Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesoscale and Convective-Scale Characteristics of Mature Hurricanes. Part II. Inner Core Structure of Hurricane Allen (1980)

Abstract The eyewall structure of Hurricane Alien is examined from analyses of multiple aircraft data on two days, 5 and 8 August 1980. These data sets are unique in that, for the first time, three instrumented aircraft executed coordinated radial penetrations of the eyewall at multiple levels. The data collected on 5 August illustrate the persistence of various features on horizontal scales > 10 km over several hours. Composite cross sections constructed from the 8 August data show similar structure, although the eye diameter had decreased to less than half that of 5 August. The convergence of air in the eyewall was highly two-dimensional. This convergence supported organized ascent that was along the inner edge of the high reflectivity region and displaced inward several kilometers from the radius of maximum wind (RMW). A mean eyewall updraft of 5–6 m s−1 is computed from integration of the two-dimensional continuity equation. Embedded within the two-dimensional eyewall were cores of high reflectivity t.
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration

The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.
Optical coherence tomography -- principles, implementation, and applications in ophthalmology

This short textbook was written for someone who newly start studying, doing research, or development about optical coherence tomography (OCT) or using OCT. The first chapter summarizes the concept and the history of OCT. In the second chapter, the technologies of OCT are summarized, which includes the mathematical principle, hardware implementation, the instructions for designs such as interferometer and wavelength selections, and theories of resolutions and sensitivity. In the third chapter, some examples of ophthalmic applications of OCT are described. In the last chapter, instructions for next learning steps are given.
Nd loaded liquid scintillator to search for Nd-150 neutrinoless double beta decay

The 150Nd is considered one of the most attractive candidate for searching neutrinoless double beta (0νββ-) decay, thanks to its high Q-value (3.367 MeV), that makes the external background issue less significative respect to other isotopes, and favorable computed matrix elements. The isotopic abundance of this isotope in natural neodimium is only 5.6% and up to now, it has been investigated only in low mass experiments. The next step is to increase the sensitivity of the experiments using larger mass of neodymium (10 ton-1 kton). This could be possible with a Nd loaded liquid scintillator (LS). At the Gran Sasso National Laboratory (LNGS), a joint INFN (Istituto Nazionale di Fisica Nucleare) and INR (Institute for Nuclear Research of Moscow) working group has been carrying out since 2001 an R&D activity aiming to develop organic liquid scintillators (LS) doped with metals. The achieved know-how and the satisfactory results obtained both with In and Gd allowed to face the development and production of Nd doped LS. The development of metal doped LS is challenging because the metal has to be embedded in a proper organic system that makes it soluble in an organic solvent minimizing the impact of the metal-organic compound on the optical and scintillation properties of the LS. A further challenge in the case of Nd is the presence of absorption bands of this element in the optical region with a transparent region around 400 nm, which is about at the maximum of the scintillator emission spectrum. A 2.5 1 Nd loaded LS has been produced diluting an originally developed Nd-Carboxylic (Nd-CBX) salt in pseudocumene (PC), the solvent of the Borexino liquid scintillator. The measured light yield, at [Nd] = 6.5 g/1 and [PPO] = 1.5 g/1, is ~ 75% of pure PC at the same fluor concentration (~ 10000 ph/MeV). The Nd doped LS has been tested in a 2 1 quartz cell (wrapped by VM2000 reflector film) having dimensions 5x5x100 cm3. The light propagates in the cell by total internal reflection at the external quartz/air interface and is reflected by the VM2000 foil. The light is trasmitted at the windows of the cell where 3-inch PMTs (one for each end) are coupled. Two fillings have been performed: distilled PC (PPO-1.5 g/1) and Nd loaded LS (Nd-6.5 g/1, PPO-1.5 g/1, MSB-30 mg/1). In both cases, the cell is irradiated with a 137Cs source, scanning different positions along the cell axis. For each position the Compton Edge (CE) in the spectrum has been fitted. The optical attenuation length measured with this methodic is: 210±10 cm (only stat error, systematics not yet investigated). The measurements performed by ICP-MS of the radiopurity of the Nd2O3, that is the compound from which the Nd-CBX salt is synthesized, and of the content of optically active impurities show that a purification procedure is needed. Further investigations, at the ppt level, on the Th content in Nd2O3 is planned with the Borexino CTF.
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing

We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classification accuracy on indistinguishable ambiguous data. Firstly, we visualize high-dimensional data features through dimensionality reduction methods and design an interactive system \textit{SpaceEditing} to display the visualized data. \textit{SpaceEditing} provides a 2D workspace based on the idea of spatial layout. In this workspace, the user can move the projection data in it according to the system guidance. Then, \textit{SpaceEditing} will find the corresponding high-dimensional features according to the projection data moved by the user, and feed the high-dimensional features back to the network for retraining, therefore achieving the purpose of interactively modifying the high-dimensional latent space for the user. Secondly, to more rationally incorporate human knowledge into the training process of neural networks, we design a new loss function that enables the network to learn user-modified information. Finally, We demonstrate how \textit{SpaceEditing} meets user needs through three case studies while evaluating our proposed new method, and the results confirm the effectiveness of our method.
UNCOVERing the extended strong lensing structures of Abell 2744 with the deepest JWST imaging

We present a new parametric lens model for the massive galaxy cluster Abell 2744 based on new ultra-deep JWST imaging taken in the framework of the UNCOVER program. These observations constitute the deepest JWST images of a lensing cluster to date, adding to existing deep Hubble Space Telescope (HST) images and the recent JWST ERS and DDT data taken for this field. The wide field-of-view of UNCOVER (∼45 arcmin2) extends beyond the cluster’s well-studied central core and reveals a spectacular wealth of prominent lensed features around two massive cluster sub-structures in the north and north-west, where no multiple images were previously known. We identify 75 new multiple images and candidates of 17 sources, 43 of which allow us, for the first time, to constrain the lensing properties and total mass distribution around these extended cluster structures using strong lensing (SL). Our model yields an effective Einstein radius of θE, main = 23.2″ ± 2.3″ for the main cluster core (for zs = 2), enclosing a mass of M( < θE, main) = (7.7 ± 1.1) × 1013 M⊙, and θE, NW = 13.1″ ± 1.3″ for the newly discovered north-western SL structure enclosing M( < θE, NW) = (2.2 ± 0.3) × 1013 M⊙. The northern clump is somewhat less massive with θE, N = 7.4″ ± 0.7″ enclosing M( < θE, N) = (0.8 ± 0.1) × 1013 M⊙. We find the northern sub-structures of Abell 2744 to broadly agree with the findings from weak lensing (WL) analyses and align with the filamentary structure found by these previous studies. Our model in particular reveals a large area of high magnification values between the various cluster structures, which will be paramount for lensed galaxy studies in the UNCOVER field. The model is made publicly available to accompany the first UNCOVER data release.
Power Allocation for Uplink Communications of Massive Cellular-Connected UAVs

Cellular-connected unmanned aerial vehicle (UAV) has attracted a surge of research interest in both academia and industry. To support aerial user equipment (UEs) in the existing cellular networks, one promising approach is to assign a portion of the system bandwidth exclusively to the UAV-UEs. This is especially favorable for use cases where a large number of UAV-UEs are exploited, e.g., for package delivery close to a warehouse. Although the nearly line-of-sight (LoS) channels can result in higher powers received, UAVs can in turn cause severe interference to each other in the same frequency band. In this contribution, we focus on the uplink communications of massive cellular-connected UAVs. Different power allocation algorithms are proposed to either maximize the minimal spectrum efficiency (SE) or maximize the overall SE to cope with the severe interference based on the successive convex approximation (SCA) principle. One of the challenges is that a UAV can affect a large area meaning that many more UAV-UEs must be considered in the optimization problem, which is essentially different from that for terrestrial UEs. The necessity of single-carrier uplink transmission further complicates the problem. Nevertheless, we find that the special property of large coherent bandwidths and coherent times of the propagation channels can be leveraged. The performances of the proposed algorithms are evaluated via extensive simulations in the full-buffer transmission mode and bursty-traffic mode. Results show that the proposed algorithms can effectively enhance the uplink SEs. This work can be considered the first attempt to deal with the interference among massive cellular-connected UAV-UEs with optimized power allocations.
SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries

Many modern software systems are enabled by deep learning libraries such as TensorFlow and PyTorch. As deep learning is now prevalent, the security of deep learning libraries is a key concern. Fuzzing deep learning libraries presents two challenges. Firstly, to reach the functionality of the libraries, fuzzers have to use inputs from the valid input domain of each API function, which may be unknown. Secondly, many inputs are redundant. Randomly sampled invalid inputs are likely not to trigger new behaviors. While existing approaches partially address the first challenge, they overlook the second challenge. We propose SkipFuzz, an approach for fuzzing deep learning libraries. To generate valid inputs, SkipFuzz learns the input constraints of each API function using active learning. By using information gained during fuzzing, SkipFuzz infers a model of the input constraints, and, thus, generate valid inputs. SkipFuzz comprises an active learner which queries a test executor to obtain feedback for inference. After constructing hypotheses, the active learner poses queries and refines the hypotheses using the feedback from the test executor, which indicates if the library accepts or rejects an input, i.e., if it satisfies the input constraints or not. Inputs from different categories are used to invoke the library to check if a set of inputs satisfies a function's input constraints. Inputs in one category are distinguished from other categories by possible input constraints they would satisfy, e.g. they are tensors of a certain shape. As such, SkipFuzz is able to refine its hypothesis by eliminating possible candidates of the input constraints. This active learning-based approach addresses the challenge of redundant inputs. Using SkipFuzz, we have found and reported 43 crashes. 28 of them have been confirmed, with 13 unique CVEs assigned.
Collapse of Rotating Massive Stars Leading to Black Hole Formation and Energetic Supernovae

