Miracle-dz/UniFAR-FTUs huggingface dataset:
Recency of Faulting and Neotechtonic Framework in the Dixie Valley Geothermal Field and Other Geothermal Fields of the Basin and Range

We studied the role that earthquake faults play in redistributing stresses within in the earths crust near geothermal fields. The geographic foci of our study were the sites of geothermal plants in Dixie Valley, Beowawe, and Bradys Hot Springs, Nevada. Our initial results show that the past history of earthquakes has redistributed stresses at these 3 sites in a manner to open and maintain fluid pathways critical for geothermal development. The approach developed here during our pilot study provides an inexpensive approach to (1) better define the best locations to site geothermal wells within known geothermal fields and (2) to define the location of yet discovered geothermal fields which are not manifest at the surface by active geothermal springs. More specifically, our investigation shows that induced stress concentrations at the endpoints of normal fault ruptures appear to promote favorable conditions for hydrothermal activity in two ways. We conclude that an understanding of the spatial distribution of active faults and the past history of earthquakes on those faults be incorporated as a standard tool in geothermal exploration and in the siting of future boreholes in existing geothermal fields.
Neuroproteomic mapping of kinases and their substrates downstream of acetylcholine: finding and implications

ABSTRACT Introduction Since the emergence of the cholinergic hypothesis of Alzheimer’s disease (AD), acetylcholine has been viewed as a mediator of learning and memory. Donepezil improves AD-associated learning deficits and memory loss by recovering brain acetylcholine levels. However, it is associated with side effects due to global activation of acetylcholine receptors. Muscarinic acetylcholine receptor M1 (M1R), a key mediator of learning and memory, has been an alternative target. The importance of targeting a specific pathway downstream of M1R has recently been recognized. Elucidating signaling pathways beyond M1R that lead to learning and memory holds important clues for AD therapeutic strategies. Areas covered This review first summarizes the role of acetylcholine in aversive learning, one of the outputs used for preliminary AD drug screening. It then describes the phosphoproteomic approach focused on identifying acetylcholine intracellular signaling pathways leading to aversive learning. Finally, the intracellular mechanism of donepezil and its effect on learning and memory is discussed. Expert opinion The elucidation of signaling pathways beyond M1R by phosphoproteomic approach offers a platform for understanding the intracellular mechanism of AD drugs and for developing AD therapeutic strategies. Clarifying the molecular mechanism that links the identified acetylcholine signaling to AD pathophysiology will advance the development of AD therapeutic strategies.
View-Independent Adjoint Light Tracing for Lighting Design Optimization

Controlling light is a central element when composing a scene, enabling artistic expression, as well as the design of comfortable living spaces. In contrast to previous camera-based inverse rendering approaches, we introduce a novel method for interactive, view-independent differentiable global illumination. Our method first performs a forward light-tracing pass, starting from the light sources and storing the resulting radiance field on the scene geometry, representing specular highlights via hemi-spherical harmonics. We then evaluate an objective function on the entire radiance data and propagate derivatives back to the lighting parameters by formulating a novel, analytical adjoint light-tracing step. Our method builds on GPU ray tracing, which allows us to optimize all lighting parameters at interactive rates, even for complex geometry. Instead of specifying optimization targets as view-specific images, our method allows us to optimize the lighting of an entire scene to match either baked illumination (e.g., lightmaps), regulatory lighting requirements for work spaces, or artistic sketches drawn directly on the geometry. This approach provides a more direct and intuitive user experience for designers. We visualize our adjoint gradients and compare them to image-based state-of-the-art differentiable rendering methods. We also compare the convergence behavior of various optimization algorithms when using our gradient data vs. image-based differentiable rendering methods. Qualitative comparisons with real-world scenes underline the practical applicability of our method.
Racial and Ethnic Disparities in Transit Supply During the COVID-19 Pandemic: Insights from 232 U.S. Transit Agencies (preprint)

