Miracle-dz/UniFAR-FTUs huggingface dataset:
Coaxing the Eclipsing Binary V367 Cygni out of Its Shell

Spectra that cover 0.63–0.69 μm with a spectral resolution ∼17,000 are presented of the W Serpentis system V367 Cygni. Absorption lines of Fe ii and Si ii that form in a circumsystem shell are prominent features, and the depths of these are stable with time, suggesting that the shell is smoothly distributed and well mixed. Further evidence of uniformity comes from modest radial velocity variations measured in the deepest parts of the shell lines. It is suggested that motions previously attributed to rotation of the shell are instead artifacts of contamination from the donor star spectrum. A donor star spectrum is extracted that is consistent with that of an early to mid-A giant. The depths of metallic lines in the donor spectrum vary with orbital phase, suggesting that spot activity covers a large fraction of the surface of that star. A spectrum of the accretion disk that surrounds the second star is also extracted, and similarities are noted with the emission spectra of Herbig Ae/Be stars. In addition to variations with orbital phase, Hα changes with time over timescales of no more than two orbits. A tentative detection of He i 6678 emission is made near primary minimum, but not at other phases. Finally, projected emission from hot dust in and around V367 Cyg is more or less symmetric and extends over 28″, or 0.09 pc at the distance of the system; V367 Cyg is thus expelling matter into a large volume of the surrounding space.
The Effect of the Chromospheric Temperature on Coronal Heating

Recent observational and numerical studies show a variety of thermal structures in the solar chromosphere. Given that the thermal interplay across the transition region is a key to coronal heating, it is worth investigating how different thermal structures of the chromosphere yield different coronal properties. In this work, by MHD simulations of Alfvén-wave heating of coronal loops, we study how the coronal properties are affected by the chromospheric temperature. To this end, instead of solving the radiative transfer equation, we employ a simple radiative loss function so that the chromospheric temperature is easily tuned. When the chromosphere is hotter, because the chromosphere extends to a larger height, the coronal part of the magnetic loop becomes shorter, which enhances the conductive cooling. A larger loop length is therefore required to maintain the high-temperature corona against the thermal conduction. From our numerical simulations we derive a condition for the coronal formation with respect to the half loop length l loop in a simple form: lloop>aTmin+lth , where Tmin is the minimum temperature in the atmosphere, and parameters a and l th have negative dependencies on the coronal field strength. Our conclusion is that the chromospheric temperature has a nonnegligible impact on coronal heating for loops with small lengths and weak coronal fields. In particular, the enhanced chromospheric heating could prevent the formation of the corona.
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories

Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, then it becomes necessary to account for the dependence of the output time series on the sequence of supplied inputs when constructing observables to approximate Koopman operators. To address these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.
Atmospheric ionization rates during a geomagnetic reversal

The Matuyama-Brunhes reversal of Earth’s magnetic dipole ﬁeld took place 0.78 Ma ago, and detailed temporally resolved paleomagnetic data are available for this period. A geomagnetic reversal is expected to impact the cosmic ray ﬂux, which in turn might impact atmospheric ionization rates. In this study a model that yields atmospheric ionization for the entire globe based on an input magnetic ﬁeld is presented. Taking the time dependent paleomagnetic data as input, a 3D time series of the atmospheric ionization rates during the reversal is produced. We show, that as the dipole ﬁeld weakens, the atmospheric ionization increases at low latitudes. The increase is ca. 25% at the surface and up to a factor of 5 in the upper atmosphere. Globally, ionization rates increase around 13% at the surface and up to a factor of 2 in the upper atmosphere, whereas polar regions are largely unaﬀected. Finally, the change in ionization due to the solar 11-year cycle is greatly aﬀected by the reversal. The relative change in atmospheric ionization between solar-minimum and solar-maximum varies between 2 and two orders of magnitude. All atmospheric ionization data is made available for download.
ALMA Data Cubes and Continuum Maps of the Irradiated Western Wall in Carina

We present Atacama Large Millimeter/submillimeter Array observations of the continuum and line emission of 12CO, 13CO, C18O, and [C i] for a portion of the G287.38-0.62 (Car 1-E) region in the Carina star-forming complex. The new data record how a molecular cloud responds on subarcsecond scales when subjected to a powerful radiation front, and provide insights into the overall process of star formation within regions that contain the most-massive young stars. The maps show several molecular clouds superpose upon the line of sight, including a portion of the Western Wall, a highly irradiated cloud situated near the young star cluster Trumpler 14. In agreement with theory, there is a clear progression from fluoresced H2, to [C i], to C18O with distance into the photodissociation region (PDR) front. Emission from optically thick 12CO extends across the region, while 13CO, [C i] and especially C18O are more optically thin, and concentrate into clumps and filaments closer to the PDR interface. Within the Western Wall cloud itself we identify 254 distinct core-sized clumps in our data cube of C18O. The mass distribution of these objects is similar to that of the stellar initial mass function. Aside from a large-scale velocity gradient, the clump radial velocities lack any spatial coherence size. There is no direct evidence for triggering of star formation in the Western Wall in that its C18O clumps and continuum cores appear starless, with no pillars present. However, the densest portion of the cloud lies closest to the PDR, and the C18O emission is flattened along the radiation front.
Learning Sparse Latent Representations for Generator Model

