Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Domain Walking with Reduced-Order Models of Locomotion

Drawing inspiration from human multi-domain walking, this work presents a novel reduced-order model based framework for realizing multi-domain robotic walking. At the core of our approach is the viewpoint that human walking can be represented by a hybrid dynamical system, with continuous phases that are fully-actuated, under-actuated, and over-actuated and discrete changes in actuation type occurring with changes in contact. Leveraging this perspective, we synthesize a multi-domain linear inverted pendulum (MLIP) model of locomotion. Utilizing the step-to-step dynamics of the MLIP model, we successfully demonstrate multi-domain walking behaviors on the bipedal robot Cassie -- a high degree of freedom 3D bipedal robot. Thus, we show the ability to bridge the gap between multi-domain reduced order models and full-order multi-contact locomotion. Additionally, our results showcase the ability of the proposed method to achieve versatile speed-tracking performance and robust push recovery behaviors.
MORALS: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space

Estimating the region of attraction (${\tt RoA}$) for a robot controller is essential for safe application and controller composition. Many existing methods require a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that topological tools based on ${\it Morse Graphs}$ (directed acyclic graphs that combinatorially represent the underlying nonlinear dynamics) offer data-efficient ${\tt RoA}$ estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a state-space discretization. This paper presents ${\it Mo}$rse Graph-aided discovery of ${\it R}$egions of ${\it A}$ttraction in a learned ${\it L}$atent ${\it S}$pace (${\tt MORALS}$). The approach combines auto-encoding neural networks with Morse Graphs. ${\tt MORALS}$ shows promising predictive capabilities in estimating attractors and their ${\tt RoA}$s for data-driven controllers operating over high-dimensional systems, including a 67-dim humanoid robot and a 96-dim 3-fingered manipulator. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior. The evaluation on high-dimensional robotic datasets indicates data efficiency in ${\tt RoA}$ estimation.
Vocal Fold Reconstruction from Optical Velocity and Displacement Measurements

The three-dimensional reconstruction of vocal folds in medicine usually involves endoscopy and an approach to extract depth information like structured light or stereo matching of images. The resulting mesh can accurately represent the superior area of the vocal folds, while new approaches also try to reconstruct the inferior area. We propose a novel approach to extract the time-dependent 3D geometry of the vocal fold from optical measurements on both the superior and inferior side, requiring optical measurements only from the superior side. First, a time-dependent, tri-variate surface velocity vector field is reconstructed using a high-speed camera and a laser Doppler vibrometer in an experimental environment. This vector field serves as target in an inverse finite-element simulation that optimizes the forces applied to a deformable vocal fold model such that the resulting movement after FEM simulation matches the velocity observations on the superior side. The required forces for the finite element method simulation are treated as unknowns and are assembled using multiple scalar fields. We use tensor products in B\'ezier Bernstein basis for our scalar fields to reduce the degrees of freedom for our optimization. We use gradient descent to optimize the control points of the force field polynomials. Our utilized error metric for gradient descent consists of two terms. The first term is used to match the simulated velocities to the observed measurements, while the second term measures the silhouette difference between observation and simulation.
Dynamic Flow Control Through Active Matter Programming Language

Dynamic networks of cytoskeletal ﬁlaments and motor proteins can generate active forces that are essential in many cellular functions, such as cell division, motility, structural support and intracellular transport. Active ﬂuids with such living matter can be chaotic without a control mechanism [1], limiting their utility for bioengineering applications. Recently-developed light-activatable motors, which only crosslink the microtubules under illumination, have been previously applied to sculpt persistent ﬂuid ﬂows [2, 3]. However, the coupled dynamics of active networks and ﬂuid is still poorly understood. Here we construct a coarse-grained model that predicts active matter and ﬂuid ﬂows under dynamic optical input, which shows good agreement with experiments. Using light bars as the basic units, we are able to deﬁne the programming language of active matter and create local stretching and mixing ﬂows. In general the ﬂow ﬁelds induced by individual bars cannot be linearly superimposed in a composition. Combining theoretical predictions and experimental results, we ﬁnd that the linear superimposition can be achieved above a critical distance. Therefore, diﬀerent ﬂows can be realized simultaneously within a single channel through the spatio-temporal composition and manipulation of light patterns, making multitasking possible in a single channel. Our theoretical framework may be extended for other emergent structures of cytoskeleton, and our platform has potentials for building optically programmable ﬂow-control devices.
Wasserstein Distortion: Unifying Fidelity and Realism