COVID-19 introduced tremendous disruptions to the normal operation of transit agencies, led to a massive suspension of regular service, and disturbed the access to essential mobility for many transit-dependent populations. In this study, we reexamine the national-level service disruption of the public transportation system as a result of the COVID-19 pandemic and investigate the disparities in the reduction of transit supply, causing certain racial-ethnic groups to be disproportionately affected. To support our analysis, we collect GTFS data from 232 transit agencies covering over 89 million people across 45 states in the U.S. Our findings suggest more disadvantaged communities and certain racial-ethnic groups have experienced disproportional reductions in transit supply, regardless of their pre-pandemic level of service. Further, we employ causal mediation analyses to address the mediation effect of the pre-pandemic level of transit service on the relationship between race/ethnicity and transit supply reduction. This analysis validates the disproportionate reduction in service quality for specific racial/ethnic groups, a trend evident across the majority of transit agencies. In particular, Black Americans are found to be associated with the greatest absolute service loss and also the largest total effects on transit supply reductions, while the White and Asian groups are less impacted. We further report that the most impacted communities by transit loss mainly consist of minority populations, where the reductions in transit are correlated with pandemic-related health and economic burdens and result in greater changes in mobility intensity. Our study contributes to a comprehensive understanding of transit inequities due to the pandemic and provides valuable insights to better prepare public transportation systems against future nationwide disasters.
Competition of many searchers

First passage times (FPTs) are often used to study timescales in physical, chemical, and biological processes. FPTs generically describe the time it takes a random"searcher"to find a"target. "In many systems, the important timescale is not the time it takes a single searcher to find a target, but rather the time it takes the fastest searcher out of many searchers to find a target. Such fastest FPTs or extreme FPTs result from many searchers competing to find the target and differ markedly from FPTs of single searchers. In this chapter, we review recent results on fastest FPTs. We show how fastest FPTs depend on the mode of stochastic search (including search by diffusion, subdiffusion, superdiffusion, and discrete jumps), the initial searcher distribution, and properties of the spatial domain.
The crustal structure of the Cocos ridge off Costa Rica

[1] The submarine Cocos ridge in the northwestern Panama basin, a bathymetric feature more than 1000-km long and 250–500 km broad, is about 2 km shallower than the adjacent basin. It is generally interpreted as the trace of the Galapagos hot spot. Two 127- and 260-km long seismic wide-angle sections were recorded along and across this ridge, offshore the Osa peninsula, Costa Rica. Crustal thickening is seen everywhere along the sections. On the northwestern outer ridge flank, increased thickness is exclusively attributed to the upper crust and expressed by 2-km thick flow basalts. The Quepos plateau caps the upper crust in this area. Toward the center of the Cocos ridge, the Moho deepens from 11–12 to 21 km depth and crustal thickening is almost entirely attributed to the lower crust which makes up 80% of the crust and is three times the thickness of normal oceanic lower crust. It is homogeneously structured and the velocities which range from 6.5 km/s at the top to 7.35 km/s at the base are comparable to normal lower crust under these depth conditions and suggest no differences to a gabbroic rock composition. Similarities to the crustal velocity structure of Iceland, central Kerguelen plateau, and Broken ridge are consistent with a formation of this 13–15 Ma old Cocos ridge segment by excessive magmatism in a near-plate boundary setting.
Towards An Analytical Framework for Potential Games

Potential game is an emerging notion and framework for studying multi-agent games, especially with heterogeneous agents. Up to date, potential games have been extensively studied mostly from the algorithmic aspect in approximating and computing the Nash equilibrium without verifying if the game is a potential game, due to the lack of analytical structure. In this paper, we aim to build an analytical framework for dynamic potential games. We prove that a game is a potential game if and only if each agent's value function can be decomposed as a potential function and a residual term that solely dependent on other agents' actions. This decomposition enables us to identify and analyze a new and important class of potential games called the distributed game. Moreover, by an appropriate notion of functional derivatives, we prove that a game is a potential game if the value function has a symmetric Jacobian. Consequently, for a general class of continuous-time stochastic games, their potential functions can be further characterized from both the probabilistic and the PDE approaches. The consistency of these two characterisations are shown in a class of linear-quadratic games.
On the near-optimality of betting confidence sets for bounded means

