Miracle-dz/UniFAR-FTUs huggingface dataset:
MICROWAVE TYPE III PAIR BURSTS IN SOLAR FLARES

A solar microwave type III pair burst is composed of normal and reverse-sloped (RS) burst branches with oppositely fast frequency drifts. It is the most sensitive signature of the primary energy release and electron accelerations in flares. This work reports 11 microwave type III pair events in 9 flares observed by radio spectrometers in China and the Czech Republic at a frequency of 0.80–7.60 GHz during 1994–2014. These type III pairs occurred in flare impulsive and postflare phases with separate frequencies in the range of 1.08–3.42 GHz and a frequency gap of 10–1700 MHz. The frequency drift increases with the separate frequency (fx), the lifetime of each burst is anti-correlated to fx, while the frequency gap is independent of fx. In most events, the normal branches are drifting obviously faster than the RS branches. The type III pairs occurring in flare impulsive phase have lower separate frequencies, longer lifetimes, wider frequency gaps, and slower frequency drifts than that occurring in postflare phase. Also, the latter always has strong circular polarization. Further analysis indicates that near the flare energy release sites the plasma density is about 10 10 – 10 11 ? > cm−3 and the temperature is higher than 107 K. These results provide new constraints to the acceleration mechanism in solar flares.
Learning to Embed Semantic Similarity for Joint Image-Text Retrieval

We present a deep learning approach for learning the joint semantic embeddings of images and captions in a euclidean space, such that the semantic similarity is approximated by the <inline-formula><tex-math notation="LaTeX">$L_{2}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="keller-ieq1-3132163.gif"/></alternatives></inline-formula> distances in the embedding space. For that, we introduce a metric learning scheme that utilizes multitask learning to learn the embedding of <italic>identical</italic> semantic concepts using a center loss. By introducing a differentiable quantization scheme into the end-to-end trainable network, we derive a semantic embedding of semantically <italic>similar</italic> concepts in euclidean space. We also propose a novel metric learning formulation using an adaptive margin hinge loss, that is refined during the training phase. The proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets, and was shown to compare favorably with contemporary state-of-the-art approaches.
Contextual Modeling for 3D Dense Captioning on Point Clouds

3D dense captioning, as an emerging vision-language task, aims to identify and locate each object from a set of point clouds and generate a distinctive natural language sentence for describing each located object. However, the existing methods mainly focus on mining inter-object relationship, while ignoring contextual information, especially the non-object details and background environment within the point clouds, thus leading to low-quality descriptions, such as inaccurate relative position information. In this paper, we make the ﬁrst attempt to utilize the point clouds clustering features as the contextual information to supply the non-object details and background environment of the point clouds and incorporate them into the 3D dense captioning task. We propose two separate modules, namely the Global Context Modeling (GCM) and Local Context Modeling (LCM), in a coarse-to- ﬁne manner to perform the contextual modeling of the point clouds. Speciﬁcally, the GCM module captures the inter- object relationship among all objects with global contextual information to obtain more complete scene information of the whole point clouds. The LCM module exploits the inﬂuence of the neighboring objects of the target object and local con- textual information to enrich the object representations. With such global and local contextual modeling strategies, our pro- posed model can effectively characterize the object representations and contextual information and thereby generate com- prehensive and detailed descriptions of the located objects. Extensive experiments on the ScanRefer and Nr3D datasets demonstrate that our proposed method sets a new record on the 3D dense captioning task, and verify the effectiveness of our raised contextual modeling of point clouds.
The 1-loop vacuum polarization for a graphene-like medium in an external magnetic field; corrections to the Coulomb potential

