Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Level Set-Based Immersed-Boundary Method for CFD Simulation of Moving-Boundary Problems

A novel level set-based immersed-boundary method (LSIBM) is proposed for a transient computational fluid dynamics (CFD) simulation of flow across a moving immersed boundary. At various time instants, the level set method (LSM) is used to capture the moving boundary accurately, and its value is used for proper numerical implementation of boundary conditions and conservation laws in the fluid region. The method is validated on flow across a pitching airfoil and four different problems on flow across an oscillating cylinder. The boundary conditions on the moving body are applied directly in the discrete equations: The method is more physics-based as well as easier to implement numerically.
Ion‐Beam Deposition of Thin Films of Diamondlike Carbon

An ion‐beam deposition technique has been developed and was used to deposit thin films of insulating carbon on room‐temperature substrates. It was established that the carbon films deposited using this technique are insulating and have the following characteristics similar to that of carbon in the diamond form: (1) transparency, (2) index of refraction greater than 2.0, (3) highly insulating, (4) able to scratch glass, (5) resistant to hydrofluoric acid for long periods of time, (6) at least partially crystalline and with a lattice constant similar to diamond as demonstrated by x‐ray diffraction, (7) dielectric constant between about 8 and 14 (diamond is 16.5). A resistance to sodium ion diffusion has been experimentally demonstrated. Preliminary measurements at a dose of 1 Mrad indicated a radiation resistance from the point of view of stability of leakage resistance and of negligibly small flat band voltage shifts. Several coplanar thin‐film transistors using the insulating carbon gate have been fabricated.
Optical and structural changes of silver nanoparticles during photochromic transformation

Silver nanoparticles embedded in titanium oxide change their color upon irradiation with visible light. Here we investigate the origin of this photochromic effect. The color change is found to result chiefly from a reduction of the optical extinction peak of the photoexcited particle plasmons. From a comparison with x-ray diffraction experiments, we conclude that this reduction is caused by a photoinduced decrease of the mean size of the silver nanocrystals.
Defect states of acoustic waves in a two-dimensional lattice of solid cylinders

Using the plane-wave expansion method, we study the propagation of acoustic waves through two-dimensional (2D) periodic composites consisting of solid cylinders in air. Defect in those structures create localized states inside the band gaps. We study both single and line defects. Line defects can act as a waveguide for acoustic waves while single defects can be used as acoustical filters.
Weighted Evolving Networks with Self-organized Communities

In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of ν ⩾ 1, γ > 2, and α > 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness.
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.
The Chained Difference-in-Differences

This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels. Our estimator accommodates (1) multiple time periods, (2) variation in treatment timing, (3) treatment effect heterogeneity, (4) general missing data patterns, and (5) sample selection on observables. We establish the asymptotic properties of the proposed estimator and discuss identification and efficiency gains in comparison to existing methods. Finally, we illustrate its relevance through (i) numerical simulations, and (ii) an application about the effects of an innovation policy in France.
Deep Synoptic Array science: a 50 Mpc fast radio burst constrains the mass of the Milky Way circumgalactic medium

We present the Deep Synoptic Array (DSA-110) discovery and interferometric localization of the so far non-repeating FRB 20220319D. The FRB originates in a young, rapidly star-forming barred spiral galaxy, IRAS 02044$+$7048, at just 50 Mpc. Although the NE2001 and YMW16 models for the Galactic interstellar-medium (ISM) contribution to the DM of FRB 20220319D exceed its total observed DM, we show that uncertainties in these models accommodate an extragalactic origin for the burst. We derive a conservative upper limit on the DM contributed by the circumgalactic medium (CGM) of the Milky Way: the limit is either 28.7 pc cm$^{-3}$ and 47.3 pc cm$^{-3}$, depending on which of two pulsars nearby on the sky to FRB 20220319D is used to estimate the ISM DM. These limits both imply that the total Galactic CGM mass is $<10^{11}M_{\odot}$, and that the baryonic mass of the Milky Way is $\lesssim60\%$ of the cosmological average given the total halo mass. More stringent albeit less conservative constraints are possible when the DMs of pulsars in the distant globular cluster M53 are additionally considered. Although our constraints are sensitive to possible anisotropy in the CGM and to the assumed form of the radial-density profile, they are not subject to uncertainties in the chemical and thermal properties of the CGM. Our results strongly support scenarios commonly predicted by galaxy-formation simulations wherein feedback processes expel baryonic matter from the halos of galaxies like the Milky Way.
Language Models are Drummers: Drum Composition with Natural Language Pre-Training

