Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle-in-Cell Simulations of the Twisted Magnetospheres of Magnetars. I.

The magnetospheres of magnetars are believed to be filled with electron–positron plasma generated by electric discharge. We present a first numerical experiment demonstrating this process in an axisymmetric magnetosphere with a simple threshold prescription for pair creation, which is applicable to the inner magnetosphere with an ultrastrong field. The discharge occurs in response to the twisting of the closed magnetic field lines by a shear deformation of the magnetar surface, which launches electric currents into the magnetosphere. The simulation shows the formation of an electric “gap” with an unscreened electric field ( ) that continually accelerates particles along the magnetic field lines and sustains pair creation. The accelerating voltage is self-regulated to the threshold of the discharge. It controls the rate of energy release and the lifetime of the magnetic twist. The simulation follows the global evolution of the twisted magnetosphere over a long time and demonstrates its gradual resistive untwisting. A vacuum cavity forms near the star and expands, gradually erasing magnetospheric electric currents j. The active j-bundle shrinks with time and its footprints form shrinking hot spots on the magnetar surface bombarded by the created particles.
Characteristics of Day-Roosts Used by Northern Long-Eared Bats (Myotis septentrionalis) in Coastal New York

Abstract - In North America, Myotis septentrionalis (Northern Long-eared Bat) has experienced precipitous declines from white-nose syndrome. As these bats become rare and difficult to capture, additional day-roost assessments to inform management may fill gaps in our understanding, particularly in habitats and regions where such roosts have never been surveyed. Over 2 summers, we radio-tracked 16 individuals from a maternity colony on Long Island, NY, in a small forested patch surrounded by development and ocean. These bats disproportionately selected small, suppressed Robinia pseudoacacia (Black Locust) trees or snags for roosting. Generally, roosts occurred within the interior or edges of this forest patch, rather than surrounding suburbia, reinforcing the hypothesis that Northern Long-eared Bats are forest adapted. Our study shows even small tracts of forest in coastal, urban areas may have conservation value in providing day-roost and foraging habitat.
SIGNATURES OF MRI-DRIVEN TURBULENCE IN PROTOPLANETARY DISKS: PREDICTIONS FOR ALMA OBSERVATIONS

Spatially resolved observations of molecular line emission have the potential to yield unique constraints on the nature of turbulence within protoplanetary disks. Using a combination of local non-ideal magnetohydrodynamics (MHD) simulations and radiative transfer calculations, tailored to properties of the disk around HD 163296, we assess the ability of ALMA to detect turbulence driven by the magnetorotational instability (MRI). Our local simulations show that the MRI produces small-scale turbulent velocity fluctuations that increase in strength with height above the mid-plane. For a set of simulations at different disk radii, we fit a Maxwell–Boltzmann distribution to the turbulent velocity and construct a turbulent broadening parameter as a function of radius and height. We input this broadening into radiative transfer calculations to quantify observational signatures of MRI-driven disk turbulence. We find that the ratio of the peak line flux to the flux at line center is a robust diagnostic of turbulence that is only mildly degenerate with systematic uncertainties in disk temperature. For the CO(3–2) line, which we expect to probe the most magnetically active slice of the disk column, variations in the predicted peak-to-trough ratio between our most and least turbulent models span a range of approximately 15%. Additional independent constraints can be derived from the morphology of spatially resolved line profiles, and we estimate the resolution required to detect turbulence on different spatial scales. We discuss the role of lower optical depth molecular tracers, which trace regions closer to the disk mid-plane where velocities in MRI-driven models are systematically lower.
Impact of Self-Contacts on Perceived Pose Equivalences

Defining equivalences between poses of different human characters is an important problem for imitation research, human pose recognition and deformation transfer. However, pose equivalence is a subjective information that depends on context and on the morphology of the characters. A common hypothesis is that interactions between body surfaces, such as self-contacts, are important attributes of human poses, and are therefore consistently included in animation approaches aiming at retargeting human motions. However, some of these self-contacts are only present because of the morphology of the character and are not important to the pose, e.g. contacts between the upper arms and the torso during a standing A-pose. In this paper, we conduct a first study towards the goal of understanding the impact of self-contacts between body surfaces on perceived pose equivalences. More specifically, we focus on contacts between the arms or hands and the upper body, which are frequent in everyday human poses. We conduct a study where we present to observers two models of a character mimicking the pose of a source character, one with the same self-contacts as the source, and one with one self-contact removed, and ask observers to select which model best mimics the source pose. We show that while poses with different self-contacts are considered different by observers in most cases, this effect is stronger for self-contacts involving the hands than for those involving the arms.
Time Reversal and Simulation Merging for Target-Driven Fluid Animation