We introduce a distortion measure for images, Wasserstein distortion, that simultaneously generalizes pixel-level fidelity on the one hand and realism or perceptual quality on the other. We show how Wasserstein distortion reduces to a pure fidelity constraint or a pure realism constraint under different parameter choices and discuss its metric properties. Pairs of images that are close under Wasserstein distortion illustrate its utility. In particular, we generate random textures that have high fidelity to a reference texture in one location of the image and smoothly transition to an independent realization of the texture as one moves away from this point. Wasserstein distortion attempts to generalize and unify prior work on texture generation, image realism and distortion, and models of the early human visual system, in the form of an optimizable metric in the mathematical sense.
Environmental effects of agricultural conservation: A framework for research in two watersheds in Oklahoma's Upper Washita River Basin

Agriculture in the Upper Washita River Basin represents mixed crop-livestock systems of the Southern Plains. Research in the Little Washita River Experimental Watershed and the Fort Cobb Reservoir Experimental Watershed addresses interactive effects of variable climate, land use, and management on environmental quality. The Little Washita River watershed provides opportunities to explore impacts of flood retarding impoundments within a watershed. The Fort Cobb Reservoir watershed provides opportunities to study effects of agricultural conservation on a large eutrophic reservoir. Analysis of 1940 to 2005 data from the Fort Cobb Reservoir watershed showed that precipitation increased 33%, corresponding runoff increased 101%, and sediment yield increased 183% when comparing multi-year wet periods to multi-year dry periods. Depth to groundwater exhibited seasonal and interannual variation. A rapid geomorphic assessment indicated that unstable stream channels dominate the stream networks. Phosphorus concentration in streams was correlated to multiple attributes of the contributing areas, including contributing area, slope, stream density, and channel stability. Anticipated outcomes are improved understanding of environmental effects of conservation, new approaches to mitigation of water quality problems, and tools to support strategic placement of conservation practices on the landscape to achieve environmental goals.
HeaP: Hierarchical Policies for Web Actions using LLMs

Large language models (LLMs) have demonstrated remarkable capabilities in performing a range of instruction following tasks in few and zero-shot settings. However, teaching LLMs to perform tasks on the web presents fundamental challenges -- combinatorially large open-world tasks and variations across web interfaces. We tackle these challenges by leveraging LLMs to decompose web tasks into a collection of sub-tasks, each of which can be solved by a low-level, closed-loop policy. These policies constitute a shared grammar across tasks, i.e., new web tasks can be expressed as a composition of these policies. We propose a novel framework, Hierarchical Policies for Web Actions using LLMs (HeaP), that learns a set of hierarchical LLM prompts from demonstrations for planning high-level tasks and executing them via a sequence of low-level policies. We evaluate HeaP against a range of baselines on a suite of web tasks, including MiniWoB++, WebArena, a mock airline CRM, as well as live website interactions, and show that it is able to outperform prior works using orders of magnitude less data.
Xcrum: A Synergistic Approach Integrating Extreme Programming with Scrum

In today's modern world, software plays a pivotal role. Software development is a highly complex and time-consuming process, demanding multidimensional efforts. Companies continually adapt their requirements to align with the evolving environment, with a specific emphasis on rapid delivery and the acceptance of changing requirements. Traditional models, such as plan-driven development, often fall short in meeting these demands. In the realm of software development, Agile has been the focal point of global discourse for both researchers and developers. Agile development is better suited to customize and streamline the development process, offering a highly flexible, early, and rapid delivery lifecycle conducive to efficient software development. This article aims to provide an overview of two prominent Agile methodologies: Scrum and Extreme Programming (XP). It achieves this by reviewing relevant publications, analyzing their impact on software development, exploring the distinctive features of each methodology, and conducting a comparative assessment. Furthermore, the article offers personal insights and recommendations. Notably, the integration of XP practices into Scrum has given rise to a novel hybrid methodology known as"Xcrum,"which retains its agility. It should be highlighted that, given this new approach's incorporation of the strengths of both methods, it holds the potential to outperform the original frameworks.
Scale-Dependent Detection of Climate Change

Abstract Spatially and temporally dependent fingerprint patterns of near-surface temperature change are derived from transient climate simulations of the second Hadley Centre coupled ocean–atmosphere GCM (HADCM2). Trends in near-surface temperature are calculated from simulations in which HADCM2 is forced with historical increases in greenhouse gases only and with both greenhouse gases and anthropogenic sulfur emissions. For each response an ensemble of four simulations is carried out. An estimate of the natural internal variability of the ocean–atmosphere system is taken from a long multicentury control run of HADCM2. The aim of the study is to investigate the spatial and temporal scales on which it is possible to detect a significant change in climate. Temporal scales are determined by taking temperature trends over 10, 30, and 50 yr using annual mean data, and spatial scales are defined by projecting these trends onto spherical harmonics. Each fingerprint pattern is projected onto the recent observed p.
Paleovegetational Reconstruction Based on Modern and Fossil Pollen Data: An Application of Discriminant Analysis

