Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Inverse Reinforcement Learning

The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations with more robustness to error compounding than behavioral cloning approaches. On the other hand, it requires that the learner repeatedly solve a computationally expensive reinforcement learning (RL) problem. Often, much of this computation is wasted searching over policies very dissimilar to the expert's. In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration. Intuitively, the expert data focuses the learner on good states during training, which reduces the amount of exploration required to compute a strong policy. Notably, such an approach doesn't need the ability to reset the learner to arbitrary states in the environment, a requirement of prior work in efficient inverse RL. More formally, we derive a reduction from inverse RL to expert-competitive RL (rather than globally optimal RL) that allows us to dramatically reduce interaction during the inner policy search loop while maintaining the benefits of the IRL approach. This allows us to derive both model-free and model-based hybrid inverse RL algorithms with strong policy performance guarantees. Empirically, we find that our approaches are significantly more sample efficient than standard inverse RL and several other baselines on a suite of continuous control tasks.
Tunneling of quantum geometries in spinfoams

Quantum gravitational tunneling effects are expected to give rise to a number of interesting observable phenomena, including, in particular, the evolution of black holes at the end of their existence or the emergence of the early universe from a quantum phase. Covariant Loop Quantum Gravity provides a framework to study these phenomena, yet a precise identification of tunneling processes is still not known. Motivated by this question, we consider a related, simpler case, that of Ponzano-Regge amplitudes: we find a surprising and detailed analogy of a class of simple transition amplitudes with tunneling processes in non-relativistic quantum mechanics.
Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process

Nowadays, sensor suits have been equipped with redundant LiDARs and IMUs to mitigate the risks associated with sensor failure. It is challenging for the previous discrete-time and IMU-driven kinematic systems to incorporate multiple asynchronized sensors, which are susceptible to abnormal IMU data. To address these limitations, we introduce a multi-LiDAR multi-IMU state estimator by taking advantage of Gaussian Process (GP) that predicts a non-parametric continuous-time trajectory to capture sensors' spatial-temporal movement with limited control states. Since the kinematic model driven by three types of linear time-invariant stochastic differential equations are independent of external sensor measurements, our proposed approach is capable of handling different sensor configurations and resilient to sensor failures. Moreover, we replace the conventional $\mathrm{SE}(3)$ state representation with the combination of $\mathrm{SO}(3)$ and vector space, which enables GP-based LiDAR-inertial system to fulfill the real-time requirement. Extensive experiments on the public datasets demonstrate the versatility and resilience of our proposed multi-LiDAR multi-IMU state estimator. To contribute to the community, we will make our source code publicly available.
GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency

Large language models (LLMs) are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. However, LLM-powered writing systems can frustrate users due to their limited personalization and control, which can be exacerbated when users lack experience with prompt engineering. We see design as one way to address these challenges and introduce GhostWriter, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization. GhostWriter leverages LLMs to learn the user's intended writing style implicitly as they write, while allowing explicit teaching moments through manual style edits and annotations. We study 18 participants who use GhostWriter on two different writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style. From this study, we present insights regarding people's relationship with AI-assisted writing and offer design recommendations for future work.
Unified Opinion Dynamic Modeling as Concurrent Set Relations in Rewriting Logic

Social media platforms have played a key role in weaponizing the polarization of social, political, and democratic processes. This is, mainly, because they are a medium for opinion formation. Opinion dynamic models are a tool for understanding the role of specific social factors on the acceptance/rejection of opinions because they can be used to analyze certain assumptions on human behaviors. This work presents a framework that uses concurrent set relations as the formal basis to specify, simulate, and analyze social interaction systems with dynamic opinion models. Standard models for social learning are obtained as particular instances of the proposed framework. It has been implemented in the Maude system as a fully executable rewrite theory that can be used to better understand how opinions of a system of agents can be shaped. This paper also reports an initial exploration in Maude on the use of reachability analysis, probabilistic simulation, and statistical model checking of important properties related to opinion dynamic models.
Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks

Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks. However, this analytical task faces substantial challenges arising from many pairwise comparisons, the dynamic correlations across time spans, and the ambiguity in deriving implications from business relational knowledge. We propose Prismatic, a visual analytics system that integrates quantitative analysis of historical performance and qualitative analysis of business relational knowledge to cluster correlated businesses interactively. Prismatic features three clustering processes: dynamic cluster generation, knowledge-based cluster exploration, and correlation-based cluster validation. Utilizing a multi-view clustering approach, it enriches data-driven clusters with knowledge-driven similarity, providing a nuanced understanding of business correlations. Through well-coordinated visual views, Prismatic facilitates a comprehensive interpretation of intertwined quantitative and qualitative features, demonstrating its usefulness and effectiveness via case studies on formulating concept stocks and extensive interviews with domain experts.
ADS: Approximate Densest Subgraph for Novel Image Discovery

The volume of image repositories continues to grow. Despite the availability of content-based addressing, we still lack a lightweight tool that allows us to discover images of distinct characteristics from a large collection. In this paper, we propose a fast and training-free algorithm for novel image discovery. The key of our algorithm is formulating a collection of images as a perceptual distance-weighted graph, within which our task is to locate the K-densest subgraph that corresponds to a subset of the most unique images. While solving this problem is not just NP-hard but also requires a full computation of the potentially huge distance matrix, we propose to relax it into a K-sparse eigenvector problem that we can efficiently solve using stochastic gradient descent (SGD) without explicitly computing the distance matrix. We compare our algorithm against state-of-the-arts on both synthetic and real datasets, showing that it is considerably faster to run with a smaller memory footprint while able to mine novel images more accurately.
A merger-driven scenario for clumpy galaxy formation in the epoch of reionization: Physical properties of clumps in the FirstLight simulation

Recent JWST observations with superb angular resolution have revealed the existence of clumpy galaxies at high redshift through the detection of rest-frame optical emission lines. We use the FirstLight simulation to study the properties of (sub-)galactic clumps that are bright in [OIII] 5007$\mathrm{\mathring{A}}$ line with flux greater than $\sim 10^{-18} \, {\rm erg\, s^{-1}\, cm^{-2}}$, to be detected by JWST. For 62 simulated galaxies that have stellar masses of $(0.5-6) \times 10^{10} \, M_\odot$ at $z=5$, we find clumps in 1828 snapshots in the redshift range $z = 9.5-5.5$. The clumps are identified by the surface density of star formation rate. About one-tenth of the snapshots show the existence of clumpy systems with two or more components. Most of the clumps are formed by mergers and can be characterized by their ages; central clumps dominated by stellar populations older than 50 Myr, and off-centered clumps dominated by younger stellar populations with specific star formation rates of $\sim 50 \, {\rm Gyr^{-1}}$. The latter type of young clumps is formed from gas debris in the tidal tails of major mergers with baryonic mass ratios of $1 \leq q<4$. The merger-induced clumps are short-lived, and merge within a dynamical time of several tens million years. The number density of the clumpy systems is estimated to be $\sim 10^{-5}\, {\rm cMpc^{-3}}$, which is large enough to be detected in recent JWST surveys.
Forecasting for Swap Regret for All Downstream Agents

We study the problem of making predictions so that downstream agents who best respond to them will be guaranteed diminishing swap regret, no matter what their utility functions are. It has been known since Foster and Vohra (1997) that agents who best-respond to calibrated forecasts have no swap regret. Unfortunately, the best known algorithms for guaranteeing calibrated forecasts in sequential adversarial environments do so at rates that degrade exponentially with the dimension of the prediction space. In this work, we show that by making predictions that are not calibrated, but are unbiased subject to a carefully selected collection of events, we can guarantee arbitrary downstream agents diminishing swap regret at rates that substantially improve over the rates that result from calibrated forecasts -- while maintaining the appealing property that our forecasts give guarantees for any downstream agent, without our forecasting algorithm needing to know their utility function. We give separate results in the ``low'' (1 or 2) dimensional setting and the ``high'' ($>2$) dimensional setting. In the low dimensional setting, we show how to make predictions such that all agents who best respond to our predictions have diminishing swap regret -- in 1 dimension, at the optimal $O(\sqrt{T})$ rate. In the high dimensional setting we show how to make forecasts that guarantee regret scaling at a rate of $O(T^{2/3})$ (crucially, a dimension independent exponent), under the assumption that downstream agents smoothly best respond. Our results stand in contrast to rates that derive from agents who best respond to calibrated forecasts, which have an exponential dependence on the dimension of the prediction space.
Dynamic load modulation predicts right heart tolerance of left ventricular cardiovascular assist in a porcine model of cardiogenic shock