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.
The minimal projective bundle dimension and toric $2$-Fano manifolds

Motivated by the problem of classifying toric $2$-Fano manifolds, we introduce a new invariant for smooth projective toric varieties, the minimal projective bundle dimension. This invariant $m(X)\in\{1, \dots,\dim(X)\}$ captures the minimal degree of a dominating family of rational curves on $X$ or, equivalently, the minimal length of a centrally symmetric primitive relation for the fan of $X$. We classify smooth projective toric varieties with $m(X)\geq \dim(X)-2$, and show that projective spaces are the only $2$-Fano manifolds among smooth projective toric varieties with $m(X)\in\{1, \dim(X)-2,\dim(X)-1,\dim(X)\}$.
Faster Approximate Dynamic Programming by Freezing Slow States

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move “fast” (and in a sense, are more influential) while other parts transition more “slowly. ” Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm’s revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of “freezing” the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic. 1 ar X iv :2 30 1. 00 92 2v 1 [ cs . A I] 3 J an 2 02 3
BS3D: Building-scale 3D Reconstruction from RGB-D Images

Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms. Compared to similar systems, we utilize raw depth maps for odometry computation and loop closure refinement which results in better reconstructions. We acquire a building-scale 3D dataset (BS3D) and demonstrate its value by training an improved monocular depth estimation model. As a unique experiment, we benchmark visual-inertial odometry methods using both color and active infrared images.
Invalidator: Automated Patch Correctness Assessment Via Semantic and Syntactic Reasoning

Automated program repair (APR) faces the challenge of test overfitting, where generated patches pass validation tests but fail to generalize. Existing methods for patch assessment involve generating new tests or manual inspection, which can be time-consuming or biased. In this paper, we propose a novel technique, Invalidator, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. Invalidator leverages program invariants to reason about program semantics while also capturing program syntax through language semantics learned from a large code corpus using a pre-trained language model. Given a buggy program and the developer-patched program, Invalidator infers likely invariants on both programs. Then, Invalidator determines that an APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains erroneous behaviors from the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, Invalidator utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of Invalidator is threefold. First, Invalidator leverages both semantic and syntactic reasoning to enhance its discriminative capability. Second, Invalidator does not require new test cases to be generated, but instead only relies on the current test suite and uses invariant inference to generalize program behaviors. Third, Invalidator is fully automated. Experimental results demonstrate that Invalidator outperforms existing methods in terms of Accuracy and F-measure, correctly identifying 79% of overfitting patches and detecting 23% more overfitting patches than the best baseline.
Optimal convergence rates in stochastic homogenization in a balanced random environment

We consider random walks in a uniformly elliptic, balanced, i.i. d. random environment in the integer lattice $Z^d$ for $d\geq 2$ and the corresponding problem of stochastic homogenization of non-divergence form difference operators. We first derive a quantitative law of large numbers for the invariant measure, which is nearly optimal. A mixing property of the field of the invariant measure is then achieved. We next obtain rates of convergence for the homogenization of the Dirichlet problem for non-divergence form operators, which are generically optimal for $d\geq 3$ and nearly optimal when $d=2$. Furthermore, we establish the existence, stationarity and uniqueness properties of the corrector problem for all dimensions $d\ge 2$. Afterwards, we quantify the ergodicity of the environmental process for both the continuous-time and discrete-time random walks, and as a consequence, we get explicit convergence rates for the quenched central limit theorem of the balanced random walk.
Timescales: the choreography of classical and unconventional computing

