Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Continual Human Motion in Diverse 3D Scenes

We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method generates a plausible motion sequence starting from the seed motion while satisfying the constraints imposed by the provided keypoints. We decompose the continual motion synthesis problem into walking along paths and transitioning in and out of the actions specified by the keypoints, which enables long generation of motions that satisfy scene constraints without explicitly incorporating scene information. Our method is trained only using scene agnostic mocap data. As a result, our approach is deployable across 3D scenes with various geometries. For achieving plausible continual motion synthesis without drift, our key contribution is to generate motion in a goal-centric canonical coordinate frame where the next immediate target is situated at the origin. Our model can generate long sequences of diverse actions such as grabbing, sitting and leaning chained together in arbitrary order, demonstrated on scenes of varying geometry: HPS, Replica, Matterport, ScanNet and scenes represented using NeRFs. Several experiments demonstrate that our method outperforms existing methods that navigate paths in 3D scenes.
Direct data-driven signal temporal logic control of linear systems

Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task for complex systems. In this paper, we develop a direct data-driven controller synthesis method for linear systems subject to a temporal logic specification, which does not require this explicit modeling step. After collecting a single sequence of input-output data from the system, we construct a data-driven characterization of the system behavior. Using this data-driven characterization we show that we can synthesize a controller, such that the controlled system satisfies a signal temporal logic-based specification. The underlying optimization problem is solved by mixed-integer linear programming. We demonstrate applicability of the results through benchmark simulation examples.
Multiphoton blockade and antibunching in an optical cavity coupled with dipole-dipole-interacting 
Λ
-type atoms

We study multiphoton blockade effects in a single-mode cavity interacting with two three-level atoms in $\Lambda$-configuration having position-dependent atom-field coupling. We consider the effects of dipole-dipole interaction (DDI) between the three-level atoms and show how the presence of DDI strongly influences the multiphoton blockade. For symmetric coupling of the atoms with the field, the DDI induces an asymmetry in the emission spectra as a function of pump field detuning. At positive detuning, the single-photon blockade gets stronger as a function of DDI strength, leading to photon antibunching. However, it becomes weaker at negative detuning and can also completely vanish. We show that this vanishing single-photon blockade is associated with a strong two-photon blockade, leading to two-photon bunching. Therefore, by just tuning the frequency of the pump field, we can achieve two very distinct features. We also study the effects of DDI when the atoms are asymmetrically coupled with the field and show that the proposed system exhibits two-photon bunching. We believe our results are important for the experimental realization of such systems where DDI may be present.
Self-Supervised Siamese Autoencoders

Fully supervised models often require large amounts of labeled training data, which tends to be costly and hard to acquire. In contrast, self-supervised representation learning reduces the amount of labeled data needed for achieving the same or even higher downstream performance. The goal is to pre-train deep neural networks on a self-supervised task such that afterwards the networks are able to extract meaningful features from raw input data. These features are then used as inputs in downstream tasks, such as image classification. Previously, autoencoders and Siamese networks such as SimSiam have been successfully employed in those tasks. Yet, challenges remain, such as matching characteristics of the features (e.g., level of detail) to the given task and data set. In this paper, we present a new self-supervised method that combines the benefits of Siamese architectures and denoising autoencoders. We show that our model, called SidAE (Siamese denoising autoencoder), outperforms two self-supervised baselines across multiple data sets, settings, and scenarios. Crucially, this includes conditions in which only a small amount of labeled data is available.
Exploring velocity-spreading factor and consequences through dynamic ray-tracing in general anisotropic media: A comprehensive tutorial

