Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Approximate $\ell_p$ Sensitivities

Recent works in dimensionality reduction for regression tasks have introduced the notion of sensitivity, an estimate of the importance of a specific datapoint in a dataset, offering provable guarantees on the quality of the approximation after removing low-sensitivity datapoints via subsampling. However, fast algorithms for approximating $\ell_p$ sensitivities, which we show is equivalent to approximate $\ell_p$ regression, are known for only the $\ell_2$ setting, in which they are termed leverage scores. In this work, we provide efficient algorithms for approximating $\ell_p$ sensitivities and related summary statistics of a given matrix. In particular, for a given $n \times d$ matrix, we compute $\alpha$-approximation to its $\ell_1$ sensitivities at the cost of $O(n/\alpha)$ sensitivity computations. For estimating the total $\ell_p$ sensitivity (i.e. the sum of $\ell_p$ sensitivities), we provide an algorithm based on importance sampling of $\ell_p$ Lewis weights, which computes a constant factor approximation to the total sensitivity at the cost of roughly $O(\sqrt{d})$ sensitivity computations. Furthermore, we estimate the maximum $\ell_1$ sensitivity, up to a $\sqrt{d}$ factor, using $O(d)$ sensitivity computations. We generalize all these results to $\ell_p$ norms for $p>1$. Lastly, we experimentally show that for a wide class of matrices in real-world datasets, the total sensitivity can be quickly approximated and is significantly smaller than the theoretical prediction, demonstrating that real-world datasets have low intrinsic effective dimensionality.
Improved Topological Preservation in 3D Axon Segmentation and Centerline Detection using Geometric Assessment-driven Topological Smoothing (GATS)

Automated axon tracing via fully supervised learning requires large amounts of 3D brain imagery, which is time consuming and laborious to obtain. It also requires expertise. Thus, there is a need for more efficient segmentation and centerline detection techniques to use in conjunction with automated annotation tools. Topology-preserving methods ensure that segmented components maintain geometric connectivity, which is especially meaningful for applications where volumetric data is used, and these methods often make use of morphological thinning algorithms as the thinned outputs can be useful for both segmentation and centerline detection of curvilinear structures. Current morphological thinning approaches used in conjunction with topology-preserving methods are prone to over-thinning and require manual configuration of hyperparameters. We propose an automated approach for morphological smoothing using geometric assessment of the radius of tubular structures in brain microscopy volumes, and apply average pooling to prevent over-thinning. We use this approach to formulate a loss function, which we call Geo-metric Assessment-driven Topological Smoothing loss, or GATS. Our approach increased segmentation and center-line detection evaluation metrics by 2%-5% across multiple datasets, and improved the Betti error rates by 9%. Our ablation study showed that geometric assessment of tubular structures achieved higher segmentation and centerline detection scores, and using average pooling for morphological smoothing in place of thinning algorithms reduced the Betti errors. We observed increased topological preservation during automated annotation of 3D axons volumes from models trained with GATS.
Efficient Beam Manipulation with Phase Symmetry Operations on Modulated Metasurfaces

Beam manipulation is of paramount importance in wave engineering, enabling diverse beam shapes like pencil beams, flat-top beams, and isoflux beams to cater to various application missions. Among the beams, shaping flat-top and isoflux beams remains challenging with the traditional synthesis approaches that mainly rely on optimization algorithms. Here, we develop modulated metasurfaces to efficiently generate flat-top and isoflux beams from the first principle of field superposition with negligible optimizations, by performing phase symmetry operations. The theoretical analysis not only facilitates the shaping of 1D and 2D flat-top and isoflux beams but also exhibits controllable beamwidths. Experimental validation confirms the efficacy of the phase symmetry operations in generating flat-top beams with adjustable beamwidths. The concept of phase symmetry operations can be extended to other vectorial components, offering potential applications for the manipulation of various wave types such as acoustic waves, water surface waves, and beyond, thereby advancing related applications.
Bounds on tidal charges from gravitational wave ringdown observations

Black hole solutions in the braneworld scenario are predicted to possess a tidal charge parameter, leaving imprints in the quasinormal spectrum. We conduct an extensive computation of such spectrum, and use it to construct a waveform model for the ringdown relaxation regime of binary black hole mergers observed by LIGO and Virgo. Applying a Bayesian time-domain analysis formalism, we analyse a selected dataset from the GWTC-3 LIGO-Virgo-Kagra catalog of binary coalescences, bounding the value of the tidal charge. With our analysis we obtain the first robust constraints on such charges, highlighting the importance of accounting for the previously ignored correlations with the other black hole intrinsic parameters.
Exposing nontrivial flat bands and superconducting pairing in infinite-layer nickelates