Tasks that one wishes to have done by a computer often come with an assortment of conditions that relate to timescales in one way or the other. For instance, the processing must terminate within a given time limit; or a signal processing computer must integrate input information across several timescales; or a robot motor controller must react to sensor signals with a short latency. For classical digital computing machines such requirements pose no fundamental problems as long as the physical clock rate is fast enough for the fastest relevant timescales. However, when digital microchips become scaled down into the few-nanometer range where quantum noise and device mismatch become unavoidable, or when the digital computing paradigm is altogether abandoned in analog neuromorphic systems or other unconventional hardware bases, temporal task conditions are more difficult to relate to the physical hardware dynamics, and instructive literature is scarce. — Here we explore the relations between task-defined timescale conditions and physical hardware timescales in some depth and breadth. The article has two main parts. In the first part we develop an abstract model of a generic computational system that admits a unified discussion of various kinds of computational timescales. This model is general enough to cover digital computing systems as well as analog neuromorphic or other “unconventional” ones. We identify four major types of “timescales” which require separate considerations: causal physical timescales which are formally expressed by time constants; timescales of phenomenal change which characterize the “speed” of how something changes in time; timescales of reactivity which describe how fast a computing system can react to incoming trigger information; and memory timescales. In the second part we survey twenty known computational mechanisms that can be used to obtain desired taskrelated timescale characteristics from the physical givens of the available hardware. This report is a substantially revised and extended version of a deliverable written for the EU H2020 project “MemScales” (grant number 871371 https: / / memscales .eu ) corresponding author 1 ar X iv :2 30 1. 00 89 3v 2 [ cs . E T ] 1 8 Ja n 20 23
4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions

In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://www.4seasons-dataset.com/.
The Spitzer–HETDEX Exploratory Large-Area Survey. IV. Model-based Multiwavelength Photometric Catalog

We present a 0.3–4.5 μm 16-band photometric catalog for the Spitzer/HETDEX Exploratory Large-Area (SHELA) survey. SHELA covers an ∼27 deg2 field within the footprint of the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). Here we present new DECam imaging and an rizK s band–selected catalog of four million sources extracted using a fully model-based approach. We validate our photometry by comparing with the model-based DECam Legacy Survey. We analyze the differences between model-based and aperture photometry by comparing with the previous SHELA catalog, finding that our model-based photometry can measure point sources to fainter fluxes and better capture the full emission of resolved sources. The catalog is 80% (50%) complete at riz ∼ 24.7 (25.1) AB mag, and the optical photometry reaches a 5σ depth of ∼25.5 AB mag. We measure photometric redshifts and achieve a 1σ scatter of Δz/(1 + z) of 0.04 with available spectroscopic redshifts at 0 ≤ z ≤ 1. This large-area, multiwavelength photometric catalog, combined with spectroscopic information from HETDEX, will enable a wide range of extragalactic science investigations.
An 840 GHz Wideband Instrument for Snow Measurements

This new start Instrument Incubator Program (IIP) will build and test a wideband instrument (8-40 GHz) in support of the Tier III Snow and Cold Land Processes (SCLP) mission as defined by the Decadal Survey. Harris Corporation will team with engineers from NASA Goddard/Glenn, Nuvotronics, and scientists from multiple Universities for this effort. Multiple instruments will be required to achieve the baseline SCLP mission goals using conventional technology. The capability to perform multiple NASA missions in a single instrument will be achieved by combining a wideband aperture with a software reconfigurable payload capable of performing multiple functions. The broad bandwidth of this instrument allows flexibility in the number of frequencies used to measure Snow Water Equivalent (SWE), a primary goal of SCLP. Potential improvements in the estimation of SWE and its spatial/temporal variability have significant implications for hydrologic modeling and water resources management on a global scale. The wideband approach can also mitigate RFI by selecting non-interfering channels. The innovative manufacturing method for the wideband antenna will reduce the size and weight of the payload, add additional functionality, and allow cost/power to remain relatively unchanged. The entry technology (8-40 GHz feed) is currently at TRL 3 for this application. We plan to bring the wideband feed/reconfigurable radar/radiometry payload, to an exit TRL of 6 for airborne applications. We will demonstrate the wideband feed and payload in both a ground and airborne demonstration during a 30 month period of performance. The first year demonstrates the compatibility of an existing wideband feed (2-18 GHz) with multiple existing radars to measure SWE of documented snow. In the second year, a wideband (8-40 GHz) passive array will be fabricated and integrated with a reconfigurable payload (SAR/radiometer). During the third year, flight tests on the NASA P3 and data reduction with new algorithms will demonstrate the science benefits of a wideband feed with reconfigurable payload.
Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method.