Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically meaningful interactions that mimic real-world forces remain largely understudied. In this work, we investigate using physical forces as a control signal for video generation and propose force prompts which enable users to interact with images through both localized point forces, such as poking a plant, and global wind force fields, such as wind blowing on fabric. We demonstrate that these force prompts can enable videos to respond realistically to physical control signals by leveraging the visual and motion prior in the original pretrained model, without using any 3D asset or physics simulator at inference. The primary challenge of force prompting is the difficulty in obtaining high quality paired force-video training data, both in the real world due to the difficulty of obtaining force signals, and in synthetic data due to limitations in the visual quality and domain diversity of physics simulators. Our key finding is that video generation models can generalize remarkably well when adapted to follow physi
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers. Second, we devise theoretical models of kinematics and power transmission based on the proposed gripper, accurately obtaining fingertip positions and contact forces. Through coupling and decoupling of five-linkage mechanisms, the proposed gripper offers the expected capabilities of grasping payload/force/stability and objects with large dimension ranges. Third, to realize the force control, an LSTM model is proposed to determine the grasping mode for synthesizing force-feedback control policies that exploit contact sensing after outlining the uncertainty of currents using a statistical method. Finally, a series of experiments are implemented to measure quantitative indicators, such as the p
We consider the self-force acting on a pointlike (electromagnetic or conformal-scalar) charge held fixed on a spacetime with a spherically-symmetric mass distribution of constant density (the Schwarzschild star). The Schwarzschild interior is shown to be conformal to a three-sphere geometry; we use this conformal symmetry to obtain closed-form expressions for mode solutions. We calculate the self-force with two complementary regularization methods, direct and difference regularization, showing agreement. For the first time, we show that difference regularization can be applied in the non-vacuum interior region, due to the vanishing of certain regularized mode sums. The new results for the self-force come in three forms: series expansions for the self-force near the centre of the star and in the far field; a new approximation that describes the divergence in the self-force near the star's boundary; and numerical data presented in a selection of plots. We conclude with a discussion of the logarithmic divergence in the self-force in the approach to the star's surface, and the effect of boundaries.
Contact-based grasp generation plays a crucial role in various applications. Recent methods typically focus on the geometric structure of objects, producing grasps with diverse hand poses and plausible contact points. However, these approaches often overlook the physical attributes of the grasp, specifically the contact force, leading to reduced stability of the grasp. In this paper, we focus on stable grasp generation using explicit contact force predictions. First, we define a force-aware contact representation by transforming the normal force value into discrete levels and encoding it using a one-hot vector. Next, we introduce force-aware stability constraints. We define the stability problem as an acceleration minimization task and explicitly relate stability with contact geometry by formulating the underlying physical constraints. Finally, we present a pose optimizer that systematically integrates our contact representation and stability constraints to enable stable grasp generation. We show that these constraints can help identify key contact points for stability which provide effective initialization and guidance for optimization towards a stable grasp. Experiments are carri
Quantum forces are long-range interactions that arise only at the loop level. In this work, we study the Sommerfeld enhancement of dark matter (DM) annihilation cross sections caused by quantum forces. One notable feature of quantum forces is that they are subject to coherent enhancement in the presence of a background of mediator particles, which occurs in many situations in cosmology. We show that this effect has important implications for the Sommerfeld enhancement and DM physics. For the first time, we calculate the Sommerfeld factor induced by quantum forces for both bosonic and fermionic mediators, including the background corrections. We observe several novel features of the Sommerfeld factor that do not exist in the case of the Yukawa potential, such as temperature-induced resonance peaks for massless mediators, and having both enhancement and suppression effects in the same model with different DM masses. As direct applications, we discuss the DM phenomenology affected by the Sommerfeld enhancement from quantum forces, including thermal freeze-out, CMB spectral distortion from DM annihilation, and DM indirect detection. We highlight one particularly interesting effect rele
We discuss the tidal force, whose notion is sometimes misunderstood in the public domain literature. We discuss the tidal force exerted by a secondary point mass on an extended primary body such as the Earth. The tidal force arises because the gravitational force exerted on the extended body by the secondary mass is not uniform across the primary. In the derivation of the tidal force, the non-uniformity of the gravity is essential, and inertial forces such as the centrifugal force are not needed. Nevertheless, it is often asserted that the tidal force can be explained by the centrifugal force. If we literally take into account the centrifugal force, it would mislead us. We therefore also discuss the proper treatment of the centrifugal force.