We explore a possible explosion scenario resulting from core collapses of rotating massive stars that leave a black hole by performing radiation-viscous-hydrodynamics simulations in numerical relativity. We take moderately and rapidly rotating compact pre-collapse stellar models with zero-age main-sequence masses of 9M ⊙ and 20M ⊙ based on stellar evolution calculations as the initial conditions. We find that viscous heating in the disk formed around the central black hole is the power source for an outflow. The moderately rotating models predict a small ejecta mass of the order of 0.1M ⊙ and an explosion energy of ≲1051 erg. Due to the small ejecta mass, these models may predict a short-timescale transient with a rise time of 3–5 days. This can lead to a bright (∼1044 erg s−1) transient, like superluminous supernovae in the presence of a dense massive circumstellar medium. For hypothetically rapidly rotating models that have a high mass-infall rate onto the disk, the explosion energy is ≳3 × 1051 erg, which is comparable to or larger than that of typical stripped-envelope supernovae, indicating that a fraction of such supernovae may be explosions powered by black hole accretion disks. The explosion energy is still increasing at the end of the simulations with a rate of >1050 erg s−1, and thus, it may reach ∼1052 erg. A nucleosynthesis calculation shows that the mass of 56Ni amounts to ≳0.1M ⊙, which, together with the high explosion energy, may satisfy the required amount for broad-lined type Ic supernovae. Irrespective of the models, the lowest value of the electron fraction of the ejecta is ≳0.4; thus, synthesis of heavy r-process elements is not found in our models.
The Pan-STARRS1 z > 5.6 Quasar Survey. III. The z ≈ 6 Quasar Luminosity Function

We present the z ≈ 6 type-1 quasar luminosity function (QLF), based on the Pan-STARRS1 (PS1) quasar survey. The PS1 sample includes 125 quasars at z ≈ 5.7–6.2, with −28 ≲ M 1450 ≲ −25. With the addition of 48 fainter quasars from the SHELLQs survey, we evaluate the z ≈ 6 QLF over −28 ≲ M 1450 ≲ −22. Adopting a double power law with an exponential evolution of the quasar density (Φ(z) ∝ 10 k(z−6); k = −0.7), we use a maximum likelihood method to model our data. We find a break magnitude of M*=−26.38−0.60+0.79mag , a faint-end slope of α=−1.70−0.19+0.29 , and a steep bright-end slope of β=−3.84−1.21+0.63 . Based on our new QLF model, we determine the quasar comoving spatial density at z ≈ 6 to be n(M1450<−26)=1.16−0.12+0.13cGpc−3 . In comparison with the literature, we find the quasar density to evolve with a constant value of k ≈ −0.7, from z ≈ 7 to z ≈ 4. Additionally, we derive an ionizing emissivity of ϵ912(z=6)=7.23−1.02+1.65×1022ergs−1Hz−1cMpc−3 , based on the QLF measurement. Given standard assumptions, and the recent measurement of the mean free path by Becker et al. at z ≈ 6, we calculate an H i photoionizing rate of ΓH I(z = 6) ≈ 6 × 10−16 s−1, strongly disfavoring a dominant role of quasars in hydrogen reionization.
The stochastic, damped KdV equation

The author applies singular perturbation theory to the study of a damped KdV soliton under the influence of space- and time-dependent external noise. He finds that asymptotically the shape of the averaged solution approaches that of a Gaussian packet, whose amplitude decays and width grows in the same way as in the case of purely time-dependent noise.
Structured Vision-Language Pretraining for Computational Cooking