In seismic imaging, understanding the relationship between wavefront-propagation velocity and time-interval velocity is crucial for achieving optimal resolution. However, this task becomes even more challenging when considering anisotropic situations. To accurately account for the influence of anisotropy on wavefronts, it is essential to have a solid grasp of the underlying physics. Unfortunately, the anisotropy model that best describes the medium is often unknown. To address this issue, we utilize paraxial-ray theory in a ray-centered coordinate system to study the wavefront phenomenon. This approach allows us to develop explicit expressions that describe the physics of the problem. Using this theoretical framework, we can accurately generalize the relationship between time-migration rays and Dix velocity by incorporating the velocity-spreading factor for general anisotropic media. This factor lets us determine the type of anisotropy present in the medium. Moreover, the velocity-spreading factor provides valuable information for various applications, including model building, time-imaging, and time-to-depth conversion. Overall, the presented theoretical framework offers a comprehensive understanding of wavefront propagation in anisotropic media, which can aid in improving the knowledge of the phenomena that form seismic images.
Large $ Y_{3,2} $-tilings in 3-uniform hypergraphs

Let $Y_{3,2}$ be the 3-graph with two edges intersecting in two vertices. We prove that every 3-graph $ H $ on $ n $ vertices with at least $ \max \left \{ \binom{4\alpha n}{3}, \binom{n}{3}-\binom{n-\alpha n}{3} \right \}+o(n^3) $ edges contains a $Y_{3,2}$-tiling covering more than $ 4\alpha n$ vertices, for sufficiently large $ n $ and $0<\alpha<1/4$. The bound on the number of edges is asymptotically best possible and solves a conjecture of the authors for 3-graphs that generalizes the Matching Conjecture of Erd\H{o}s.
Investigation into triggered star formation by radiative driven implosion

When massive stars form, they emit strong, hydrogen ionising radiation fields into their molecular cloud environment, forming HII regions. This is believed to be capable of inducing effects which can trigger further star formation through a process known as Radiative Driven Implosion. Hydrodynamic shock fronts are generated at the interface between ionised and un-ionised material. These shocks propagate into the clouds, and their motion and increase in density can result in the conditions required for star formation. Using the method of Smoothed Particle Hydrodynamics, the effect of varied initial geometrical and physical properties of a molecular cloud on the prospect of radiation triggered star formation is investigated over a large parameter space. The physical processes of the model include a detailed ray-tracing implementation of the ionising radiation, along with a thermodynamic model and chemical evolution for multiple species of atoms. A parameter d_euv, defined as the ratio of the initial ionising penetration depth to the scale length of the cloud along the radiation axis, was found to be an effective indicator of the final evolutionary prospects of the molecular clouds investigated. Low d_euv clouds typically exhibit shock front motion which converges on a focus or foci, and form symmetric or asymmetric B or C type Bright Rimmed Clouds depending on orientation. At medium d_euv there is a mixture of focus/foci convergent and linear or filamentary structure formation with cores formed indirectly, after disruption of material by the shock fronts. At high d_euv only fragment-core and irregular structures form, with the clouds being increasingly dominated by photoevaporation. At extremely high d_euv cores cannot form and the cloud will photoevaporate. In addition, qualitative impressions of the scope of structure morphologies, especially those for irregular morphologies, is compiled. Of note, it is found that the simple initial conditions of a uniform prolate cloud at inclinations to incident radiation are capable of producing a wide variety of the structures observed at HII boundaries.
Contemporaneous Observations of Hα Luminosities and Photometric Amplitudes for M Dwarfs

While many M dwarfs are known to have strong magnetic fields and high levels of magnetic activity, we are still unsure about the properties of their starspots and the origin of their magnetic dynamos. Both starspots and chromospheric heating are generated by the surface magnetic field; they produce photometric variability and Hα emission, respectively. Connecting brightness variations to magnetic activity therefore provides a means to examine M-dwarf magnetism. We survey 30 M dwarfs previously identified as fast rotating stars (P rot < 10 days). We present time-series optical photometry from the Transiting Exoplanet Survey Satellite (TESS) and contemporaneous optical spectra obtained using the Ohio State Multi-Object Spectrograph (OSMOS) on the 2.4 m Hiltner telescope at MDM Observatory in Arizona. We measure rotation periods and photometric amplitudes from TESS light curves using Gaussian Processes. From the OSMOS spectra, we calculate the equivalent width of Hα, and L Hα /L bol. We find a weak positive correlation between Hα luminosity and the semiamplitude, R var ( p=0.005−0.005+0.075 ). We also observe short-term variability (between 20 and 45 minutes) in Hα equivalent widths and possible enhancement from flares consistent to recent literature.
A proof that Reed-Muller codes achieve Shannon capacity on symmetric channels