Constructing nonasymptotic confidence intervals (CIs) for the mean of a univariate distribution from independent and identically distributed (i.i.d.) observations is a fundamental task in statistics. For bounded observations, a classical nonparametric approach proceeds by inverting standard concentration bounds, such as Hoeffding's or Bernstein's inequalities. Recently, an alternative betting-based approach for defining CIs and their time-uniform variants called confidence sequences (CSs), has been shown to be empirically superior to the classical methods. In this paper, we provide theoretical justification for this improved empirical performance of betting CIs and CSs. Our main contributions are as follows: (i) We first compare CIs using the values of their first-order asymptotic widths (scaled by $\sqrt{n}$), and show that the betting CI of Waudby-Smith and Ramdas (2023) has a smaller limiting width than existing empirical Bernstein (EB)-CIs. (ii) Next, we establish two lower bounds that characterize the minimum width achievable by any method for constructing CIs/CSs in terms of certain inverse information projections. (iii) Finally, we show that the betting CI and CS match the fundamental limits, modulo an additive logarithmic term and a multiplicative constant. Overall these results imply that the betting CI~(and CS) admit stronger theoretical guarantees than the existing state-of-the-art EB-CI~(and CS); both in the asymptotic and finite-sample regimes.
Soda: An Object-Oriented Functional Language for Specifying Human-Centered Problems

We present Soda (Symbolic Objective Descriptive Analysis), a language that helps to treat qualities and quantities in a natural way and greatly simplifies the task of checking their correctness. We present key properties for the language motivated by the design of a descriptive language to encode complex requirements on computer systems, and we explain how these key properties must be addressed to model these requirements with simple definitions. We give an overview of a tool that helps to describe problems in an easy way that we consider more transparent and less error-prone.
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration

Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datasets of sharp ground truth images and trained with supervision. Sharp, high quality ground truth images, however, are not always available, especially for biomedical applications. This severely hampers the applicability of current approaches in practice. In this paper, we propose a novel non-blind deconvolution method that leverages the power of deep learning and classic iterative deconvolution algorithms. Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot optimisation process is employed to integrate the deconvolved features, resulting in a high-quality reconstructed image. By performing the preliminary reconstruction with the classic iterative deconvolution method, we can effectively utilise a smaller network to produce the final image, thus accelerating the reconstruction whilst reducing the demand for valuable computational resources. Our method demonstrates significant improvements in various real-world applications non-blind deconvolution tasks.
Spectro-Spatial Hyperspectral Image Reconstruction From Interferometric Acquisitions

In the last decade, novel hyperspectral cameras have been developed with particularly desirable characteristics of compactness and short acquisition time, retaining their potential to obtain spectral/spatial resolution competitive with respect to traditional cameras. However, a computational effort is required to recover an interpretable data cube. In this work we focus our attention on imaging spectrometers based on interferometry, for which the raw acquisition is an image whose spectral component is expressed as an interferogram. Previous works have focused on the inversion of such acquisition on a pixel-by-pixel basis within a Bayesian framework, leaving behind critical information on the spatial structure of the image data cube. In this work, we address this problem by integrating a spatial regularization for image reconstruction, showing that the combination of spectral and spatial regularizers leads to enhanced performances with respect to the pixelwise case. We compare our results with Plug-and-Play techniques, as its strategy to inject a set of denoisers from the literature can be implemented seamlessly with our physics-based formulation of the optimization problem.
Unifying Safety Approaches for Stochastic Systems: From Barrier Functions to Uncertain Abstractions via Dynamic Programming

Providing safety guarantees for stochastic dynamical systems has become a central problem in many fields, including control theory, machine learning, and robotics. Existing methods either employ Stochastic Barrier Functions (SBFs) or rely on numerical approaches based on abstractions. While SBFs are analogous to Lyapunov functions to prove (probabilistic) set invariance, abstraction-based approaches approximate the stochastic system into a finite model for the computation of safety probability bounds. In this paper, we offer a new perspective on these seemingly different methods. We show that both these approaches arise as approximations of a stochastic dynamic programming problem. Such a new and unifying perspective allows us to formally show the correctness of both approaches, characterize their convergence and optimality properties, and examine advantages and disadvantages of each. Our analysis reveals that abstraction-based methods can provide more accurate certificates of safety, but generally at a higher computational cost. We conclude the article with a discussion that highlights possible future directions.
Closing the Curious Case of Neural Text Degeneration

Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation methods that discard tokens below some probability threshold (the most common type of truncation) can guarantee that all sampled tokens have nonzero true probability. However, thresholds are a coarse heuristic, and necessarily discard some tokens with nonzero true probability as well. In pursuit of a more precise sampling strategy, we show that we can leverage a known source of model errors, the softmax bottleneck, to prove that certain tokens have nonzero true probability, without relying on a threshold. Based on our findings, we develop an experimental truncation strategy and the present pilot studies demonstrating the promise of this type of algorithm. Our evaluations show that our method outperforms its threshold-based counterparts under automatic and human evaluation metrics for low-entropy (i.e., close to greedy) open-ended text generation. Our theoretical findings and pilot experiments provide both insight into why truncation sampling works, and make progress toward more expressive sampling algorithms that better surface the generative capabilities of large language models.
Spherical Rolling Robots Design, Modeling, and Control: A Systematic Literature Review

Spherical robots have garnered increasing interest for their applications in exploration, tunnel inspection, and extraterrestrial missions. Diverse designs have emerged, including barycentric configurations, pendulum-based mechanisms, etc. In addition, a wide spectrum of control strategies has been proposed, ranging from traditional PID approaches to cutting-edge neural networks. Our systematic review aims to comprehensively identify and categorize locomotion systems and control schemes employed by spherical robots, spanning the years 1996 to 2023. A meticulous search across five databases yielded a dataset of 3189 records. As a result of our exhaustive analysis, we identified a collection of novel designs and control strategies. Leveraging the insights garnered, we provide valuable recommendations for optimizing the design and control aspects of spherical robots, supporting both novel design endeavors and the advancement of field deployments. Furthermore, we illuminate key research directions that hold the potential to unlock the full capabilities of spherical robots
Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View

As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple large language models (LLMs)? This paper probes the collaboration mechanisms among contemporary NLP systems by melding practical experiments with theoretical insights. We fabricate four unique `societies' comprised of LLM agents, where each agent is characterized by a specific `trait' (easy-going or overconfident) and engages in collaboration with a distinct `thinking pattern' (debate or reflection). Through evaluating these multi-agent societies on three benchmark datasets, we discern that certain collaborative strategies not only outshine previous top-tier approaches, but also optimize efficiency (using fewer API tokens). Moreover, our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring foundational social psychology theories. In conclusion, we integrate insights from social psychology to contextualize the collaboration of LLM agents, inspiring further investigations into the collaboration mechanism for LLMs. We commit to sharing our code and datasets\footnote{\url{https://github.com/zjunlp/MachineSoM}. }, hoping to catalyze further research in this promising avenue.
AI-Generated Images as Data Source: The Dawn of Synthetic Era

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .
Property-preserving numerical approximations of a Cahn-Hilliard-Navier-Stokes model with variable densities and degenerate mobility

In this paper, we present a new computational framework using coupled and decoupled approximations for a Cahn-Hilliard-Navier-Stokes model with variable densities and degenerate mobility. In this sense, the coupled approximation is shown to conserve the mass of the fluid, preserve the point-wise bounds of the density and decrease an energy functional. In contrast, the decoupled scheme is presented as a more computationally efficient alternative but the discrete energy-decreasing property can not be assured. Both schemes are based on a finite element approximation for the Navier-Stokes fluid flow with discontinuous pressure and an upwind discontinuous Galerkin scheme for the Cahn-Hilliard part. Finally, several numerical experiments contrasting both approaches are conducted. In particular, results for a convergence test, a simple qualitative comparison and some well-known benchmark problems are shown.
Collaborative Active SLAM: Synchronous and Asynchronous Coordination Among Agents

In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present two approaches for coordinating a system consisting of multiple robots to perform Active Collaborative SLAM (AC-SLAM) for environmental exploration. Our two coordination approaches, synchronous and asynchronous implement a methodology to prioritize robot goal assignments by the central server. We also present a method to efficiently spread the robots for maximum exploration while keeping SLAM uncertainty low. Both coordination approaches were evaluated through simulation and experiments on publicly available datasets, rendering promising results.