I calculate the 1-loop vacuum polarization $\Pi_{\mu\nu}(k,B,a)$ for a photon of momentum $k=(\hat k,k_3)$ interacting with the electrons of a thin medium of thickness $2a$ simulating graphene, in the presence of a constant and uniform external magnetic field $B$ orthogonal to it (parallel to $k_3$). Calculations are done with the techniques of Schwinger, adapted to the geometry and Hamiltonian under scrutiny. The situation gets more involved than for the electron self-energy because the photon is now allowed to also propagate outside the medium. This makes $\Pi_{\mu\nu}$ factorize into a quantum, "reduced" $T_{\mu\nu}(\hat k,B)$ and a transmittance function $V(k,a)$, in which the geometry of the sample and the resulting confinement of the $\gamma\,e^+\,e^-$ vertices play major roles. This drags the results away from reduced QED$_{3+1}$ on a 2-brane. The finiteness of $V$ at $k^2=0$ is an essential ingredient to fulfill suitable renormalization condition for $\Pi_{\mu\nu}$ and to fix the corresponding counterterms. Their connection with the transversality of $\Pi_{\mu\nu}$ is investigated. The corrections to the Coulomb potential and their dependence on $B$ strongly differ from QED$_{3+1}$.
Reconstruction theorems for genus 2 Gromov-Witten invariants

We use Pixton's relations to prove a reconstruction theorem for genus 2 Gromov-Witten invariants in the style of Kontsevich-Manin (genus 0) and Getzler (genus 1). We also calculate genus 2 (descendant) Gromov-Witten invariants of $\mathbb{P}^2$ blown up at a finite number of points in general position.
A deep learning approach to solve forward differential problems on graphs

We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabolic, and hyperbolic problems on graphs. A system of physics-informed neural network (PINN) models is used to solve the differential equations, by assigning each PINN model to a specific edge of the graph. Kirkhoff-Neumann (KN) nodal conditions are imposed in a weak form by adding a penalization term to the training loss function. Through the penalization term that imposes the KN conditions, PINN models associated with edges that share a node coordinate with each other to ensure continuity of the solution and of its directional derivatives computed along the respective edges. Using individual PINN models for each edge of the graph allows our approach to fulfill necessary requirements for parallelization by enabling different PINN models to be trained on distributed compute resources. Numerical results show that the system of PINN models accurately approximate the solutions of the differential problems across the entire graph for a broad set of graph topologies.
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike all existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame, 2) the auxiliary self-supervised scene flow estimator, and 3) our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios.
EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status? ), predictive (what will? ), explanatory (what caused? ), and counterfactual (what if? ) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.
Energy-Efficient Reactive and Predictive Connected Cruise Control

Connected and automated vehicles (CAVs) have shown great potential in improving the energy efficiency of road transportation. Energy savings, however, greatly depends on driving behavior. Therefore, the controllers of CAVs must be carefully designed to fully leverage the benefits of connectivity and automation, especially if CAVs travel amongst other non-connected and human-driven vehicles. With this as motivation, we introduce a framework for the longitudinal control of CAVs traveling in mixed traffic including connected and non-connected human-driven vehicles. Reactive and predictive connected cruise control strategies are proposed. Reactive controllers are given by explicit feedback control laws. Predictive controllers, on the other hand, optimize the control input in a receding-horizon fashion, by predicting the motions of preceding vehicles. Beyond-line-of-sight information obtained via vehicle-to-vehicle (V2V) communication is leveraged by the proposed reactive and predictive controllers. Simulations utilizing real traffic data show that connectivity can bring up to 30% energy savings in certain scenarios.
The LCLS-II Gun&Buncher LLRF Controller Upgrade