Flat bands coupled with magnetism and topological orders near or at the Fermi level are well known to drive exotic correlation physics and unconventional superconductivity. Here, based on first-principles modeling combined with an in-depth symmetry analysis, we reveal the presence of topological flat bands involving low-energy Ni-$3d_{z^2}$ states in the recently discovered superconductor LaNiO$_{2}$. Our analysis demonstrates that LaNiO$_2$ is an Axion insulator with $\mathbb{Z}_{4} = 2$ and that it supports topological crystalline insulating states protected by the glide mirror symmetries. The topological flat bands in LaNiO$_{2}$ are also shown to host odd-parity superconductivity. Our study indicates that the nickelates would provide an interesting materials platform for exploring the interplay of flat bands, topological states, and superconductivity.
The effect of wave blocking on the mapping of sea bottom topography with imaging radar

In 1989 and 1991 the imaging of submarine sand waves off the coast of the Netherlands was investigated with the AIRSAR, an airborne P-, L- and C-band Synthetic Aperture Radar (SAR) operated by NASA/JPL. The sand waves in the test area have an asymmetric slope with their steep slopes oriented towards the northeast. In both experiments the surface current velocity was coming from the northeast. According to existing theory, the steep slopes of the sand waves should be visible as dark bands in the radar images. Only the P-band image of 1989 shows this behavior. The P-band image of 1991 and the L-band images of 1989 and 1991 show saw-tooth shaped modulations. During both experiments the wind was coming from the southwest, so wind and current had opposite directions. Therefore wave blocking or wave reflection is expected to play a role. In this paper it is shown how proper inclusion of the kinematics of the waves can explain, at least qualitatively, the observed modulations. <<ETX>>
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning

Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge researchers to train agents capable of generalizing to tasks, maps, and opponents never seen during training. Neural MMO features procedurally generated maps with 128 agents in the standard setting and support for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold improved performance and compatibility with CleanRL. We release the platform as free and open-source software with comprehensive documentation available at neuralmmo.github. io and an active community Discord. To spark initial research on this new platform, we are concurrently running a competition at NeurIPS 2023.
Terrain Recognition and Contact Force Estimation Through a Sensorized Paw for Legged Robots

This paper introduces the Terrain Recognition And Contact Force Estimation Paw, a compact and sensorized shoe designed for legged robots. The paw end-effector is made of silicon that deforms upon the application of contact forces, while an embedded micro camera is utilized to capture images of the deformed inner surface inside the shoe, and a microphone picks up audio signals. Processed through machine learning techniques, the images are mapped to compute an accurate estimate of the cumulative 3D force vector, while the audio signals are analyzed to identify the terrain class (e.g., gravel, snow). By leveraging its on-edge computation ability, the paw enhances the capabilities of legged robots by providing key information in real-time that can be used to adapt locomotion control strategies. To assess the performance of this novel sensorized paw, we conducted experiments on the data collected through a specially-designed testbed for force estimation, as well as data from recordings of the audio signatures of different terrains interacting with the paw. The results demonstrate the accuracy and effectiveness of the system, highlighting its potential for improving legged robot performance.
Impact of soil aeration on runoff characteristics in dual-purpose no-till wheat systems

Dual-purpose wheat (Triticum aestivum L.) systems are an important practice in the Southern Great Plains of the United States. As no-till receives increasing interest, there are concerns that grazing no-till wheat increases soil compaction and reduces rainfall infiltration. The objective of this study was to evaluate runoff characteristics as impacted by tilling dual-purpose, no-till wheat systems using conventional and varying aeration methods. Tillage treatments included (1) no-till; (2) conventional till; and aeration roller angles of (3) 0°, (4) 5°, and (5) 10°. These treatments were evaluated on two grazing systems: (1) graze out and (2) graze/grain. Rainfall simulators were used to provide four storm events: two after tillage implementation and prior to grazing and two after harvest and grazing season. When runoff events occurred within three weeks of aeration, 5° and 10° roller angles reduced runoff volume, increased infiltration, and reduced nutrient losses compared to no-till. However, six weeks after tillage, no-till runoff volumes were lower than all other treatments except the 10° aeration treatment. Soluble reactive phosphorus (P), total P, and ammonium-nitrogen (NH4-N) loads were significantly reduced by 5° and 10° aeration treatments when runoff events occurred within three weeks of tillage and immediately after fertilizer application. After 10 months of tillage implementation, infiltration was 10% to 52% greater on no-till plots compared to tilled treatments. For summer runoff events, graze out systems increased runoff by as much as 1.5-fold and decreased infiltration by as much as 1.3-fold compared to the graze/grain system. Overall, aeration was most effective in reducing runoff volumes and nutrient losses within three weeks after implementation and provided more positive effects than disking. If aeration is implemented with nutrient applications, nutrient losses could be reduced by more than 4-fold compared to no-till. Aeration may also be beneficial in no-till wheat systems that undergo much more intensive grazing systems than observed in this study. Ultimately, economic considerations must be carefully weighed when considering tillage of no-till, dual-purpose wheat systems.
The Distance to the Stars