Abstract This paper introduces the use of discriminant analysis as an aid in paleovegetational reconstruction based on modern and fossil pollen data. We derived a set of discriminant functions that correctly classifies 95 percent of 121 surface samples collected from different vegetation regions in northeastern North America. These functions were then applied to the pollen-stratigraphic data from Jack Lake to reconstruct the postglacial vegetational history of northern Ontario. The discriminant analysis results are summarized in two indices. A “vegetation zonal index'’directly translates each fossil pollen spectrum into its modern vegetational analogue. Paleovegetation types without modern analogue could be easily detected by computing the “probability of modern analogue. '’These results indicate explicitly that the early spruce-dominated boreal forest around Jack Lake does not have a modern analogue and that the boreal forest/Great Lakes—St. Lawrence Forest ecotone advanced far north of Jack Lake during t.
Missing sinks, feedbacks, and understanding the role of terrestrial ecosystems in the global carbon balance

Terrestrial ecosystems are thought to be a major sink for carbon at the present time. The endeavor to find this terrestrial sink and to determine the mechanisms responsible has dominated terrestrial research on the global carbon cycle for years. Some of the mechanisms advanced to explain the “missing sink” are also negative feedbacks to a global warming. Here we distinguish between mechanisms likely to act as feedbacks to a global warming and other mechanisms consistent with a terrestrial sink that are not feedbacks to a global warming. One of the postulated negative feedback mechanisms that also helps explain the current “missing sink” is based on the theory that carbon should accumulate in vegetation as a result of a warming‐enhanced mineralization of nitrogen in soil organic matter. The theory assumes that mineralized N is neither retained in the soil (through reimmobilization by microbial biomass) nor lost from the ecosystem, but rather becomes available for plant growth. None of these assumptions is supported yet by field data. In contrast, trends across existing climatic gradients suggest that warmer temperatures will lead to a decrease in the C:N ratio of soils (i.e., the mineralized N remains in soil). Data pertaining to temporal variability in the global carbon balance are conflicting with respect to the question of whether increasing temperatures cause a release or storage of terrestrial carbon. The answer seems to depend in part on time scale. Most likely, multiple mechanisms, including some that release carbon and others that accumulate it, account for the present net accumulation of carbon on land. However, a positive feedback between temperature and the release of CO2 to the atmosphere by terrestrial respiration seems likely to grow in importance and could change significantly the role that terrestrial ecosystems play in the global carbon balance.
Learning Trivializing Flows in a $\phi^4$ theory from coarser lattices

The so-called trivializing flows were proposed to speed up Hybrid Monte Carlo simulations, where the Wilson flow was used as an approximation of a trivializing map, a transformation of the gauge fields which trivializes the theory. It was shown that the scaling of the computational costs towards the continuum did not change with respect to HMC. The introduction of machine learning tecniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on solving topology freezing in lattice QCD simulations. In this talk I will present our work in a $\phi^{4}$ theory using normalizing flows as trivializing flows (given its similarity with the idea of a trivializing map), training from a trivial distribution as well as from coarser lattices, and study its scaling towards the continuum, comparing it with standard HMC.
ContactGen: Generative Contact Modeling for Grasp Generation

This paper presents a novel object-centric contact representation ContactGen for hand-object interaction. The ContactGen comprises 3 components: a contact map indicates the contact location, a part map represents the contact hand part, and a direction map tells the contact direction within each part. Given an input object, we propose a conditional generative model to predict ContactGen and adopt model-based optimization to predict diverse and geometrically feasible grasps. Experimental results demonstrate our method can generate high-fidelity and diverse human grasps for various objects.
Improved Baselines with Visual Instruction Tuning

Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ~1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available.
Boundary transfer matrix spectrum of measurement-induced transitions

Measurement-induced phase transitions (MIPTs) are known to be described by non-unitary conformal field theories (CFTs) whose precise nature remains unknown. Most physical quantities of interest, such as the entanglement features of quantum trajectories, are described by boundary observables in this CFT. We introduce a transfer matrix approach to study the boundary spectrum of this field theory, and consider a variety of boundary conditions. We apply this approach numerically to monitored Haar and Clifford circuits, and to the measurement-only Ising model where the boundary scaling dimensions can be derived analytically. Our transfer matrix approach provides a systematic numerical tool to study the spectrum of MIPTs.
New Auction Algorithms for the Assignment Problem and Extensions

