leads to significant improvements in phone error rates.