Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor

Despite recent progress in language models, generating constrained text for specific domains remains a challenge, particularly when utilizing black-box models that lack domain-specific knowledge. In this paper, we introduce ScoPE (Score-based Progressive Editor) generation, a novel approach for controlled text generation for black-box language models. We employ ScoPE to facilitate text generation in the target domain by integrating it with language models through a cascading approach. Trained to enhance the target domain score of the edited text, ScoPE progressively edits intermediate output discrete tokens to align with the target attributes throughout the auto-regressive generation process of the language model. This iterative process guides subsequent steps to produce desired output texts for the target domain. Our experimental results on diverse controlled generations demonstrate that ScoPE effectively facilitates controlled text generation for black-box language models in both in-domain and out-of-domain conditions, which is challenging for existing methods.
Inference on spatiotemporal dynamics for networks of biological populations (preprint)

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference due to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been developed which enables effective likelihood-based inference for the high-dimensional partially observed stochastic dynamic models arising in metapopulation systems. The COVID-19 pandemic provides a situation where mathematical models and their policy implications were widely visible, and we use the new inferential technology to revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model limitations, and leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on January 23, 2020 in China was more effective than previously thought. We proceed to recommend statistical analysis standards for future metapopulation system modeling.
Paleozoic Rocks of North-Central Nevada

Recent geologic mapping and stratigraphic study in north-central Nevada has increased our knowledge of the Paleozoic stratigraphy and structural history of this complex area. The present paper summarizes the stratigraphy and outlines a suggested synthesis of the Paleozoic geologic history. Eastern Nevada differs greatly in stratigraphy and structural history from the western part. In eastern Nevada, roughly east of longitudes 116°-117°, the Paleozoic rocks from Middle Cambrian to Upper Mississippian are mostly limestone and dolomite with minor amounts of shale and quartzite. In central and western Nevada the correlative strata are predominantly clastic sedimentary rocks and chert, with intercalated volcanic rocks and pyroclastics. The eastern assemblage is on the order of 15,000 feet thick in most places; the western assemblage is much thicker, probably more than 50,000 feet. The depositional environments of the eastern and western assemblages were clearly different. The eastern assemblage is miogeosynclinal, the western eugeosynclinal. The two have been broug t into contact by telescoping along a thrust fault of great magnitude--the Roberts Mountains thrust--which carried the western assemblage relatively eastward or southeastward over the eastern assemblage. In several places an assemblage that does not belong clearly to either the eastern or western assemblages, but includes elements of both, is recognized as transitional. Rocks of the transitional assemblage occur both in parautochthonous windows beneath the Roberts Mountains thrust plate of western assemblage rocks, and as slivers in the upper plate of the thrust. The broad geosyncline in which the three assemblages were laid down persisted, with local disturbances beginning in Late Cambrian, until the end of Devonian time. A belt along the 116°-118° meridians--the Antler orogenic belt--was the locus of intense folding and faulting that culminated in the Roberts Mountains thrust fault in Late Devonian or Early Mississippian time. From an End_Page 2813------------------------------ emerged area along this belt coarse clastic rocks were deposited both eastward and westward; these grade laterally into finer sediments and limestone of normal marine facies. In the orogenic belt the sediments that were deposited on the deformed strata of the eastern, transitional, and western assemblages are designated the overlap assemblage. Orogenic movements continued along the belt in Pennsylvanian and Permian time, and throughout the Mesozoic, probably into Cretaceous time, causing further deformation of the previously folded and thrust-faulted rocks and of the sediments of the overlap assemblage as well. A correlation chart and representative stratigraphic sections of the several stratigraphic assemblages are included.
Fast and Space-Efficient Parallel Algorithms for Influence Maximization

