Miracle-dz/UniFAR-FTUs huggingface dataset:
NavP: Enabling Navigational Programming for Science Data Processing via Application-Initiated Checkpointing

Science Data Systems (SDS) handle science data from acquisition through processing to distribution. They are deployed in the Cloud today, and the efficiency of Cloud instance utilization is critical to success. Conventional SDS are unable to take advantage of a cost-effective Amazon EC2 spot market, especially for long-running tasks. Some of the difficulties found in current practice at NASA/JPL are: a lack of mechanism for app programmers to save valuable partial results for future processing continuation, the heavy weight from using container-based (Singularity) sandboxes with more than 200,000 OS-level files; and the gap between scientists developing algorithms/programs on a laptop and the SDS experts deploying software in Cloud computing or supercomputing. All of the above difficulties can be ameliorated if the process carrying out computations on the scientist’s laptop can be directly migrated among multiple nodes in the Cloud or on the supercomputer. The key idea is to allow the scientist programmers to directly navigate the computations to the data, or help them move the remaining computations to a new Cloud instance when the old one is taken away. We present a first proof-of-principle of this using NavP (Navigational Programming) and fault-tolerant computing (FTC) in SDS, by employing program state migration facilitated by Checkpoint-Restart (C/R). NavP provides a new navigational view of computations in a distributed world for the application programmers. The tool of DHP (DMTCP Hop and Publish) we developed enables the application programmers to navigate the computation among instances or nodes by inserting hop(destination) statements in their app code, and choose when to publish partial results at stages of their algorithms that they think worthwhile for future continuation. The result of using DHP is that a parallel distributed SDS becomes easier to program and deploy, and this enables more efficient leveraging of the Amazon EC2 Spot market. This technical report describes a high-level design and an initial implementation. NASA/Jet Propulsion Laboratory acquires massive amounts of observatory instrument data in their missions from planetary (e.g., Mars Exploration, Cassini, Rosetta, Psyche) to earth science (e.g., Jason, OCO, NISAR, SMAP, MAIA). This instrument data is then processed into science data on massively parallel distributed computing facilities such as the Cloud and supercomputers. Innovations in SDS programming are the key to the success of JPL missions. This work was partially supported by National Science Foundation Grant OAC-1740218 and a grant from Intel Corporation. 1 ar X iv :2 11 2. 10 36 4v 1 [ cs . D C ] 2 0 D ec 2 02 1
Omni-Roach: A Legged Robot Capable of Traversing Multiple Types of Large Obstacles and Self-Righting

Robots excel at avoiding obstacles but struggle to traverse complex 3-D terrain with cluttered large obstacles. By contrast, insects like cockroaches excel at doing so. Recent research in our lab elucidated how locomotor transitions emerge from locomotor-environment interaction for diverse locomotor challenges abstracted from complex 3-D terrain and the strategies to overcome them. Here we built on these fundamental insights to develop a cockroach-inspired legged robot, Omni-Roach, that integrated these strategies to achieve multi-modal locomotion and provide a robophysical model to study the tradeoff between multi-functionality and performance. The robot was based on the RHex design with six compliant legs and featured a rounded body with two wings that can open and a tail with pitch and yaw degrees of freedom. After two development and testing iterations, our robot was capable of overcoming all loco-motor challenges with a high performance and success rate. It traversed cluttered rigid pillars only 1.1× robot body width apart, a 2.5× hip height bump, a 0.75× body length gap, densely cluttered flexible beams only 65% body width apart, and self-righted within 4 seconds. Systematic beam traversal experiments further revealed that a downward-pointing tail oscillating laterally helps roll the body into beam gaps and break frictional and interlocking contact to traverse. Our work highlights the usefulness of multi-functional appendages and exaptation for large obstacle traversal.
Good lambda inequalities for non-doubling measures in $\mathbb{R}^n$

We establish a good lambda inequality relating to the distribution function of Riesz potential and fractional maximal function on (R, dμ) where μ is a positive Radon measure which doesn’t necessarily satisfy a doubling condition. This is extended to weights w in A∞(μ) associated to the measure μ. We also derive potential inequalities as an application.
Convergence of a regularized finite element discretization of the two-dimensional Monge-Ampère equation

