Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-Centric Templatic Views of Documents

Authors seeking to communicate with broader audiences often compose their ideas about the same underlying knowledge in different documents and formats -- for example, as slide decks, newsletters, reports, brochures, etc. Prior work in document generation has generally considered the creation of each separate format to be different a task, developing independent methods for generation and evaluation. This approach is suboptimal for the advancement of AI-supported content authoring from both research and application perspectives because it leads to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. Thus, in our work, we consider each of these documents to be templatic views of the same underlying knowledge, and we aim to unify the generation and evaluation of these templatic views of documents. We begin by introducing an LLM-powered method to extract the most important information from an input document and represent this information in a structured format. We show that this unified representation can be used to generate multiple templatic views with no supervision and with very little guidance, improving over strong baselines. We additionally introduce a unified evaluation method that is template agnostic, and can be adapted to building document generators for heterogeneous downstream applications. Finally, we conduct a human evaluation, which shows that humans prefer 82% of the downstream documents generated with our method. Furthermore, the newly proposed evaluation metric correlates more highly with human judgement than prior metrics, while providing a unified evaluation method.
Construction and Evaluation of Mandarin Multimodal Emotional Speech Database

A multi-modal emotional speech Mandarin database including articulatory kinematics, acoustics, glottal and facial micro-expressions is designed and established, which is described in detail from the aspects of corpus design, subject selection, recording details and data processing. Where signals are labeled with discrete emotion labels (neutral, happy, pleasant, indifferent, angry, sad, grief) and dimensional emotion labels (pleasure, arousal, dominance). In this paper, the validity of dimension annotation is verified by statistical analysis of dimension annotation data. The SCL-90 scale data of annotators are verified and combined with PAD annotation data for analysis, so as to explore the internal relationship between the outlier phenomenon in annotation and the psychological state of annotators. In order to verify the speech quality and emotion discrimination of the database, this paper uses 3 basic models of SVM, CNN and DNN to calculate the recognition rate of these seven emotions. The results show that the average recognition rate of seven emotions is about 82% when using acoustic data alone. When using glottal data alone, the average recognition rate is about 72%. Using kinematics data alone, the average recognition rate also reaches 55.7%. Therefore, the database is of high quality and can be used as an important source for speech analysis research, especially for the task of multimodal emotional speech analysis.
2.5-D MHD Simulation of the Formation and Evolution of Plasmoids in Coronal Current Sheets

In the present paper, using MPI-AMRVAC, we perform a 2.5-D numerical MHD simulation of the dynamics and associated thermodynamical evolution of an initially force-free Harris current sheet subjected to an external velocity perturbation under the condition of uniform resistivity. The amplitude of the magnetic field is taken to be 10 Gauss, typical of the solar corona. We impose a Gaussian velocity pulse across this current sheet mimicking the interaction of fast magnetoacoustic waves with a current sheet in corona. This leads to a variety of dynamics and plasma processes in the current sheet, which is initially quasi-static. The initial pulse interacts with the current sheet and splits into a pair of counter-propagating wavefronts, which forms a rarefied region and leads to inflow and a thinning of the current sheet. The thinning results in Petschek-type magnetic reconnection followed by tearing instability and plasmoid formation. The reconnection outflows containing outward-moving plasmoids have accelerated motions with velocities ranging from 105-303 km/s. The average temperature and density of the plasmoids are found to be 8 MK and twice the background density of the solar corona, respectively. These estimates of velocity, temperature and density of plasmoids are similar to values reported from various solar coronal observations. Therefore, we infer that the external triggering of a quasi-static current sheet by a single velocity pulse is capable of initiating magnetic reconnection and plasmoid formation in the absence of a localized enhancement of resistivity in the solar corona.
Multiple timestep reversible $N$-body integrators

We present new powerful time-reversible integrators for solution of planetary systems consisting of"planets"and a dominant mass ("star"). The algorithms can be considered adaptive generalizations of the Wisdom--Holman method, in which all pairs of planets can be assigned timesteps. These timesteps, along with the global timestep, can be adapted time-reversibly, often at no appreciable additional compute cost, without sacrificing any of the long-term error benefits of the Wisdom--Holman method. The method can also be considered a simpler and more flexible version of the \texttt{SYMBA} symplectic code. We perform tests on several challenging problems with close encounters and find the reversible algorithms are up to $2.6$ times faster than a code based on \texttt{SYMBA}. The codes presented here are available on Github. We also find adapting a global timestep reversibly and discretely must be done in block-synchronized manner.
SeMaScore : a new evaluation metric for automatic speech recognition tasks