In CFD simulations of two-phase flows, accurate drag force modeling is essential for predicting particle dynamics. However, a generally valid formulation is lacking, as all available drag force correlations have been established for specific flow situations. In particular, these correlations have not been evaluated for particle-laden flows subjected to electrostatic forces. The paper reports the effect of drag force modeling on the flow of electrically charged particles. To this end, we implemented different drag force correlations to the open-source CFD tool pafiX. Then, we performed highly-resolved Direct Numerical Simulations (DNS) using the Eulerian-Lagrangian approach of a particle-laden channel flow with the friction Reynolds number of 180. The simulations generally revealed a strong influence of the precise drag correlation on particles in the near-wall region and a minor effect on the particles far from the walls. Due to their turbophoretic drift, particles accumulate close to the channel walls. For uncharged particles, the simulations show large deviations of the particle concentration profile in the near-wall region depending on the drag force correlation. Therefore, the
Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure, despite their apparent differences. Here I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $Δ\mathbfθ = \mathbf{M}\,\mathbf{f} + \mathbf{b} + \mathbfξ$. The force $\mathbf{f}$ drives improvement in parameters, $Δ\mathbfθ$, in proportion to the slope of performance with respect to the parameters. The metric $\mathbf{M}$ rescales movement by inverse curvature. The bias $\mathbf{b}$ adds momentum or changes in the frame of reference. The noise $\mathbfξ$ enables exploration. This framework unifies natural selection, Bayesian updating, Newton's method, stochastic gradient descent, stochastic Langevin dynamics, Adam optimization, and most other algorithms as special cases of the same underlying process. The Price equation also reveals why Fisher information, Kullback-Leibler divergence, and d'Alembert's principle arise naturally in learning dynamics. By exposing this common structure, the FMB law provides a principled foundation for understanding, comparing, and designing learning algorithms across dis
Myosin II is the muscle molecular motor that works in two bipolar arrays in each thick filament of the striated (skeletal and cardiac) muscle, converting the chemical energy into steady force and shortening by cyclic ATP--driven interactions with the nearby actin filaments. Different isoforms of the myosin motor in the skeletal muscles account for the different functional requirements of the slow muscles (primarily responsible for the posture) and fast muscles (responsible for voluntary movements). To clarify the molecular basis of the differences, here the isoform--dependent mechanokinetic parameters underpinning the force of slow and fast muscles are defined with a unidimensional synthetic nanomachine powered by pure myosin isoforms from either slow or fast rabbit skeletal muscle. Data fitting with a stochastic model provides a self--consistent estimate of all the mechanokinetic properties of the motor ensemble including the motor force, the fraction of actin--attached motors and the rate of transition through the attachment--detachment cycle. The achievements in this paper set the stage for any future study on the emergent mechanokinetic properties of an ensemble of myosin molec
Atomic Force Microscopy (AFM) conventional static force curves and Force Feedback Microscopy (FFM) force curves acquired with the same cantilever at the solid/air and solid/liquid interfaces are here compared. The capability of the FFM to avoid the jump to contact leads to the complete and direct measurement of the interaction force curve, including the attractive short-range van der Waals and chemical contributions. Attractive force gradients five times higher than the lever stiffness do not affect the stability of the FFM static feedback loop. The feedback loop keeps the total force acting on the AFM tip equal to zero, allowing the use of soft cantilevers as force transducers to increase the instrumental sensitivity. The attractive interactions due to the nucleation of a capillary bridge at the native oxide silicon/air interface or due to a DLVO interaction at the mica/deionized water interface have been measured. This set up, suitable for measuring directly and quantitatively interfacial forces, can be exported to a SFA (Surface Force Apparatus).
The integration of AI systems into the military domain is changing the way war-related decisions are made. It binds together three disparate groups of actors - developers, integrators, users - and creates a relationship between these groups and the machine, embedded in the (pre-)existing organisational and system structures. In this article, we focus on the important, but often neglected, group of integrators within such a sociotechnical system. In complex human-machine configurations, integrators carry responsibility for linking the disparate groups of developers and users in the political and military system. To act as the mediating group requires a deep understanding of the other groups' activities, perspectives and norms. We thus ask which challenges and shortcomings emerge from integrating AI systems into resort-to-force (RTF) decision-making processes, and how to address them. To answer this, we proceed in three steps. First, we conceptualise the relationship between different groups of actors and AI systems as a sociotechnical system. Second, we identify challenges within such systems for human-machine teaming in RTF decisions. We focus on challenges that arise a) from the t
We compute the electromagnetic self-force acting on a charged particle held in place at a fixed position r outside a five-dimensional black hole described by the Schwarzschild-Tangherlini metric. Using a spherical-harmonic decomposition of the electrostatic potential and a regularization prescription based on the Hadamard Green's function, we express the self-force as a convergent mode sum. The self-force is first evaluated numerically, and next presented as an analytical expansion in powers of R/r, with R denoting the event-horizon radius. The power series is then summed to yield a closed-form expression. Unlike its four-dimensional version, the self-force features a dependence on a regularization parameter s that can be interpreted as the particle's radius. The self-force is repulsive at large distances, and its behavior is related to a model according to which the force results from a gravitational interaction between the black hole and the distribution of electrostatic field energy attached to the particle. The model, however, is shown to become inadequate as r becomes comparable to R, where the self-force changes sign and becomes attractive. We also calculate the self-force ac
Compact binaries with asymmetric mass ratios are key expected sources for next-generation gravitational wave detectors. Gravitational self-force theory has been successful in producing post-adiabatic waveforms that describe the quasi-circular inspiral around a non-spinning black hole with sub-radian accuracy, in remarkable agreement with numerical relativity simulations. Current inspiral models, however, break down at the innermost stable circular orbit, missing part of the waveform as the secondary body transitions to a plunge into the black hole. In this work we derive the transition-to-plunge expansion within a multiscale framework and asymptotically match its early-time behaviour with the late inspiral. Our multiscale formulation facilitates rapid generation of waveforms: we build second post-leading transition-to-plunge waveforms, named 2PLT waveforms. Although our numerical results are limited to low perturbative orders, our framework contains the analytic tools for building higher-order waveforms consistent with post-adiabatic inspirals, once all the necessary numerical self-force data becomes available. We validate our framework by comparing against numerical relativity sim
We present polynomial force reconstruction from experimental intermodulation atomic force microscopy (ImAFM) data. We study the tip-surface force during a slow surface approach and compare the results with amplitude-dependence force spectroscopy. Based on polynomial force reconstruction we generate high-resolution surface property maps of polymer blend samples. The polynomial method is described as a special example of a more general approximataive force reconstruction, where the aim is to determine model parameters which best approximate the measured force spectrum. This approximative approach is not limited to spectral data and we demonstrate how is can adapted to a force quadrature picture.