Ventricular assist devices (VADs) offer mechanical support for patients with cardiogenic shock by unloading the impaired ventricle and increasing cardiac outflow and subsequent tissue perfusion. Their ability to adjust ventricular assistance allows for rapid and safe dynamic changes in cardiac load, which can be used with direct measures of chamber pressures to quantify cardiac pathophysiologic state, predict response to interventions, and unmask vulnerabilities such as limitations of left-sided support efficacy due to intolerance of the right heart. We defined hemodynamic metrics in five pigs with dynamic peripheral transvalvular VAD (pVAD) support to the left ventricle. Metrics were obtained across a spectrum of disease states, including left ventricular ischemia induced by titrated microembolization of a coronary artery and right ventricular strain induced by titrated microembolization of the pulmonary arteries. A sweep of different pVAD speeds confirmed mechanisms of right heart decompensation after left-sided support and revealed intolerance. In contrast to the systemic circulation, pulmonary vascular compliance dominated in the right heart and defined the ability of the right heart to adapt to left-sided pVAD unloading. We developed a clinically accessible metric to measure pulmonary vascular compliance at different pVAD speeds that could predict right heart efficiency and tolerance to left-sided pVAD support. Findings in swine were validated with retrospective hemodynamic data from eight patients on pVAD support. This methodology and metric could be used to track right heart tolerance, predict decompensation before right heart failure, and guide titration of device speed and the need for biventricular support. A percutaneous ventricular assist device in pigs enabled evaluation of ventricular coupling in response to increasing right heart strain. Editor’s summary When the heart fails to pump enough blood to the body, left ventricular assist devices are given to patients, but these devices sometimes cause right ventricle decompensation resulting in worsening heart failure. Lamberti et al. investigate this decompensation in a porcine model of cardiogenic shock, where the animals received a percutaneous ventricular assist device. The authors applied different stress stimuli to the two ventricles and varied the device speed to measure characteristics that could influence right ventricle response to left ventricle support. They discovered that pulmonary vascular compliance was a key factor. They confirmed this finding in a retrospective dataset of eight patients with cardiogenic shock receiving a left ventricular assist device. This study provides a way to predict how patients will respond to left heart support, thus helping to improve clinical decision making. —Brandon Berry
Group composition and dynamics in American Crows: insights into an unusual cooperative breeder

Breeding pairs of American Crows (Corvus brachyrhynchos) in Stillwater, OK, lived with 0-10 auxil-iaries in territories distributed throughout public, campus, commercial, and residential areas. Unpaired crows moved easily among groups throughout the year, but commonly did so during the two months or so preceding the onset of nesting across the population, and the week or so preceding hatching within groups. In 2001 and 2002, pre-hatch group size ranged from 2-10 (mean = 4.5 in both years), and auxiliaries included a male sibling, social and genetic offspring, step-offspring, half-siblings, and unrelated immigrants of both sexes, ranging in age from 1 to at least five years old. Twenty nine percent of pre-hatch auxiliaries dispersed out of groups at hatch-ing (for half, only temporarily), including all females unrelated to female breeders. Post-hatch group size ranged from 2-6, with means of 3.7 in both years, and the post-hatch auxiliary popula-tion differed in composition from the pre-hatch population: whereas post-hatch male auxiliaries included a sibling, half siblings, and unrelated immigrants in addition to social and genetic sons, all post-hatch female auxiliaries were the social and genetic daughters of female breeders, and all but one (the same individual in both years) were also the social and genetic daughters of male breeders. Crows in Stillwater delayed breeding for one or more years beyond sexual maturity, despite the availability of space and members of the opposite sex. Individual dispersal decisions by unpaired crows, and the behavior of paired territory owners, did not follow patterns described for other cooperative breeders. We found little support for extant theories regarding the formation, com-position, and maintenance of avian groups, and discuss aspects of the lives of crows that may have contributed to the complex and benign nature of this population’s society. How to Cite: Caffrey, C. and C. C. Peterson. 2015. Group composition and dynamics in American Crows: insights into an unusual cooperative breeder. Friesen Press.
Microbial responses to stress cryptically alter natural selection on plants