In this study, we present SeMaScore, generated using a segment-wise mapping and scoring algorithm that serves as an evaluation metric for automatic speech recognition tasks. SeMaScore leverages both the error rate and a more robust similarity score. We show that our algorithm's score generation improves upon the state-of-the-art BERTscore. Our experimental results show that SeMaScore corresponds well with expert human assessments, signal-to-noise ratio levels, and other natural language metrics. We outperform BERTscore by 41x in metric computation speed. Overall, we demonstrate that SeMaScore serves as a more dependable evaluation metric, particularly in real-world situations involving atypical speech patterns.
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation

Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only a single reward for an entire output. This sparsity of rewards can lead to inefficient and unstable learning. To address this challenge, our paper introduces an novel framework that utilizes the critique capability of Large Language Models (LLMs) to produce intermediate-step rewards during RL training. Our method involves coupling a policy model with a critic language model, which is responsible for providing comprehensive feedback of each part of the output. This feedback is then translated into token or span-level rewards that can be used to guide the RL training process. We investigate this approach under two different settings: one where the policy model is smaller and is paired with a more powerful critic model, and another where a single language model fulfills both roles. We assess our approach on three text generation tasks: sentiment control, language model detoxification, and summarization. Experimental results show that incorporating artificial intrinsic rewards significantly improve both sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation.
AugSumm: towards generalizable speech summarization using synthetic labels from large language model

Abstractive speech summarization (SSUM) aims to generate human-like summaries from speech. Given variations in information captured and phrasing, recordings can be summarized in multiple ways. Therefore, it is more reasonable to consider a probabilistic distribution of all potential summaries rather than a single summary. However, conventional SSUM models are mostly trained and evaluated with a single ground-truth (GT) human-annotated deterministic summary for every recording. Generating multiple human references would be ideal to better represent the distribution statistically, but is impractical because annotation is expensive. We tackle this challenge by proposing AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries for training and evaluation. First, we explore prompting strategies to generate synthetic summaries from ChatGPT. We validate the quality of synthetic summaries using multiple metrics including human evaluation, where we find that summaries generated using AugSumm are perceived as more valid to humans. Second, we develop methods to utilize synthetic summaries in training and evaluation. Experiments on How2 demonstrate that pre-training on synthetic summaries and fine-tuning on GT summaries improves ROUGE-L by 1 point on both GT and AugSumm-based test sets. AugSumm summaries are available at https://github.com/Jungjee/AugSumm.
Generalized Planning for the Abstraction and Reasoning Corpus

The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction. In this work, we introduce an ARC solver, Generalized Planning for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized planning (GP) problem, where a solution is formalized as a planning program with pointers. We express each ARC problem using the standard Planning Domain Definition Language (PDDL) coupled with external functions representing object-centric abstractions. We show how to scale up GP solvers via domain knowledge specific to ARC in the form of restrictions over the actions model, predicates, arguments and valid structure of planning programs. Our experiments demonstrate that GPAR outperforms the state-of-the-art solvers on the object-centric tasks of the ARC, showing the effectiveness of GP and the expressiveness of PDDL to model ARC problems. The challenges provided by the ARC benchmark motivate research to advance existing GP solvers and understand new relations with other planning computational models. Code is available at github.com/you68681/GPAR.
Ion Channels in Critical Membranes: Clustering, Cooperativity, and Memory Effects

Much progress has been made in elucidating the inner workings of voltage-gated ion channels, but less understood is the influence of lipid rafts on gating kinetics. Here we propose that state-dependent channel affinity for different lipid species provides a unified explanation for the experimentally observed behaviors of clustering, cooperativity, and hysteresis. We develop models of diffusing lipids and channels engaged in Ising-like interactions to investigate the collective behaviors driven by raft formation in critical membranes close to the demixing transition. The model channels demonstrate lipid-mediated long-range interactions, activation curve steepening, and long-term memory in ionic currents. These behaviors likely play a role in channel-mediated cellular signaling and suggest a universal mechanism for self-organization of biomolecular assemblies.
Females smell differently: characteristics and significance of the most common olfactory sensilla of female silkmoths