In 1948, Shannon used a probabilistic argument to show that there exist codes achieving a maximal rate defined by the channel capacity. In 1954, Muller and Reed introduced a simple deterministic code construction, conjectured shortly after to achieve channel capacity. Major progress was made towards establishing this conjecture over the last decades, with various branches of discrete mathematics involved such as combinatorial bounds, sharp thresholds, hypercontractivity, additive combinatorics and polarization theory. In particular, the special case of the erasure channel was settled by Kudekar at al., relying on Bourgain-Kalai’s sharp threshold theorem for symmetric monotone properties. The main case of error channels remained however unsettled, due in particular to the property being non-monotone and the lack of techniques to obtain fast local error decay up to capacity, despite the notable vanishing bound on the local error from Reeves-Pfister. This paper closes the conjecture’s proof. The main ingredient is a new recursive boosting framework for coding, where codewords are decoded by aggregating restrictions on a ‘subspace-sunflower’ structure, analogous to the structure from Erdős-Rado 1960. The dependencies between the sunflower petals are handled with an $L_{2}$ and $L_{4}$ Boolean Fourier analysis, and a list-decoding argument with a weight enumerator bound from Sberlo-Shpilka is used to control the global error from the local one. For the local error, while monotonicity does not apply, we show that a ‘weak threshold’ result still holds using solely symmetries. This gives in particular a shortened and tightened argument for the vanishing local error result, and with prior works, it also implies the strong wire-tap secrecy of RM codes on pure-state classical-quantum channels.
Autonomous Hook-Based Grasping and Transportation with Quadcopters

Payload grasping and transportation with quadcopters is an active research area that has rapidly developed over the last decade. To grasp a payload without human interaction, most state-of-the-art approaches apply robotic arms that are attached to the quadcopter body. However, due to the large weight and power consumption of these aerial manipulators, their agility and flight time are limited. This paper proposes a motion control and planning method for transportation with a lightweight, passive manipulator structure that consists of a hook attached to a quadrotor using a 1 DoF revolute joint. To perform payload grasping, transportation, and release, first, time-optimal reference trajectories are designed through specific waypoints to ensure the fast and reliable execution of the tasks. Then, a two-stage motion control approach is developed based on a robust geometric controller for precise and reliable reference tracking and a linear--quadratic payload regulator for rapid setpoint stabilization of the payload swing. Furthermore, stability of the closed-loop system is mathematically proven to give safety guarantee for its operation. The proposed control architecture and design are evaluated in a high-fidelity physical simulator, and also in real flight experiments, using a custom-made quadrotor--hook manipulator platform.
What Affects Learned Equivariance in Deep Image Recognition Models?

Equivariance w.r. t. geometric transformations in neural networks improves data efficiency, parameter efficiency and robustness to out-of-domain perspective shifts. When equivariance is not designed into a neural network, the network can still learn equivariant functions from the data. We quantify this learned equivariance, by proposing an improved measure for equivariance. We find evidence for a correlation between learned translation equivariance and validation accuracy on ImageNet. We therefore investigate what can increase the learned equivariance in neural networks, and find that data augmentation, reduced model capacity and inductive bias in the form of convolutions induce higher learned equivariance in neural networks.
Inferring nonlinear fractional diffusion processes from single trajectories

We present a method to infer the arbitrary space-dependent drift and diffusion of a nonlinear stochastic model driven by multiplicative fractional Gaussian noise from a single trajectory. Our method, fractional Onsager-Machlup optimisation (fOMo), introduces a maximum likelihood estimator by minimising a field-theoretic action which we construct from the observed time series. We successfully test fOMo for a wide range of Hurst exponents using artificial data with strong nonlinearities, and apply it to a data set of daily mean temperatures. We further highlight the significant systematic estimation errors when ignoring non-Markovianity, underlining the need for nonlinear fractional inference methods when studying real-world long-range (anti-)correlated systems.
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules

Charts are a powerful tool for visually conveying complex data, but their comprehension poses a challenge due to the diverse chart types and intricate components. Existing chart comprehension methods suffer from either heuristic rules or an over-reliance on OCR systems, resulting in suboptimal performance. To address these issues, we present ChartReader, a unified framework that seamlessly integrates chart derendering and comprehension tasks. Our approach includes a transformer-based chart component detection module and an extended pre-trained vision-language model for chart-to-X tasks. By learning the rules of charts automatically from annotated datasets, our approach eliminates the need for manual rule-making, reducing effort and enhancing accuracy. We also introduce a data variable replacement technique and extend the input and position embeddings of the pre-trained model for cross-task training. We evaluate ChartReader on Chart-to-Table, ChartQA, and Chart-to-Text tasks, demonstrating its superiority over existing methods. Our proposed framework can significantly reduce the manual effort involved in chart analysis, providing a step towards a universal chart understanding model. Moreover, our approach offers opportunities for plug-and-play integration with mainstream LLMs such as T5 and TaPas, extending their capability to chart comprehension tasks.1
Measuring the Students' Ability among Engineering Technology Student: A Preliminary Investigation

Summary statistics of person and item. Fitting Items: Based on the distribution of the person, in this case the students, and the items, those students located above the mean person of -0.21 logit measures can be grouped as mediocre performer students. The mean person is indicated by the point -0.21 and the „M‟ sign on the left side of the Wright map. These groups of student are located on the upper half portion of the logit ruler. There are 53 students in this group, about 0.42% students having their ability logit measures above -0.21 logit. The average performer students finds 24%, eight (8) from the total of 34 items, are easy to be accomplished, since those items are located below their ability logit measures. However, looking back at the detail logit measures for each of the items in item measure table as in Table 2, shows that there are few items appears to be very difficult for this group of students. The items are those located at the upper end portion of the Wright map from Figure 1.
The CAMELS Project: Expanding the Galaxy Formation Model Space with New ASTRID and 28-parameter TNG and SIMBA Suites

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω m , σ 8, Ω b ) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.
Low latency computing for time stretch instruments

Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.
Using elliptical galaxy kinematics to compare the strength of gravity in cosmological regions of differing gravitational potential– a first look

Various models of modified gravity invoke ‘screening’ mechanisms that are sensitive to the value of the local gravitational potential. This could have observable consequences for galaxies. These consequences might be seen by comparing two proxies for galaxy mass – their luminosity and their internal kinematics – as a function of local galaxy density. Motivated by this prospect, we have compared the observed properties of luminous red galaxies (LRGs) inside and outside of voids in the cosmic large scale structure. We used archival measurements of line widths, luminosities, redshifts, colors, and positions of galaxies in conjunction with recent void catalogs to construct comparison LRG samples inside and outside of voids. We fitted these two samples to the well-established fundamental plane of elliptical galaxies to constrain any differences between the inferred value of the Newtonian gravitational constant G for the two samples. We obtained a null result, with an upper limit on any fractional difference in G within and outside of cosmological voids to be α = δG/G ∼ 40%. This upper bound is dominated by the small-number statistics of our N ∼ 100 within-void LRG sample. With the caveat that environmental effects could influence various parameters such as star formation, we estimate that a 1% statistical limit on α could be attained with data from 105 elliptical galaxies within voids. This is within the reach of future photometric and spectroscopic surveys, both of which are required to pursue this method.
Ericson: An Interactive Open-Domain Conversational Search Agent

Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art question answering and information retrieval components, as well as intent inference and dialogue management models for proactive question refinement and recommendations. Our system was stress-tested in the Amazon Alexa Prize, by engaging in live conversations with thousands of Alexa users, thus providing empirical basis for the analysis of the ODCS system in real settings. Our interaction data analysis revealed that accurate intent classification, encouraging user engagement, and careful proactive recommendations contribute most to the users satisfaction. Our study further identifies limitations of the existing search techniques, and can serve as a building block for the next generation of ODCS agents.