Influence Maximization (IM) is a crucial problem in data science. The goal is to find a fixed-size set of highly influential seed vertices on a network to maximize the influence spread along the edges. While IM is NP-hard on commonly used diffusion models, a greedy algorithm can achieve (1 - 1/ e )-approximation by repeatedly selecting the vertex with the highest marginal gain in influence as the seed. However, we observe two performance issues in the existing work that prevent them from scaling to today's large-scale graphs: space-inefficient memorization to estimate marginal gain, and time-inefficient seed selection process due to a lack of parallelism. This paper significantly improves the scalability of IM using two key techniques. The first is a sketch-compression technique for the independent cascading model on undirected graphs. It allows combining the simulation and sketching approaches to achieve a time-space tradeoff. The second technique includes new data structures for parallel seed selection. Using our new approaches, we implemented PaC-IM : Parallel and Compressed IM. We compare PaC-IM with state-of-the-art parallel IM systems on a 96-core machine with 1.5TB memory. PaC-IM can process the ClueWeb graph with 978M vertices and 75B edges in about 2 hours. On average, across all tested graphs, our uncompressed version is 5--18x faster and about 1.4x more space-efficient than existing parallel IM systems. Using compression further saves 3.8x space with only 70% overhead in time on average.
Comparison theorems for multi-dimensional BSDEs with jumps and applications to constrained stochastic linear-quadratic control

In this paper, we, for the first time, establish two comparison theorems for multi-dimensional backward stochastic differential equations with jumps. Our approach is novel and completely different from the existing results for one-dimensional case. Using these and other delicate tools, we then construct solutions to coupled two-dimensional stochastic Riccati equation with jumps in both standard and singular cases. In the end, these results are applied to solve a cone-constrained stochastic linear-quadratic and a mean-variance portfolio selection problem with jumps. Different from no jump problems, the optimal (relative) state processes may change their signs, which is of course due to the presence of jumps.
Contributions of Automobile Mechanic Sites to Heavy Metals in Soil: A Case Study of North Bank Mechanic Village Makurdi, Benue State, Central Nigeria

The distribution of six heavy metals (Pb, Cu, Zn, M n, Ni and Cd) in soil around North Bank automobile mechanic site in Benue State, North Central Nigeria were studied using AAS. Results o f the analysis revealed that majority of heavy metal concentrations (mgkg-1 ) of the samples were above background levels and threshold limits recommended for soils in some countries. The results indicate the following level s Cu, (24.6mgkg-1), Pb (123 mgkg-1), Zn (42.7 mgkg-1), Mn (92.0mgkg-1), Ni (8.44mgkg-1) and Cd (0.60 mgkg-1) with a distribution pattern in the order: P b>Mn>Zn>Cu>Ni>Cd. Factors which influence the mobility of metals in s oils were determined such as pH, cation exchange capacity, organic matter, mo isture content and soil texture. Geoaccumulation index values of the metals in soils under study showed that the environment is highly polluted with Pb and Cu, and to a lesser degree with Ni. Both Cd and Mn showed moderate pollution status while the soil remains unpolluted with Zn.
Efficient adaptivity for simulating cardiac electrophysiology with spectral deferred correction methods

The locality of solution features in cardiac electrophysiology simulations calls for adaptive methods. Due to the overhead incurred by established mesh refinement and coarsening, however, such approaches failed in accelerating the computations. Here we investigate a different route to spatial adaptivity that is based on nested subset selection for algebraic degrees of freedom in spectral deferred correction methods. This combination of algebraic adaptivity and iterative solvers for higher order collocation time stepping realizes a multirate integration with minimal overhead. This leads to moderate but significant speedups in both monodomain and cell-by-cell models of cardiac excitation, as demonstrated at four numerical examples.
Benchmarking Northern Hemisphere midlatitude atmospheric synoptic variability in centennial reanalysis and numerical simulations

The representation of midlatitude winter atmospheric synoptic variability in centennial reanalysis products, which assimilate surface observations only, and atmospheric model simulations constrained by observation‐based data sets is assessed. Midlatitude waves activity in twentieth century reanalyses (20CR, ERA‐20C) and atmospheric model simulations are compared with those estimated from observationally complete reanalysis products. All reanalyses are in good agreement regarding the representation of the synoptic variability during the last decades of the twentieth century. This suggests that the assimilation of surface observations can generate high‐quality extratropical upper air fields. In the first decades of the twentieth century a suppression of high‐frequency variability is apparent in the centennial reanalysis products. This behavior does not have a counterpart in the atmospheric model integrations. Since the latter differ from one of the reanalysis products considered here (ERA‐20C) only in the assimilation of surface observations, it seems reasonable to attribute the high‐frequency variability suppression to the poor coverage of the observations assimilated.
Large Language Models for Robotics: A Survey