In the silkmoth Bombyx mori, the role of male sensilla trichodea in pheromone detection is well established. Here we study the corresponding female sensilla, which contain two olfactory sensory neurons (OSNs) and come in two lengths, each representing a single physiological type. Only OSNs in medium trichoids respond to the scent of mulberry, the silkworm's exclusive host plant, and are more sensitive in mated females, suggesting a role in oviposition. In long trichoids, one OSN is tuned to (+)-linalool and the other to benzaldehyde and isovaleric acid, both odours emitted by silkworm faeces. While the significance of (+)-linalool detection remains unclear, isovaleric acid repels mated females and may therefore play a role in avoiding crowded oviposition sites. When we examined the underlying molecular components of neurons in female trichoids, we found non-canonical co-expression of Ir8a, the co-receptor for acid responses, and ORco, the co-receptor of odorant receptors, in long trichoids, and the unexpected expression of a specific odorant receptor in both trichoid sensillum types. In addition to elucidating the function of female trichoids, our results suggest that some accepted organizational principles of the insect olfactory system may not apply to the predominant sensilla on the antenna of female B. mori.
Why do dogs wag their tails?

Tail wagging is a conspicuous behaviour in domestic dogs (Canis familiaris). Despite how much meaning humans attribute to this display, its quantitative description and evolutionary history are rarely studied. We summarize what is known about the mechanism, ontogeny, function and evolution of this behaviour. We suggest two hypotheses to explain its increased occurrence and frequency in dogs compared to other canids. During the domestication process, enhanced rhythmic tail wagging behaviour could have (i) arisen as a by-product of selection for other traits, such as docility and tameness, or (ii) been directly selected by humans, due to our proclivity for rhythmic stimuli. We invite testing of these hypotheses through neurobiological and ethological experiments, which will shed light on one of the most readily observed yet understudied animal behaviours. Targeted tail wagging research can be a window into both canine ethology and the evolutionary history of characteristic human traits, such as our ability to perceive and produce rhythmic behaviours.
Misalignment of plastic and evolutionary responses of lifespan to novel carbohydrate diets

Diet elicits varied effects on longevity across a wide range of animal species where dietary discordance between an organisms' evolutionary and developmental dietary history is increasingly recognized to play a critical role in shaping lifespan. However, whether such changes, predominantly assessed in a single generation, lead to evolutionary shifts in lifespan remains unclear. In this study, we used an experimental evolution approach to test whether changes in an organisms' evolutionary and developmental dietary history, specifically carbohydrate content, causes lifespan evolution in Drosophila serrata. After 30 generations, we investigated the evolutionary potential of lifespan in response to four novel diets that varied systematically in their ratio of carbohydrate–protein content. We also examined developmental plasticity effects using a set of control populations that were raised on the four novel environments allowing us to assess the extent to which plastic responses of lifespan mirrored adaptive responses observed following experimental evolution. Both high- and low-carbohydrate diets elicited plastic effects on lifespan; however, the plastic responses for lifespan to developmental diets bore little resemblance to the evolved responses on evolutionary diets. Understanding the dietary conditions regulating the match/mismatch of plastic and evolved responses will be important in determining whether a particular match/mismatch combination is adaptive for lifespan. While the differences in evolutionary diet by developmental diet interactions are only beginning to be elucidated, this study lays the foundation for future investigations of carbohydrate contributions to evolved and plastic effects on health and lifespan.
Kinematics and dynamics of non-developable origami

Non-developable origami is a unique type of origami structures that cannot be unfolded into a flat sheet without stretching. In this work, we study the kinematics and dynamics of such origami, theoretically, numerically and experimentally, considering rigid panels and stretchable creases. Unlike developable origami, we find that non-developable origami exhibits distinct folding angle relationships, leading to several separate branches in its kinematic configuration space with snap-through and locking behaviours. By modelling the creases as stretchable torsional springs, we derive a dynamic model to analyse the deployment of non-developable origami structures, from single-vertex origami to origami chains. Our dynamic model unveils the snap-through behaviour between the two kinematics branches of the single-vertex non-developable origami structure, which is further validated by experiments with excellent agreement. We believe that our kinematic and dynamic framework of non-developable origami will greatly expand the current design space of origami structures and guide the design of novel origami actuators.
Ecological structure of diversity-dependent diversification in Phanerozoic marine bivalves