LCLS-II is currently in its commissioning phase at SLAC. It is an X-ray FEL driven by a CW superconducting LINAC. The beam injector plays a crucial role in the overall performance of the accelerator, and is critical to the final electron beam performance parameters. The LCLS-II injector comprises of a 185.7 MHz VHF copper gun cavity, and a 1.3 GHz two-cell L-band copper buncher cavity. The FPGA-based controller employs feedback and Self-Excited Loop logic in order to regulate the cavity fields. It also features several other functionalities, such as live detune computation, active frequency tracking, and waveform recording. The LLRF system drives the cavities via two 60 kW SSAs through two power couplers, and thus stabilizes the fields inside the plant. This paper provides an outline of the general functionalities of the system, alongside a description of its hardware, firmware and software architecture, before finalizing with the current status of the project and its future goals.
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approaches are particularly appealing in the offline setting since they can extract more learning signals from the logged dataset by learning a model of the environment. However, the performance of existing model-based approaches falls short of model-free counterparts, due to the compounding of estimation errors in the learned model. Driven by this observation, we argue that it is critical for a model-based method to understand when to trust the model and when to rely on model-free estimates, and how to act conservatively w.r. t. both. To this end, we derive an elegant and simple methodology called conservative Bayesian model-based value expansion for offline policy optimization (CBOP), that trades off model-free and model-based estimates during the policy evaluation step according to their epistemic uncertainties, and facilitates conservatism by taking a lower bound on the Bayesian posterior value estimate. On the standard D4RL continuous control tasks, we find that our method significantly outperforms previous model-based approaches: e.g., MOPO by $116.4$%, MOReL by $23.2$% and COMBO by $23.7$%. Further, CBOP achieves state-of-the-art performance on $11$ out of $18$ benchmark datasets while doing on par on the remaining datasets.
Do you pay for Privacy in Online learning?

Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?
Learning on the Edge: Online Learning with Stochastic Feedback Graphs

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erd\H{o}s-R\'enyi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge. We prove nearly optimal regret bounds of order $\min\bigl\{\min_{\varepsilon} \sqrt{(\alpha_\varepsilon/\varepsilon) T},\, \min_{\varepsilon} (\delta_\varepsilon/\varepsilon)^{1/3} T^{2/3}\bigr\}$ (ignoring logarithmic factors), where $\alpha_{\varepsilon}$ and $\delta_{\varepsilon}$ are graph-theoretic quantities measured on the support of the stochastic feedback graph $\mathcal{G}$ with edge probabilities thresholded at $\varepsilon$. Our result, which holds without any preliminary knowledge about $\mathcal{G}$, requires the learner to observe only the realized out-neighborhood of the chosen action. When the learner is allowed to observe the realization of the entire graph (but only the losses in the out-neighborhood of the chosen action), we derive a more efficient algorithm featuring a dependence on weighted versions of the independence and weak domination numbers that exhibits improved bounds for some special cases.
Rhombic Grids Reduce the Number of Voxels in Fast Pulse-Echo Ultrasound Imaging

Ultrafast imaging modes, such as coherent plane-wave compounding (CPWC), capture a large field of view in a single pulse-echo measurement using parallel receive focusing. The number of foci or, equivalently, the number of volume elements (voxels) in the image determines the computational costs and the memory consumption of the image formation. Herein, 120° rhombic grids are proposed to specify the voxel positions and reduce the number of voxels in comparison to orthogonal grids. The proposed grids derive from the bivariate sampling theorem and the spectral properties of the images formed by the delay-and-sum algorithm in CPWC. A phantom experiment validated the proposed grids and showed reductions in the number of voxels by 81.4% and 14.7% in comparison to the usual and optimal orthogonal grids, respectively. Mean structural similarity indices above 96.6 % and relative root mean-squared errors below 6.8 % confirmed the visual equivalence of all images after interpolations to the usual orthogonal grid.
Variation of Fluxes of RR Tel Emission Lines Measured in 2000 with Respect to 1996

The aim of this work is to make available unpublished non-Fe+ emission line fluxes from optical spectra of the symbiotic nova RR Tel which were taken in 2000, and to compare them with fluxes of the same lines from spectra taken in 1996. After leaving out blends and misidentifications, as well as the unreliable far-red and violet lines, we present the log (F2000/F1996) flux ratios for identified non-Fe+ lines. Mean values of log (F2000/F1996) for different ionization potential ranges of the ions producing the lines are shown separately for the permitted and forbidden lines. All means show fading, which is larger in the lowest range of ionization potential. Provisional interpretations are suggested. We also measured the values of FWHM in 2000; the previously known decrease with time of FWHM of lines due to the same ion has continued.
Applying unsupervised system-call based software security techniques for anomaly detection