We consider the classical linear assignment problem, and we introduce new auction algorithms for its optimal and suboptimal solution. The algorithms are founded on duality theory, and are related to ideas of competitive bidding by persons for objects and the attendant market equilibrium, which underlie real-life auction processes. We distinguish between two fundamentally different types of bidding mechanisms: aggressive and cooperative. Mathematically, aggressive bidding relies on a notion of approximate coordinate descent in dual space, an epsilon-complementary slackness condition to regulate the amount of descent approximation, and the idea of epsilon-scaling to resolve efficiently the price wars that occur naturally as multiple bidders compete for a smaller number of valuable objects. Cooperative bidding avoids price wars through detection and cooperative resolution of any competitive impasse that involves a group of persons. We discuss the relations between the aggressive and the cooperative bidding approaches, we derive new algorithms and variations that combine ideas from both of them, and we also make connections with other primal-dual methods, including the Hungarian method. Furthermore, our discussion points the way to algorithmic extensions that apply more broadly to network optimization, including shortest path, max-flow, transportation, and minimum cost flow problems with both linear and convex cost functions.
Peat fabric and structure.

The results of various index tests and standard and scanning electron microscope examinations of Sphagnum and sedge peats form the basis for an investigation of peat fabric and structure.The volumetric and gravimetric relationships of a moderately humified Sphagnum peat are examined in detail, and phase diagrams are constructed for two Sphagnum peats with different degrees of humification. Tentative phase diagrams for a sedge peat are also presented.A brief examination is made of existing classification systems and of some aspects of the formation of peats.
Boundary signature of singularity in the presence of a shock wave

Matter falling into a Schwarzschild-AdS black hole from the left causes increased focussing of ingoing geodesics from the right, and, as a consequence, they reach the singularity sooner. In a standard Penrose diagram, the singularity “bends down”. We show how to detect this feature of the singularity holographically, using a boundary two-point function. We model the matter with a shock wave, and show that this bending down of the singularity can be read off from a novel analytic continuation of the boundary two-point function. Along the way, we obtain a generalization of the recently proposed thermal product formula for two-point correlators.
Detailed Thermal Characterization on a 48V Lithium-Ion Battery Pack during Charge-Discharge Cycles

This study experimentally investigates the temperature distribution and behavior of a 48V Lithium-Ion (Li-ion) battery pack during two charge-discharge cycles using 25 thermocouples. Results indicate that better convective heat transfer occurs at the external surfaces of the pack, while middle cells reach maximum temperatures. Differences are also observed in the behavior of the three modules. The discharge cycle shows a temperature rise of 5.8°? with a pack temperature gradient increasing from 1.3°? to 2.7°? The study highlights the importance of assessing the thermal behavior of each module and the complexity of the Li-ion battery pack system. Findings on the battery cells, modules, and pack in the same study can provide valuable insights for designing efficient cooling systems for Li-ion battery packs.
Non-perturbative mass renormalization effects in non-relativistic quantum electrodynamics

This work lays the foundation to accurately describe ground-state properties in multimode photonic environments and highlights the importance of the mass renormalization procedure for ab-initio quantum electrodynamics simulations. We first demonstrate this for free particles, where the energy dispersion is employed to determine the mass of the particles. We then show how the multimode photon field influences various ground and excited-state properties of atomic and molecular systems. For instance, we observe the enhancement of localization for the atomic system, and the modification of the potential energy surfaces of the molecular dimer due to photon-mediated long-range interactions. These phenomena get enhanced under strong light-matter coupling in a cavity environment and become relevant for the emerging field of polaritonic chemistry. We conclude by demonstrating how non-trivial ground-state effects due to the multimode field can be accurately captured by approximations that are simple and numerically feasible even for realistic systems.
An estimate of the impact of observed ozone losses on stratospheric temperature

The climatic impact of the observed stratospheric ozone changes since 1980 was studied in simulations with the Berlin Climate Middle Atmosphere Model (CMAM). The model was run with prescribed ozone climatologies of the years 1980 and 2000, derived from the newest WMO assessment of ozone trends. It is shown that in the northern hemisphere stratosphere, the temperature decreases as a reaction to the 20 year ozone changes. Whereas the patterns of the observed stratospheric cooling for that period are reproduced in the model, its magnitude is underestimated, indicating an impact of other factors on stratospheric temperature.