Microbial communities can rapidly respond to stress, meaning plants may encounter altered soil microbial communities in stressful environments. These altered microbial communities may then affect natural selection on plants. Because stress can cause lasting changes to microbial communities, microbes may also cause legacy effects on plant selection that persist even after the stress ceases. To explore how microbial responses to stress and persistent microbial legacy effects of stress affect natural selection, we grew Chamaecrista fasciculata plants in stressful (salt, herbicide, or herbivory) or non-stressful conditions with microbes that had experienced each of these environments in the previous generation. Microbial community responses to stress generally counteracted the effects of stress itself on plant selection, thereby weakening the strength of stress as a selective agent. Microbial legacy effects of stress altered plant selection in non-stressful environments, suggesting that stress-induced changes to microbes may continue to affect selection after stress is lifted. These results suggest that soil microbes may play a cryptic role in plant adaptation to stress, potentially reducing the strength of stress as a selective agent and altering the evolutionary trajectory of plant populations.
Serotonin deficiency from constitutive SKN-1 activation drives pathogen apathy

When an organism encounters a pathogen, the host innate immune system activates to defend against pathogen colonization and toxic xenobiotics produced. C. elegans employ multiple defense systems to ensure survival when exposed to Pseudomonas aeruginosa including activation of the cytoprotective transcription factor SKN-1/NRF2. Although wildtype C. elegans quickly learn to avoid pathogens, here we describe a peculiar apathy-like behavior towards PA14 in animals with constitutive activation of SKN-1, whereby animals choose not to leave and continue to feed on the pathogen even when a non-pathogenic and healthspan-promoting food option is available. Although lacking the urgency to escape the infectious environment, animals with constitutive SKN-1 activity are not oblivious to the presence of the pathogen and display the typical pathogen-induced intestinal distension and eventual demise. SKN-1 activation, specifically in neurons and intestinal tissues, orchestrates a unique transcriptional program which leads to defects in serotonin signaling that is required from both neurons and non-neuronal tissues. Serotonin depletion from SKN-1 activation limits pathogen defense capacity, drives the pathogen-associated apathy behaviors and induces a synthetic sensitivity to selective serotonin reuptake inhibitors. Taken together, our work reveals new insights into how animals perceive environmental pathogens and subsequently alter behavior and cellular programs to promote survival. KEY POINTS Identify an apathy-like behavioral response for pathogens resulting from the constitutive activation of the cytoprotective transcription factor SKN-1. Uncover the obligate role for serotonin synthesis in both neuronal and non-neuronal cells for the apathy-like state and ability of serotonin treatment to restore normal behaviors. Characterize the timing and tissue specificity of SKN-1 nuclear localization in neurons and intestinal cells in response to pathogen exposure. Define the unique and context-specific transcriptional signatures of animals with constitutive SKN-1 activation when exposed to pathogenic environments. Reveal necessity for both neuronal and non-neuronal serotonin signaling in host survival from pathogen infection.
The interplay between relative sea-level rise and sediment supply at the distal part of the Nile littoral cell

During Holocene sea-level rise, coastal areas became transitional environments as marine incursion covered the land. Changing conditions resulted in dynamic depositional environments that recorded the migration and stabilization of modern shorelines. These processes are viewed in the Zevulun Plain (Haifa Bay, Israel) record located in the northern edge of the Nile littoral cell. Sedimentological and palaeontological analyses combined with dating enabled the reconstruction of the Holocene chrono-stratigraphical frame. The results reveal an unconformity representing a long period of exposure and erosion during the last glacial. The interplay between relative sea-level rise and sediment supply was first set out by the deposition of alluvial sediments, evidence of the hydrological system reactivation and base level landward migration. Sea flooding of the Zevulun Plain started about 7.8 cal ka BP and the coastline was pushed eastward. Nile-driven sands transported by longshore currents formed dunes that blocked the rivers estuaries and led to wetlands formation. Peat accumulation is evident first in the north of the plain at 7.6–6.2 cal ka BP and later in the south at 6.5–5.5 cal ka BP. Both wetlands showed a change from fresh to brackish water environments at the end of their existence. Following the maximum sea-level rise and inland sea invasion at about 4 ka BP, alluvial sediments covered the plain and the coastline moved westward to its current position. This record serves as a model for the development of Mediterranean clastic coasts controlled by sea rise and infill processes.