Vision-Language Pretraining (VLP) and Foundation models have been the go-to recipe for achieving SoTA performance on general benchmarks. However, leveraging these powerful techniques for more complex vision-language tasks, such as cooking applications, with more structured input data, is still little investigated. In this work, we propose to leverage these techniques for structured-text based computational cuisine tasks. Our strategy, dubbed VLPCook (Structured V ision-L anguage P retraining for Computational Cook ing), ﬁrst transforms existing image-text pairs to image and structured-text pairs. This allows to pretrain our VLP-Cook model using VLP objectives adapted to the strutured data of the resulting datasets, then ﬁnetuning it on down-stream computational cooking tasks. During ﬁnetuning, we also enrich the visual encoder, leveraging pretrained foundation models (e.g. CLIP) to provide local and global textual context. VLPCook outperforms current SoTA by a signiﬁ-cant margin (+3.3 Recall@1 absolute improvement) on the task of Cross-Modal Food Retrieval on the large Recipe1M dataset. Finally, we conduct further experiments on VLP to validate their importance, especially on the Recipe1M+ dataset. The code will be made publicly available.
Hardware Efficient Neural Network Assisted Qubit Readout

—Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classiﬁcation to assign the qubit states ‘0’ or ‘1’. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10%. These errors occur for various reasons – crosstalk, spontaneous state transitions, and excitation caused by the readout pulse. The error-prone nature of readout has resulted in signiﬁcant research to design better discriminators to achieve higher qubit-readout accuracies. The readout accuracy impacts the benchmark ﬁdelity for Noisy Intermediate Scale Quantum (NISQ) applications or the logical error rate in error-correcting codes such as the surface code. Prior works have used machine-learning-assisted single-shot qubit-state classiﬁcation, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-the-shelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency, since discrimination has to be performed in software. Inthiswork, we propose HERQULES , a scalable approach to improve qubit-state inference by using matched ﬁlters in conjunction with a signiﬁcantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more versatile and can support shorter readout durations than the baseline design without additional training overheads
Deep Model Assembling

Large deep learning models have achieved remarkable success in many scenarios. However, training large models is usually challenging, e.g., due to the high computational cost, the unstable and painfully slow optimization procedure, and the vulnerability to overﬁtting. To alleviate these problems, this work studies a divide-and-conquer strategy, i.e., dividing a large model into smaller modules, training them independently, and reassembling the trained modules to obtain the target model. This approach is promising since it avoids directly training large models from scratch. Nevertheless, implementing this idea is non-trivial, as it is difﬁ-cult to ensure the compatibility of the independently trained modules. In this paper, we present an elegant solution to address this issue, i.e., we introduce a global, shared meta model to implicitly link all the modules together. This enables us to train highly compatible modules that collaborate effectively when they are assembled together. We further propose a module incubation mechanism that enables the meta model to be designed as an extremely shallow network. As a result, the additional overhead introduced by the meta model is minimalized. Though conceptually simple, our method signiﬁcantly outperforms end-to-end (E2E) training in terms of both ﬁnal accuracy and training efﬁciency. For example, on top of ViT-Huge, it improves the accuracy by 2.7% compared to the E2E baseline on ImageNet-1K, while saving the training cost by 43% in the meantime. Code is available at https://github.com/LeapLabTHU/ Model-Assembling .
Effects of the antiferrodistortive instability on the structural behavior of 
BaZrO3
 by atomistic simulations

Recently, the possibility of a low-temperature non-cubic phase in BaZrO$_3$ has generated engaging discussions about its true ground state and the consequences on its physical properties. In this paper, we investigate the microscopic behavior of the BaZrO$_3$ cubic phase by developing a shell model from $ab~initio$ calculations and by performing molecular dynamics simulations at finite temperature and under negative pressure. We study three different scenarios created by tuning the intensities of the antiferrodistortive (AFD) instabilities, and consequently, the sequence of phase transitions with temperature. From a detailed analysis of the cubic phase at atomic scale, we find that oxygen octahedra are barely distorted, present rotation angles that may oscillate with significant amplitudes, are AFD correlated with their closest neighbors on the plane perpendicular to the pseudocubic rotation axis exhibiting $(0 0 a^-)$-type ordering, and form instantaneous, dynamic and unstable domains over time. Our simulations support the existence of nanoregions with short-range ordering in cubic BaZrO$_3$ associated with experimentally observed anomalies and unveil that they can exist regardless of whether or not structural phase transitions related with AFD distortions occur at lower temperatures.
An Empirical Study on Multi-Domain Robust Semantic Segmentation

How to eﬀectively leverage the plentiful existing datasets to train a robust and high-performance model is of great signiﬁcance for many practical applications. However, a model trained on a naive merge of diﬀerent datasets tends to obtain poor performance due to annotation conﬂicts and domain divergence. In this paper, we attempt to train a uniﬁed model that is expected to perform well across domains on several popularity segmentation datasets. We conduct a detailed analysis of the impact on model generalization from three aspects of data augmentation, training strategies, and model capacity. Based on the analysis, we propose a robust solution that is able to improve model generalization across domains. Our solution ranks 2nd on RVC 2022 semantic segmentation task, with a dataset only 1/3 size of the 1st model used.