We present an approach to control the animation of liquids. The user influences the simulation by providing a target surface which will be matched by a portion of the liquid at a specific frame of the animation; our approach is also effective for multiple target surfaces forming an animated sequence. A source simulation provides the context liquid animation with which we integrate the controlled target elements. From each target frame, we compute a target simulation in two parts, one forward and one backward, which are then joined together. The particles for the two simulations are initially placed on the target shape, with velocities sampled from the source simulation. The backward particles use velocities in the opposite direction as the forward simulation, so that the two halves join seamlessly. When there are multiple target frames, each target frame simulation is computed independently, and the particles from these multiple target simulations are later combined. In turn, the target simulation is joined to the source simulation. Appropriate steps are taken to select which particles to keep when joining the forward, backward, and source simulations. This results in an approach where only a small fraction of the computation time is devoted to the target simulation, allowing faster computation times as well as good turnaround times when designing the full animation. Source and target simulations are computed using an off-the-shelf Lagrangian simulator, making it easy to integrate our approach with many existing animation pipelines. We present test scenarios demonstrating the effectiveness of the approach in achieving a well-formed target shape, while still depicting a convincing liquid look and feel.
Toward Believable Acting for Autonomous Animated Characters

This paper describes design principles and a system, based on reinforcement learning and procedural animation, to create an autonomous character capable of believable acting—exhibiting a responsive and expressive illusion of interactive life, grounded in its subjective experience of its world. The design principles incorporate knowledge from animation, human-computer interaction, and psychology, articulating guidelines that, when followed, support a viewer’s suspension of disbelief. The system’s reinforcement learning brain generates action, emotion, and attention signals based on motivational drives, and its procedural animation system translates those signals into expressive biophysical movement in real time. We demonstrate the system on a stylized quadruped character in a virtual habitat. In a user study, participants rated the character favorably on animacy and ability to experience emotions, which is consistent with finding the character believable.
SPECTROSCOPY AND THERMODYNAMICS OF MSW BLACK HOLE

We study the thermodynamics and spectroscopy of a (2+1)-dimensional black hole proposed by Mandal et al.1 [Mod. Phys. Lett. A6, 1685 (1991)]. We put the background spacetime in Kruskal like co-ordinate and find period with respect to Euclidean time. Different thermodynamic quantities like entropy, specific heat, temperature etc. are obtained. The adiabatic invariant for the black hole is found and quantized using Bohr–Sommerfeld quantization rule. The study shows that the area spectrum of MSW black hole is equally spaced and the value of spacing is found to be ℏ.
How gender affects the decoding of facial expressions of pain

Abstract Objectives Gender has been suggested to play a critical role in how facial expressions of pain are perceived by others. With the present study we aim to further investigate how gender might impact the decoding of facial expressions of pain, (i) by varying both the gender of the observer as well as the gender of the expressor and (ii) by considering two different aspects of the decoding process, namely intensity decoding and pain recognition. Methods In two online-studies, videos of facial expressions of pain as well as of anger and disgust displayed by male and female avatars were presented to male and female participants. In the first study, valence and arousal ratings were assessed (intensity decoding) and in the second study, participants provided intensity ratings for different affective states, that allowed for assessing intensity decoding as well as pain recognition. Results The gender of the avatar significantly affected the intensity decoding of facial expressions of pain, with higher ratings (arousal, valence, pain intensity) for female compared to male avatars. In contrast, the gender of the observer had no significant impact on intensity decoding. With regard to pain recognition (differentiating pain from anger and disgust), neither the gender of the avatar, nor the gender of the observer had any affect. Conclusions Only the gender of the expressor seems to have a substantial impact on the decoding of facial expressions of pain, whereas the gender of the observer seems of less relevance. Reasons for the tendency to see more pain in female faces might be due to psychosocial factors (e.g., gender stereotypes) and require further research.
Deep learning based non-intrusive load monitoring with low resolution data from smart meters

Abstract A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy supplier, improving energy awareness and allowing the implementation of consumption management policies through demand response techniques. Monitoring the consumption of individual appliances is certainly expensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developed that allow the consumption of appliances to be derived from the sole measurement of the aggregate consumption of a house. However, these methodologies often require additional hardware to be installed in the domestic system to measure total energy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the low frequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need of additional specific hardware. The performances obtained on two reference datasets are promising and demonstrate the applicability of the proposed approach.
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences

Dialogue systems that aim to acquire user models through interactions with users need to have interviewing functionality. In this study, we propose a method to generate interview dialogues to build a dialogue system that acquires user preferences for food. First, we collected 118 text-based dialogues between the interviewer and customer and annotated the communicative function and semantic content of the utterances. Next, using the corpus as training data, we created a classification model for the communicative function of the interviewer’s next utterance and a generative model that predicts the semantic content of the utterance based on the dialogue history. By representing semantic content as a sequence of tokens, we evaluated the semantic content prediction model using BLEU. The results demonstrated that the semantic content produced by the proposed method was closer to the ground truth than the semantic content transformed from the output text generated by the retrieval model and GPT-2. Further, we present some examples of dialogue generation by applying model outputs to template-based sentence generation.
Adaptive Threshold Selective Self-Attention for Chinese NER

Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context. However, the fully connected way of self-attention may scatter the attention distribution and allow some irrelevant character information to be integrated, leading to entity boundaries being misidentified. In this paper, we propose a data-driven Adaptive Threshold Selective Self-Attention (ATSSA) mechanism that aims to dynamically select the most relevant characters to enhance the Transformer architecture for Chinese NER. In ATSSA, the attention score threshold of each query is automatically generated, and characters with attention score higher than the threshold are selected by the query while others are discarded, so as to address irrelevant attention integration. Experiments on four benchmark Chinese NER datasets show that the proposed ATSSA brings 1.68 average F1 score improvements to the baseline model and achieves state-of-the-art performance.
In-the-Wild Video Question Answering

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs. umich.edu/wildqa/.
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues

While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation

Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased. Existing methods seldom consider the cross-modal alignment between textual and visual features and tend to ignore disease tags as an auxiliary for report generation. To bridge the gap between textual and visual information, in this study, we propose a “Jointly learning framework for automated disease Prediction and radiology report Generation (JPG)” to improve the quality of reports through the interaction between the main task (report generation) and two auxiliary tasks (feature alignment and disease prediction). The feature alignment and disease prediction help the model learn text-correlated visual features and record diseases as keywords so that it can output high-quality reports. Besides, the improved reports in turn provide additional harder samples for feature alignment and disease prediction to learn more precise visual and textual representations and improve prediction accuracy. All components are jointly trained in a manner that helps improve them iteratively and progressively. Experimental results demonstrate the effectiveness of JPG on the most commonly used IU X-RAY dataset, showing its superior performance over multiple state-of-the-art image captioning and medical report generation methods with regard to BLEU, METEOR, and ROUGE metrics.
Noisy Label Regularisation for Textual Regression

Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such as weight decay and dropout, are ineffective in the face of noisy labels. We propose a simple noisy label detection method that prevents error propagation from the input layer. The approach is based on the observation that the projection of noisy labels is learned through memorisation at advanced stages of learning, and that the Pearson correlation is sensitive to outliers. Extensive experiments over real-world human-disagreement annotations as well as randomly-corrupted and data-augmented labels, across various tasks and domains, demonstrate that our method is effective, regularising noisy labels and improving generalisation performance.
Changes in Tweet Geolocation over Time: A Study with Carmen 2.0

Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.
Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity

Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important perspectives and in many cases cannot do justice to the subjective character of the tasks. One solution to this shortcoming are multi-perspective approaches, which have received very little attention in the field of argument mining so far. In this work we present PerspectifyMe, a method to incorporate perspectivism by enriching a task with subjectivity information from the data annotation process. We exemplify our approach with the use case of classifying argument concreteness, and provide first promising results for the recently published CIMT PartEval Argument Concreteness Corpus.
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings

Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems.
A Two Stage Adaptation Framework for Frame Detection via Prompt Learning

Framing is a communication strategy to bias discussion by selecting and emphasizing. Frame detection aims to automatically analyze framing strategy. Previous works on frame detection mainly focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically. To better deal with various context and frame typologies across different issues, we propose a two-stage adaptation framework. In the framing domain adaptation from pre-training stage, we design two tasks based on pivots and prompts to learn a transferable encoder, verbalizer, and prompts. In the downstream scenario generalization stage, the transferable components are applied to new issues and label sets. Experiment results demonstrate the effectiveness of our framework in different scenarios. Also, it shows superiority both in full-resource and low-resource conditions.