The vastness of a clear night sky evokes curiosity about the distance to the stars. There are two primary methods for estimating stellar distances, parallax and luminosity. In this study, we present a new analysis revealing a noteworthy discrepancy between these two methods. Due to the accuracy of GAIA, parallaxes can directly be converted into distances. In contrast, luminosity distances require, apart from the determination of the apparent and absolute brightness of a star, the reddening value that allows a correction for interstellar extinction. Using 47 stars with non-peculiar reddening curves from the high-quality sample we find here that the luminosity distance overestimates the parallactic distance for most (80 %) of these stars. This puzzling discrepancy can only be removed when incorporating a new population of large dust grains, so-called dark dust, with our model that respects contemporary constraints of the inter-stellar dust and is updated to scope for the first time with absolute reddening. The model provides a visual extinction which unifies the conflicting distances. Another far-reaching consequence of the flat absorption and scattering properties of dark dust is that it broadens the light curves of SN Ia, which serves as a measure of the quantity of dark energy.
PINNs-TF2: Fast and User-Friendly Physics-Informed Neural Networks in TensorFlow V2

Physics-informed neural networks (PINNs) have gained prominence for their capability to tackle supervised learning tasks that conform to physical laws, notably nonlinear partial differential equations (PDEs). This paper presents"PINNs-TF2", a Python package built on the TensorFlow V2 framework. It not only accelerates PINNs implementation but also simplifies user interactions by abstracting complex PDE challenges. We underscore the pivotal role of compilers in PINNs, highlighting their ability to boost performance by up to 119x. Across eight diverse examples, our package, integrated with XLA compilers, demonstrated its flexibility and achieved an average speed-up of 18.12 times over TensorFlow V1. Moreover, a real-world case study is implemented to underscore the compilers' potential to handle many trainable parameters and large batch sizes. For community engagement and future enhancements, our package's source code is openly available at: https://github.com/rezaakb/pinns-tf2.
Nonvolatile spin field effect transistor based on VSi2N4/Sc2CO2 multiferroic heterostructure

We report first-principles calculations that propose a nonvolatile spin field effect transistor (spin- FET) based on a van der Waals multiferroic heterostructure, i.e., VSi2N4/Sc2CO2. We find that the inversion of ferroelectric polarization of monolayer Sc2CO2 can efficiently modulate the electronic states of monolayer VSi2N4. A half-metal to half-semiconductor phase translation of VSi2N4 can be efficiently realized, which leads to distinct electronic transport properties. We additionally construct a spin-FET device based on the multiferroic heterostructure. We find that the VSi2 N4 /Sc2 CO2 based Spin-FET has remarkable all-electric-controlled performance. A large on-off current ratio of about 650% induced by the inversion of Sc2CO2 ferroelectric polarization can be obtained under a small bias voltage (0.02 V). We also find an interesting spatially-separated spin-polarized transport phenomenon, with the pure spin-up (spin-down) electrons transporting merely in VSi2N4 (Sc2CO2). Our study provides a promising approach for constructing low-energy-dissipation and nonvolatile FET devices.
SymPhase: Phase Symbolization for Fast Simulation of Stabilizer Circuits

This paper proposes an efficient stabilizer circuit simulation algorithm that only traverses the circuit forward once. We introduce phase symbolization into stabilizer generators, which allows possible Pauli faults in the circuit to be accumulated explicitly as symbolic expressions in the phases of stabilizer generators. This way, the measurement outcomes are also symbolic expressions, and we can sample them by substituting the symbolic variables with concrete values, without traversing the circuit repeatedly. We show how to integrate symbolic phases into the stabilizer tableau and maintain them efficiently using bit-vector encoding. A new data layout of the stabilizer tableau in memory is proposed, which improves the performance of our algorithm (and other stabilizer simulation algorithms based on the stabilizer tableau). We implement our algorithm and data layout in a Julia package named SymPhase.jl, and compare it with Stim, the state-of-the-art simulator, on several benchmarks. We show that SymPhase.jl has superior performance in terms of sampling time, which is crucial for generating a large number of samples for further analysis.
Fabrication of Sawfish photonic crystal cavities in bulk diamond