This paper proposes a regularization of the Monge-Amp\`ere equation in planar convex domains through uniformly elliptic Hamilton-Jacobi-Bellman equations. The regularized problem possesses a unique strong solution $u_\varepsilon$ and is accessible to the discretization with finite elements. This work establishes locally uniform convergence of $u_\varepsilon$ to the convex Alexandrov solution $u$ to the Monge-Amp\`ere equation as the regularization parameter $\varepsilon$ approaches $0$. A mixed finite element method for the approximation of $u_\varepsilon$ is proposed, and the regularized finite element scheme is shown to be locally uniformly convergent. Numerical experiments provide empirical evidence for the efficient approximation of singular solutions $u$.
Dynamic Property of Phase Transition for Non-Linear Charged Anti-de Sitter black holes

Understanding the thermodynamic phase transition of black holes can provide the deep insight into the fundamental properties of black hole gravity to establish the quantum gravity. In this work, we investigate the phase transition and its dynamics for the charged EPYM AdS black hole. Through reconstructing the Maxwell's equal-area law, we find there exists the high-/low-potential black hole (HPBH/LPBL) phase transition, not only the pure large/small one. The Gibbs free energy landscape ($G_L$) is treated as a function of the black hole horizon, which is the order parameter of the phase transition due to the thermal fluctuation. From the view point of $G_L$, the stable HPBH/LPBL states are corresponding to two wells of $G_L$, which have the same depth. The unstable intermediate-potential black hole state corresponds to the local maximum of $G_L$. Then we focus on the probability evolution governed by the Fokker-Planck equation. Through solving the Fokker-Planck equating with different reflection/absorption boundary conditions and initial conditions, the dynamics of switching between the coexistent HPBH and LPBL phases is probed within the first passage time. Furthermore, the effect of temperature on the dynamic property of phase transition is also investigated. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Parameterized Approximation Algorithms for k-Center Clustering and Variants

k-center is one of the most popular clustering models. While it admits a simple 2-approximation in polynomial time in general metrics, the Euclidean version is NP-hard to approximate within a factor of 1.93, even in the plane, if one insists the dependence on k in the running time be polynomial. Without this restriction, a classic algorithm yields a 2^{O((klog k)/{epsilon})}dn-time (1+epsilon)-approximation for Euclidean k-center, where d is the dimension. In this work, we give a faster algorithm for small dimensions: roughly speaking an O^*(2^{O((1/epsilon)^{O(d)} k^{1-1/d} log k)})-time (1+epsilon)-approximation. In particular, the running time is roughly O^*(2^{O((1/epsilon)^{O(1)}sqrt{k}log k)}) in the plane. We complement our algorithmic result with a matching hardness lower bound. We also consider a well-studied generalization of k-center, called Non-uniform k-center (NUkC), where we allow different radii clusters. NUkC is NP-hard to approximate within any factor, even in the Euclidean case. We design a 2^{O(klog k)}n^2 time 3-approximation for NUkC, and a 2^{O((klog k)/epsilon)}dn time (1+\epsilon)-approximation for Euclidean NUkC. The latter time bound matches the bound for k-center.
A tutorial review: probing molecular structure and dynamics with CEI and LIED

Knowledge of the molecular structure is key to understanding the function of participating molecules in photo-induced chemical reactions. Visualizing the nuclear dynamics of a photochemical reaction requires an ultrafast measurement technique that can identify the location of atoms in molecules with atomic Ångstrom spatial resolution evolving on the nuclear (i.e. hundreds of femtosecond) timescale. Coulomb explosion imaging (CEI) and laser-induced electron diffraction (LIED) offer the required sub-Ångstrom spatial and tens of femtosecond temporal resolution to track in real-time changes in the molecular structure. In this tutorial review, details of the tools, analysis procedures, exemplary previous results and future perspectives of both CEI and LIED techniques are described.
Face Deblurring Based on Separable Normalization and Adaptive Denormalization

Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient details. Considering that faces and backgrounds have different distribution information, in this study, we designed an effective face deblurring network based on separable normalization and adaptive denormalization (SNADNet). First, We fine-tuned the face parsing network to obtain an accurate face structure. Then, we divided the face parsing feature into face foreground and background. Moreover, we constructed a new feature adaptive denormalization to regularize fafcial structures as a condition of the auxiliary to generate more harmonious and undistorted face structure. In addition, we proposed a texture extractor and multi-patch discriminator to enhance the generated facial texture information. Experimental results on both CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring network restores face structure with more facial details and performs favorably against state-of-the-art methods in terms of structured similarity indexing method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance (FID) and L1, and qualitative comparisons.
Image Animation with Keypoint Mask

Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. In order to solve it, we face the challenging complexity of motion representation and the unknown relations between the driving video and the source image. Despite its difficulty, this problem attracted great interests from researches at the recent years, with gradual improvements. The goal is often thought as the decoupling of motion and appearance, which is may be solved by extracting the motion from keypoint movement. We chose to tackle the generic, unsupervised setting, where we need to apply animation to any arbitrary object, without any domain specific model for the structure of the input. In this work, we extract the structure from a keypoint heatmap, without an explicit motion representation. Then, the structures from the image and the video are extracted to warp the image according to the video, by a deep generator. We suggest two variants of the structure from different steps in the keypoint module, and show superior qualitative pose and quantitative scores.
Diffusion approximation of controlled branching processes using limit theorems for random step processes

Abstract A controlled branching process (CBP) is a modification of the standard Bienaymé–Galton–Watson process in which the number of progenitors in each generation is determined by a random mechanism. We consider a CBP starting from a random number of initial individuals. The main aim of this article is to provide a Feller diffusion approximation for critical CBPs. A similar result by considering a fixed number of initial individuals by using operator semigroup convergence theorems has been previously proved by Sriram et al. (Stochastic Processes Appl. 2007;117:928–946). An alternative proof is now provided making use of limit theorems for random step processes.
ArcFace Knows the Gender, Too!

The main idea of this paper is that if a model can recognize a person, of course, it must be able to know the gender of that person, too. Therefore, instead of defining a new model for gender classification, this paper uses ArcFace features to determine gender, based on the facial features. A face image is given to ArcFace and 512 features are obtained for the face. Then, with the help of traditional machine learning models, gender is determined. Discriminative methods such as Support Vector Machine (SVM), Linear Discriminant, and Logistic Regression well demonstrate that the features extracted from the ArcFace create a remarkable distinction between the gender classes. Experiments on the Gender Classification Dataset show that SVM with Gaussian kernel is able to classify gender with an accuracy of 96.4% using ArcFace features.
Tracing Accretion onto Herbig Ae/Be Stars Using the Brγ Line

Accretion plays an important role in protoplanetary disk evolution, and it is thought that the accretion mechanism changes between low- and high-mass stars. Here we characterize accretion in intermediate-mass, pre-main-sequence Herbig Ae/Be (HAeBe) stars to search for correlations between accretion and system properties. We present new high-resolution, near-infrared spectra from the Immersion GRating INfrared Spectrograph for 102 HAeBes and analyze the accretion-tracing Brγ line at 2.166 μm. We also include the samples of Fairlamb et al. and Donehew & Brittain, for a total of 155 targets. We find a positive correlation between the Brγ and stellar luminosity, with a change in the slope between the Herbig Aes and Bes. We use L Brγ to determine the accretion luminosity and rate. We find that the accretion luminosity and rate depend on stellar mass and age; however, the trend disappears when normalizing the accretion luminosity by the stellar luminosity. We classify the objects into flared (group I) or flat (group II) disks and find that there is no trend with accretion luminosity or rate, indicating that the disk dust structure is not impacting accretion. We test for Brγ variability in objects that are common to our sample and previous studies. We find that the Brγ line equivalent width is largely consistent between the literature observations and those that we present here, except in a few cases where we may be seeing changes in the accretion rate.
Controlling long ion strings for quantum simulation and precision measurements

Scaling a trapped-ion based quantum simulator to a large number of ions creates a fully-controllable quantum system that becomes inaccessible to numerical methods. When highly anisotropic trapping potentials are used to confine the ions in the form of a long linear string, several challenges have to be overcome to achieve high-fidelity coherent control of a quantum system extending over hundreds of micrometers. In this paper, we describe a setup for carrying out many-ion quantum simulations including single-ion coherent control that we use for demonstrating entanglement in 50-ion strings. Furthermore, we present a set of experimental techniques probing ion-qubits by Ramsey and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences that enable detection (and compensation) of power-line-synchronous magnetic-field variations, measurement of path length fluctuations, and of the wavefronts of elliptical laser beams coupling to the ion string.
JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis

Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JUCIFY approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JUCIFY builds on the Soot internal intermediate representation. We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are “unreachable” in apps' callgraphs, both in goodware and malware. Using JUCIFY, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JUCIFY'S model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JUCIFY we can find sensitive data leaks that pass through native code.
Semi-Decentralized Federated Edge Learning With Data and Device Heterogeneity

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively train deep learning models from the distributed data in 6G networks. Nevertheless, the limited coverage of a single edge server results in an insufficient number of participating client nodes, which may impair the learning performance. In this paper, we investigate a novel FEEL framework, namely semi-decentralized federated edge learning (SD-FEEL), where multiple edge servers collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL incorporates more training data, while enjoying lower latency compared with conventional federated learning. We detail the training algorithm for SD-FEEL with three steps, including local model update, intra-cluster, and inter-cluster model aggregations. The convergence of this algorithm is proved on non-independent and identically distributed data, which reveals the effects of key parameters and provides design guidelines. Meanwhile, the heterogeneity of edge devices may cause the straggler effect and deteriorate the convergence speed of SD-FEEL. To resolve this issue, we propose an asynchronous training algorithm with a staleness-aware aggregation scheme, of which, the convergence is also analyzed. The simulations demonstrate the effectiveness and efficiency of the proposed algorithms for SD-FEEL and corroborate our analysis.
An adaptively optimized algorithm for counting nuclei in X-ray micro-CT scans of whole organisms

Living organisms are primarily made of cells. Identifying them and characterizing their geometry and spatial distribution is a first step towards building multi-scale models of these biomaterials. We propose a method to count cells using nuclei in an X-ray microtomographic scan of a zebrafish. To account for scanning artifacts and partial volume effect, the method is adaptively calibrated using parameters approximated from the manifold of manually selected and optimized special cases. The methodology is tested on nuclei in the eyes of zebrafish larvae of different ages.
D ec 2 02 1 Formation of bound states and BCS-BEC crossover near a flat band : the sawtooth lattice

We investigate pairing and superconductivity in the attractive Hubbard model on the onedimensional sawtooth lattice. This is a periodic lattice with two sites per unit cell, which exhibits a flat band (FB) by fine-tuning the hopping rates. We first discuss the formation of pairs in vacuum, showing that there is a broad region of hopping rates values around the FB point, for which both the binding energy and the effective mass of the bound state are strongly affected even by weak interactions. Based on the DMRG method, we then address the ground-state properties of a system with equal spin populations as a function of the interaction strength and the hopping rates. We compare our results with those available for a linear chain, where the model is integrable by Bethe ansatz, and show that the multiband nature of the system substantially modifies the physics of the BCS-BEC crossover. The chemical potential of a system near the FB point remains always close to its zero-density limit predicted by the two-body physics. In contrast, the pairing gap exhibits a remarkably strong density dependence and, differently from the binding energy, it is no longer peaked at the FB point. We show that these results can be interpreted in terms of polarization screening effects, due to an anomalous attraction between pairs in the medium and single fermions. In particular, we find that three-body bound states (trimers) are allowed in the sawtooth lattice, in sharp contrast with the linear chain geometry.
Neural Networks for Delta Hedging

The Black-Scholes model, defined under the assumption of a perfect financial market, theoretically creates a flawless hedging strategy allowing the trader to evade risks in a portfolio of options. However, the concept of a"perfect financial market,"which requires zero transaction and continuous trading, is challenging to meet in the real world. Despite such widely known limitations, academics have failed to develop alternative models successful enough to be long-established. In this paper, we explore the landscape of Deep Neural Networks(DNN) based hedging systems by testing the hedging capacity of the following neural architectures: Recurrent Neural Networks, Temporal Convolutional Networks, Attention Networks, and Span Multi-Layer Perceptron Networks. In addition, we attempt to achieve even more promising results by combining traditional derivative hedging models with DNN based approaches. Lastly, we construct \textbf{NNHedge}, a deep learning framework that provides seamless pipelines for model development and assessment for the experiments.
Flashover Prediction of Polymeric Insulators Using PD Signal Time-Frequency Analysis and BPA Neural Network Technique

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.