Pedestrian movements can be modeled at different degrees of detail. While flux models (Predeshensky/Milinski 1971) and cellular automata models (Schreckenberg 2002) give answers to some important questions and are fast and easy to use, continuous space modeling has the potential of full flexibility in geometry and realistic description of individual movements in arbitrary fine resolution. While the acceleration forces in these models are known with good reliability, there is no agreement on the repulsive forces, not even on the functional form of these forces (Lakoba 2005, Molnar 1996, Parisi 2005, Yu 2005). We give some basic consideration to define the minimal complexity of the functional form of the repulsive forces together with some estimates of the values of parameters. From these considerations it becomes obvious that the repulsive forces have to depend not only on the relative position of persons, but also on the speeds and speed differences. The parameters of these forces will be situation dependant. They can in principle be derived from video observations of people moving, although the large scatter of data and the complexity involved makes for large uncertainties.
Flexible robots have advantages over rigid robots in their ability to conform physically to their environment and to form a wide variety of shapes. Sensing the force applied by or to flexible robots is useful for both navigation and manipulation tasks, but it is challenging due to the need for the sensors to withstand the robots' shape change without encumbering their functionality. Also, for robots with long or large bodies, the number of sensors required to cover the entire surface area of the robot body can be prohibitive due to high cost and complexity. We present a novel soft air pocket force sensor that is highly flexible, lightweight, relatively inexpensive, and easily scalable to various sizes. Our sensor produces a change in internal pressure that is linear with the applied force. We present results of experimental testing of how uncontrollable factors (contact location and contact area) and controllable factors (initial internal pressure, thickness, size, and number of interior seals) affect the sensitivity. We demonstrate our sensor applied to a vine robot-a soft inflatable robot that "grows" from the tip via eversion-and we show that the robot can successfully grow and
To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms
The classical theory of electrodynamics is built upon Maxwell's equations and the concepts of electromagnetic (EM) field, force, energy, and momentum, which are intimately tied together by Poynting's theorem and by the Lorentz force law. Whereas Maxwell's equations relate the fields to their material sources, Poynting's theorem governs the flow of EM energy and its exchange between fields and material media, while the Lorentz law regulates the back-and-forth transfer of momentum between the media and the fields. An alternative force law, first proposed by Einstein and Laub, exists that is consistent with Maxwell's equations and complies with the conservation laws as well as with the requirements of special relativity. While the Lorentz law requires the introduction of hidden energy and hidden momentum in situations where an electric field acts on a magnetized medium, the Einstein-Laub (E-L) formulation of EM force and torque does not invoke hidden entities under such circumstances. Moreover, total force/torque exerted by EM fields on any given object turns out to be independent of whether the density of force/torque is evaluated using the law of Lorentz or that of Einstein and Laub
We determine the strength of the weak nuclear force which holds the lattices of the elementary particles together. We also determine the strength of the strong nuclear force which emanates from the sides of the nuclear lattices. The strong force is the sum of the unsaturated weak forces at the surface of the nuclear lattices. The strong force is then about ten to the power of 6 times stronger than the weak force between two lattice points.
This paper presents two new theories and a new current representation to explain the magnetic force between two filamentary current elements as a result of electric force interactions between current charges. The first theory states that a current has an electric charge relative to its moving observer. The second theory states that the magnetic force is an electric force in origin. The new current representation characterizes a current as equal amounts of positive and negative point charges moving in opposite directions at the speed of light. Previous work regarded electricity and magnetism as different aspects of the same subject. One effort was made by Johnson to unify the origin of electricity and magnetism, but this effort yielded a formula that is unequal to the well-known magnetic force law. The explanation provided for the magnetic force depends on three factors: 1) representing the electric current as charges moving at the speed of light, 2) considering the relative velocity between moving charges, and 3) analyzing the electric field spreading in the space due to the movement of charges inside current elements. The electric origin of the magnetic force is proved by deriving