Color centers in diamonds are quantum systems with optically active spin-states that show long coherence times and are, therefore, a promising candidate for the development of efficient spin–photon interfaces. However, only a small portion of the emitted photons is generated by the coherent optical transition of the zero-phonon line (ZPL), which limits the overall performance of the system. Embedding these emitters in photonic crystal cavities improves the coupling to the ZPL photons and increases their emission rate. Here, we demonstrate the fabrication process of “Sawfish” cavities, a design recently proposed that has the experimentally realistic potential to simultaneously provide a high waveguide coupling efficiency and significantly enhance the emission rate. The presented process allows for the fabrication of fully suspended devices with a total length of 20.5 μm and feature sizes as small as 40 nm. The optical characterization shows fundamental mode resonances that follow the behavior expected from the corresponding design parameters and quality (Q) factors as high as (3800 ± 1200). Finally, we investigate the effects of nanofabrication on the devices and show that, despite a noticeable erosion of the fine features, the measured cavity resonances deviate by only 0.8 (1.2)% from the values estimated by simple inspection via scanning electron microscopy. This proves that the Sawfish design is robust against fabrication imperfections, which makes it an attractive choice for the development of quantum photonic networks.
HyperS2V: A Framework for Structural Representation of Nodes in Hyper Networks

In contrast to regular (simple) networks, hyper networks possess the ability to depict more complex relationships among nodes and store extensive information. Such networks are commonly found in real-world applications, such as in social interactions. Learning embedded representations for nodes involves a process that translates network structures into more simplified spaces, thereby enabling the application of machine learning approaches designed for vector data to be extended to network data. Nevertheless, there remains a need to delve into methods for learning embedded representations that prioritize structural aspects. This research introduces HyperS2V, a node embedding approach that centers on the structural similarity within hyper networks. Initially, we establish the concept of hyper-degrees to capture the structural properties of nodes within hyper networks. Subsequently, a novel function is formulated to measure the structural similarity between different hyper-degree values. Lastly, we generate structural embeddings utilizing a multi-scale random walk framework. Moreover, a series of experiments, both intrinsic and extrinsic, are performed on both toy and real networks. The results underscore the superior performance of HyperS2V in terms of both interpretability and applicability to downstream tasks.
Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach

Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models for automatic extraction of the names of the various constituents of a methodology, e.g., `R-CNN', `ELMo' etc. The main research challenge for this task is effectively modeling the contexts around these methodology component names in a few-shot or even a zero-shot setting. The main contributions of this paper towards effectively identifying new evolving scientific methodology names are as follows: i) we propose a factored approach to sequence modeling, which leverages a broad-level category information of methodology domains, e.g., `NLP', `RL' etc.; ii) to demonstrate the feasibility of our proposed approach of identifying methodology component names under a practical setting of fast evolving AI literature, we conduct experiments following a simulated chronological setup (newer methodologies not seen during the training process); iii) our experiments demonstrate that the factored approach outperforms state-of-the-art baselines by margins of up to 9.257\% for the methodology extraction task with the few-shot setup.
Embedding First Order Logic into Kernel Machines

In this paper we propose a general framework to integrate supervised and unsupervised examples with background knowledge expressed by a collection of first-order logic clauses into kernel machines. In particular, we consider a multi-task learning scheme where multiple predicates defined on a set of objects are to be jointly learned from examples, enforcing a set of FOL constraints on the admissible configurations of their values. The predicates are defined on the feature spaces, in which the input objects are represented, and can be either known a priori or approximated by an appropriate kernel-based learner. A general approach is presented to convert the FOL clauses into a continuous implementation that can deal with the outputs computed by the kernel-based predicates. The learning problem is formulated as a semi-supervised task that requires the optimization in the primal of a loss function that combines a fitting loss measure on the supervised examples, a regularization term, and a penalty term that enforces the constraints on both the supervised and unsupervised examples. Unfortunately, the penalty term is not convex and it can hinder the optimization process. However, it is possible to avoid poor solutions by using a two stage learning schema, in which the supervised examples are learned first and then the constraints are enforced.
More on the spin-2 analogue of the massive BF model

The addition of mass terms in general breaks gauge symmetries which can be recovered usually via Stueckelberg fields. The massive BF model describes massive spin-1 particles while preserving the $U(1)$ symmetry without Stueckelberg fields. Replacing the spin-1 curvature (field strength) by the Riemann tensor one can define its spin-2 analogue (massive``BR'' model). Here we investigate the canonical structure of the free mBR model in terms of gauge invariants in arbitrary dimensions and compare with the massive BF model. We also investigate non linear completions of the mBR model in arbitrary dimensions. In $D=3$ we find a non linear completion in the form of a bimetric model which is a sub case of a new class of bimetric models whose decoupling limit is ghost free at leading order. Their spectrum consists only of massive spin-2 particles. In arbitrary dimensions $D\ge 3$ we show that the consistency of a possible single metric completion of the mBR model is related with the consistency of a higher rank description of massless spin-1 particles in arbitrary backgrounds.