The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning. We first provide an overview of the background and development of LLMs for robotics, followed by a description of the benefits of LLMs for robotics and recent advancements in robotics models based on LLMs. We then delve into the various techniques used in the model, including those employed in perception, decision-making, control, and interaction. Finally, we explore the applications of LLMs in robotics and some potential challenges they may face in the near future. Embodied intelligence is the future of intelligent science, and LLMs-based robotics is one of the promising but challenging paths to achieve this.
LIMITS Special Issue on Durban Platform scenarios A multi-model analysis of the regional and sectoral roles of bioenergy in near- and long-term CO2 emissions reduction

This paper examines the near- and the long-term contribution of regional and sectoral bioenergy use in response to both regionally diverse near-term policies and longer-term global climate change mitigation policies. The use of several models provides a source of heterogeneity in terms of incorporating uncertain assumptions about future socioeconomics and technology, as well as different paradigms for how different regions and major economies of the world may respond to climate policies. The results highlight the heterogeneity and versatility of bioenergy itself, with different types of resources and applications in several energy sectors. In large part due to this versatility, the contribution of bioenergy to climate mitigation is a robust response across all models. Regional differences in bioenergy consumption, however, highlight the importance of assumptions about trade in bioenergy feedstocks and the influence of energy and climate policies. When global trade in bioenergy is possible, regional patterns of bioenergy use follow global patterns. When trade is assumed not to be feasible, regions with high bioenergy supply potential tend to consume more bioenergy than other regions. Energy and climate policies, such as renewable energy targets, can incentivize bioenergy use, but specifics of the policies will dictate the degree to which this is true. For example, renewable final energy targets, which include electric and non-electric renewable sources, increase bioenergy use in all models, while electric-only renewable targets have a mixed effect on bioenergy use across models.
Analysis and Numerical Study of Boundary control of generalized Burgers-Huxley equation

In this work, a boundary control problem for the following generalized Burgers-Huxley (GBH) equation: $$u_t=\nu u_{xx}-\alpha u^{\delta}u_x+\beta u(1-u^{\delta})(u^{\delta}-\gamma), $$ where $\nu,\alpha,\beta>0,$ $1\leq\delta<\infty$, $\gamma\in(0,1)$ subject to Neumann boundary conditions is analyzed. Using the Minty-Browder theorem, standard elliptic partial differential equations theory, the maximum principle and the Crandall-Liggett theorem, we first address the global existence of a unique strong solution to GBH equation. Then, for the boundary control problem, we prove that the controlled GBH equation (that is, the closed loop system) is exponentially stable in the $\mathrm{H}^1$-norm (hence pointwise) when the viscosity $\nu$ is known (non-adaptive control). Moreover, we show that a damped version of GBH equation is globally asymptotically stable (in the $\mathrm{L}^2$-norm), when $\nu$ is unknown (adaptive control). Using the Chebychev collocation method with the backward Euler method as a temporal scheme, numerical findings are reported for both the non-adaptive and adaptive situations, supporting and confirming the analytical results of both the controlled and uncontrolled systems.
Red Giant Rotational Inversion Kernels Need Nonlinear Surface Corrections

Asteroseismology is our only means of measuring the rotations of stars in their interiors, rather than at their surfaces. Some techniques for measurements of this kind—“rotational inversions”—require the shapes of linear response kernels computed from reference stellar models to be representative of those in the stars they are intended to match. This is not the case in evolved stars exhibiting gravitoacoustic mixed modes: we show that the action of the asteroseismic surface term—systematic errors in the modeling of near-surface layers—changes the shapes of their inversion kernels. Corrections for the surface term are not ordinarily considered necessary for rotational inversions. We show how this may have caused previous estimates of red-giant envelope rotation rates from mixed-mode asteroseismic inversions to have been unintentionally contaminated by core rotation as a result, with errors comparable to the entire reported estimates. We derive a mitigation procedure for this hitherto unaccounted systematic error, and demonstrate its viability and effectiveness. We recommend this mitigation be applied when revising existing rotational inversions. Finally, we discuss both the prospects for applying such mitigation to the harder problem of inversions for stellar structure (rather than rotation), as well as the broader implications of this systematic error with regard to the longstanding problem of internal angular momentum transport.
Virtual Photons Shed Light on the Early Temperature of Dense QCD Matter