Rigorous analysis of diversity-dependence—the hypothesis that the rate of proliferation of new species is inversely related to standing diversity—requires consideration of the ecology of the organisms in question. Differences between infaunal marine bivalves (living entirely within the sediment) and epifaunal forms (living partially or completely above the sediment–water interface) predict that these major ecological groups should have different diversity dynamics: epifaunal species may compete more intensely for space and be more susceptible to predation and physical disturbance. By comparing detrended standing diversity with rates of diversification, origination, and extinction in this exceptional fossil record, we find that epifaunal bivalves experienced significant, negative diversity-dependence in origination and net diversification, whereas infaunal forms show little appreciable relationship between diversity and evolutionary rates. This macroevolutionary contrast is robust to the time span over which dynamics are analysed, whether mass-extinction rebounds are included in the analysis, the treatment of stratigraphic ranges that are not maximally resolved, and the details of detrending. We also find that diversity-dependence persists over hundreds of millions of years, even though diversity itself rises nearly exponentially, belying the notion that diversity-dependence must imply equilibrial diversity dynamics.
On expanders from the action of GL(2,Z)

Consider the undirected graph $G_n=(V_n, E_n)$ where $V_n = (Z/nZ)^2$ and $E_n$ contains an edge from $(x,y)$ to $(x+1,y)$, $(x,y+1)$, $(x+y,y)$, and $(x,y+x)$ for every $(x,y) \in V_n$. Gabber and Galil, following Margulis, gave an elementary proof that ${G_n}$ forms an expander family. In this note, we present a somewhat simpler proof of this fact, and demonstrate its utility by isolating a key property of the linear transformations $(x,y) ->(x+y,x), (x,y+x)$ that yields expansion. As an example, consider any invertible, integral matrix $S \in GL_2(Z)$ and let $G^S_n = (V_n, E^S_n)$ where $E^S_n$ contains, for every $(x,y) \in V_n$, an edge from $(x,y)$ to $(x+1,y)$, $(x,y+1)$, $S(x,y)$, and $S^T(x,y)$, where $S^T$ denotes the transpose of $S$. Then {G_n^S} forms an expander family if and only if a related infinite graph has positive Cheeger constant. This latter property turns out to be elementary to analyze and can be used to show that {G_n^S} are expanders precisely when the trace of S is non-zero and S is not equal to its transpose. We also present some other generalizations.
Rural Hospitals and Rural Health Challenges: An Analysis of Recent National Health Rankings and Hospital Data

Rural hospitals play a critical role in providing acute care and other essential medical services to rural populations. However, they are increasingly unable to meet the needs for health services in rural communities. This paper utilizes the data from the recent County Health Rankings and Definitive Healthcare to identify the health care challenges in rural counties and evaluate the operating and financial condition of rural hospitals in the context of the health care needs in rural areas and in comparison with urban hospitals. Our data analysis finds that while there are major health challenges and increased needs for hospital service in rural areas, the operating and financial performance of rural hospitals has been worsening in key aspects. We argue that continued policy support as well as health system innovations will be critically important to address the health care needs of rural America.
Reciprocity in Leader and Follower Behavior among Federal Employees: Test of a Nonrecursive Model

The purpose of this article was to advance followership theory and research by studying reciprocal relationships between leaders and followers. Using FEVS data, a nonrecursive path analysis demonstrated strong evidence of reciprocal, positive relationships between leader behavior fostering employee development and follower initiative taking. This virtuous cycle provided preliminary evidence that a “reversed lens” followership model is applicable to public sector organizations. Leader and follower behavior were also positively related to follower effectiveness, providing additional evidence of the efficacy of the followership model tested. Implications for PA research and practice were discussed.
Medial entorhinal cortex plays a specialized role in learning of flexible, context-dependent interval timing behavior

Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC ’time cells’, which fire at specific moments during interval timing tasks, collectively tiling the entire timing period. It has been hypothesized that MEC time cells could provide temporal information necessary for episodic memories, yet it remains unknown whether MEC time cells display learning dynamics required for encoding different temporal contexts. To explore this, we developed a novel behavioral paradigm that requires distinguishing temporal contexts. Combined with methods for cellular resolution calcium imaging, we find that MEC time cells display context-dependent neural activity that emerges with task learning. Through chemogenetic inactivation we find that MEC activity is necessary for learning of context-dependent interval timing behavior. Finally, we find evidence of a common circuit mechanism that could drive sequential activity of both time cells and spatially selective neurons in MEC. Our work suggests that the clock-like firing of MEC time cells can be modulated by learning, allowing the tracking of various temporal structures that emerge through experience.