Abstract System call analysis is a technique intended for detecting malware. The above method helps in achieving better detection accuracy. Thus, machine learning (ML) techniques are used for this evaluation. This paper discusses unsupervised ML techniques to detect malware. Our proposed detector monitors the software and marks them anomalous or benign based on their behavior. Experimental results provide performance statistics based on the true positive rate at a low false positive rate. As we got considerable accuracy in some models, there is scope for designing an anomaly detection system centered on unsupervised learning. We illustrated how models performed against various malware samples when executed on benign hosts and testbeds. We included a case study to mitigate the adversary attack on the anomaly detection system.
Dust growth in protoplanetary disks - a comprehensive experimental / theoretical approach

More than a decade of dedicated experimental work on the collisional physics of protoplanetary dust has brought us to a point at which the growth of dust aggregates can — for the first time — be self-consistently and reliably modeled. In this article, the emergent collision model for protoplanetery dust aggregates, as well as the numerical model for the evolution of dust aggregates in protoplanetary disks, is reviewed. It turns out that, after a brief period of rapid collisional growth of fluffy dust aggregates to sizes of a few centimeters, the protoplanetary dust particles are subject to bouncing collisions, in which their porosity is considerably decreased. The model results also show that low-velocity fragmentation can reduce the final mass of the dust aggregates but that it does not trigger a new growth mode as discussed previously. According to the current stage of our model, the direct formation of kilometer-sized planetesimals by collisional sticking seems unlikely, implying that collective effects, such as the streaming instability and the gravitational instability in dust-enhanced regions of the protoplanetary disk, are the best candidates for the processes leading to planetesimals.
Performance analysis of nonlinear active noise control system using weighted FLANN algorithm

Abstract Functional Link Artificial Neural Network is popularly used as controller in nonlinear Active noise control system as it is characterized by simple structure and relatively low computational complexity. Learning capability of the controller is achieved by filter-s least mean square algorithm and its variants. But these class of algorithms are characterized by slow convergence. This paper uses a set of combining weights to expedite the convergence process and the performance of the active noise controller is analyzed by tracking the combining weights. Computer simulation experiments are also conducted and analysis results presented.
Formation of interface states and defects in GaAs–AlxGa1−xAs DH lasers under room‐temperature cw operation

Experimental results of an investigation on the nature of degradation‐induced defects in AlxGa1−xAs–GaAs double‐heterostructure injection lasers are presented. The variation of junction capacitance data before and after aging indicates that defects are generated initially at the p‐n heterojunction interface and later spread into the active layer as traps. The density of interface states is calculated to be on the order of 1012 cm−2 and the density of traps in the active layer is calculated to be greater than 1017 cm−3. In addition, the carrier lifetime is measured by using both the delay‐time and voltage‐decay methods. After degradation, the carrier lifetime reduces slightly at the appearance of interface states but shows significant changes when traps are detected in the active layer. An explanation is given and correlated with the threshold‐current variation.
Sensitivity Study for Tritium Permeation in Helium-Cooled Lead-Lithium DEMO Blanket with the FUS-TPC Code

Abstract Hydrogen dissolves in and permeates through most materials, thus it is important to understand the permeation, diffusion and dissolution phenomena of hydrogen and its isotopes in materials. We address the problem of tritium transport in Helium Cooled Lead-Lithium (HCLL) DEMO blanket from lead-lithium breeder through different heat transfer surfaces to the environment by developing a computational code (FUS-TPC). The main features of the code are briefly described and a parametric study is performed in order to identify the most influencing parameters in terms of tritium releases into the environment and of tritium inventories. The results showed that the results are strongly affected by the tritium Sievert’s constant in Lead-Lithium and the efficiency of permeation barriers.
Fabrication of stable and reproducible submicron tunnel junctions

The authors have performed a detailed study of the time stability and reproducibility of submicron Al/AlOx/Al tunnel junctions, fabricated using standard double angle shadow evaporations. The authors have found that by aggressively cleaning the substrate before the evaporations; thus preventing any contamination of the junction, they obtained perfectly stable oxide barriers. The authors also present measurements on large ensembles of junctions which prove the reproducibility of the fabrication process. The measured tunnel resistance variance in large ensembles of identically fabricated junctions is in the range of only a few percent. Finally, the authors have studied the effect of different thermal treatments on the junction barrier. This is especially important for multiple step fabrication processes which imply annealing the junction.