Dileptons produced during heavy-ion collisions represent a unique probe of the QCD phase diagram, and convey information about the state of the strongly interacting system at the moment their preceding off-shell photon is created. In this study, we compute thermal dilepton yields from Au+Au collisions performed at different beam energies, employing a (3+1)-dimensional dynamic framework combined with emission rates accurate at next-to-leading order in perturbation theory and which include baryon chemical potential dependencies. By comparing the effective temperature extracted from the thermal dilepton invariant mass spectrum with the average temperature of the fluid, we offer a robust quantitative validation of dileptons as effective probe of the early quark-gluon plasma stage.
Multisite, multifrequency tensor decomposition of magnetotelluric data

Accurate interpretation of magnetotelluric data requires an understanding of the directionality and dimensionality inherent in the data, and valid implementation of an appropriate method for removing the effects of shallow, small-scale galvanic scatterers on the data to yield responses representative of regionalscale structures. The galvanic distortion analysis approach advocated by Groom and Bailey has become the most adopted method, rightly so given that the approach decomposes the magnetotelluric impedance tensor into determinable and indeterminable parts, and tests statistically the validity of the galvanic distortion assumption. As proposed by Groom and Bailey, one must determine the appropriate frequency-independent telluric distortion parameters and geoelectric strike by fitting the seven-parameter model on a frequencyby-frequency and site-by-site basis independently. Although this approach has the attraction that one gains a more intimate understanding of the data set, it is rather time-consuming and requires repetitive application. We propose an extension to Groom-Bailey decomposition in which a global minimum is sought to determine the most appropriate strike direction and telluric distortion parameters for a range of frequencies and a set of sites. Also, we show how an analytically-derived approximate Hessian of the objective function can reduce the required computing time. We illustrate application of the analysis to two synthetic data sets and to real data. Finally, we show how the analysis can be extended to cover the case of frequency-dependent distortion caused by the magnetic effects of the galvanic charges.
PerceptionGPT: Effectively Fusing Visual Perception into LLM

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities, giving rise to visual large language models (VLLMs). However, effectively harnessing VLLMs for intricate visual perception tasks remains a challenge. In this paper, we present a novel end-to-end framework named PerceptionGPT, which efficiently and effectively equips the VLLMs with visual perception abilities by leveraging the representation power of LLMs' token embedding. Our proposed method treats the token embedding of the LLM as the carrier of spatial information, then leverage lightweight visual task encoders and decoders to perform visual perception tasks (e.g., detection, segmentation). Our approach significantly alleviates the training difficulty suffered by previous approaches that formulate the visual outputs as discrete tokens, and enables achieving superior performance with fewer trainable parameters, less training data and shorted training time. Moreover, as only one token embedding is required to decode the visual outputs, the resulting sequence length during inference is significantly reduced. Consequently, our approach enables accurate and flexible representations, seamless integration of visual perception tasks, and efficient handling of a multiple of visual outputs. We validate the effectiveness and efficiency of our approach through extensive experiments. The results demonstrate significant improvements over previous methods with much fewer trainable parameters and GPU hours, which facilitates future research in enabling LLMs with visual perception abilities.
CrashCar101: Procedural Generation for Damage Assessment

In this paper, we are interested in addressing the problem of damage assessment for vehicles, such as cars. This task requires not only detecting the location and the extent of the damage but also identifying the damaged part. To train a computer vision system for the semantic part and damage segmentation in images, we need to manually annotate images with costly pixel annotations for both part categories and damage types. To overcome this need, we propose to use synthetic data to train these models. Synthetic data can provide samples with high variability, pixel-accurate annotations, and arbitrarily large training sets without any human intervention. We propose a procedural generation pipeline that damages 3D car models and we obtain synthetic 2D images of damaged cars paired with pixel-accurate annotations for part and damage categories. To validate our idea, we execute our pipeline and render our CrashCar101 dataset. We run experiments on three real datasets for the tasks of part and damage segmentation. For part segmentation, we show that the segmentation models trained on a combination of real data and our synthetic data outperform all models trained only on real data. For damage segmentation, we show the sim2real transfer ability of CrashCar101.