Sparsity is a desirable attribute. It can lead to more efﬁcient and more effective representations compared to the dense model. Meanwhile, learning sparse latent representations has been a challenging problem in the ﬁeld of computer vision and machine learning due to its complexity. In this paper, we present a new unsupervised learning method to enforce sparsity on the latent space for the generator model with a gradually sparsiﬁed spike and slab distribution as our prior. Our model consists of only one top-down generator network that maps the latent variable to the observed data. Latent variables can be inferred following generator posterior direction using non-persistent gradient based method. Spike and Slab regularization in the inference step can push non-informative latent dimensions towards zero to induce sparsity. Extensive experiments show the model can preserve majority of the information from original images with sparse representations while demon-strating improved results compared to other existing methods. We observe that our model can learn disentangled semantics and increase explainability of the latent codes while boosting the robustness in the task of classiﬁcation and denoising.
A Semi-Analytical Model for the Formation and Evolution of Radio Relics in Galaxy Clusters

Radio relics are Mpc-sized synchrotron sources located in the peripheral regions of galaxy clusters. Models based on the diffuse shock acceleration (DSA) scenario have been widely accepted to explain the formation of radio relics. However, a critical challenge to these models is that most observed shocks seem too weak to generate detectable emission, unless fossil electrons, a population of mildly energetic electrons that have been accelerated previously, are included in the models. To address this issue, we present a new semi-analytical model to describe the formation and evolution of radio relics by incorporating fossil relativistic electrons into DSA theory, which is constrained by a sample of 14 observed relics, and employ the Press-Schechter formalism to simulate the relics in a $20^{\circ} \times 20^{\circ}$ sky field at 50, 158, and 1400 MHz, respectively. Results show that fossil electrons contribute significantly to the radio emission, which can generate radiation four orders of magnitude brighter than that solely produced by thermal electrons at 158 MHz, and the power distribution of our simulated radio relic catalog can reconcile the observed $P_{1400}-M_{\mathrm{vir}}$ relation. We predict that $7.1\%$ clusters with $M_{\mathrm{vir}}>1.2\times 10^{14}\,\mathrm{M}_{\odot}$ would host relics at 158 MHz, which is consistent with the result of $10 \pm 6\%$ given by the LoTSS DR2. It is also found that radio relics are expected to cause severe foreground contamination in future EoR experiments, similar to that of radio halos. The possibility of AGN providing seed fossil relativistic electrons is evaluated by calculating the number of radio-loud AGNs that a shock is expected to encounter during its propagation.
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem

The Euclidean Steiner tree problem seeks the min-cost network to connect a collection of target locations, and it underlies many applications of wireless networks. In this paper, we present a study on solving the Euclidean Steiner tree problem using reinforcement learning enhanced by graph representation learning. Different from the commonly studied connectivity problems like travelling salesman problem or vehicle routing problem where the search space is finite, the Euclidean Steiner tree problem requires to search over the entire Euclidean space, thereby making the existing methods not applicable. In this paper, we design discretization methods by leveraging the unique characteristics of the Steiner tree, and propose new training schemes for handling the dynamic Steiner points emerging during the incremental construction. Our design is examined through a sanity check using experiments on a collection of datasets, with encouraging results demonstrating the utility of our method as an alternative to classic combinatorial methods.
HiRA: Hidden Row Activation for Reducing Refresh Latency of Off-the-Shelf DRAM Chips

DRAM is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent data loss. Refresh operations degrade system performance by interfering with memory accesses. As DRAM chip density increases with technology node scaling, refresh operations also increase be-cause: 1) the number of DRAM rows in a chip increases; and 2) DRAM cells need additional refresh operations to mitigate bit failures caused by RowHammer, a failure mechanism that becomes worse with technology node scaling. Thus, it is critical to enable refresh operations at low performance overhead. To this end, we propose a new operation, Hidden Row Activation (HiRA), and the HiRA Memory Controller (HiRA-MC) to perform HiRA operations. HiRA hides a refresh operation’s latency by refreshing a row concurrently with accessing or refreshing another row within the same bank. Unlike prior works, HiRA achieves this parallelism without any modifications to off-the-shelf DRAM chips. To do so, it leverages the new observation that two rows in the same bank can be activated without data loss if the rows are connected to different charge restoration circuitry. We experimentally demonstrate on 56 real off-the-shelf DRAM chips that HiRA can reliably parallelize a DRAM row’s refresh operation with refresh or activation of any of the 32% of the rows within the same bank. By doing so, HiRA reduces the overall latency of two refresh operations by 51.4%. HiRA-MC modifies the memory request scheduler to perform HiRA when a refresh operation can be performed concurrently with a memory access or another refresh. Our system-level evaluations show that HiRA-MC increases system performance by 12.6% and $3.73\times$ as it reduces the performance degradation due to periodic refreshes and refreshes for RowHammer protection (preventive refreshes), respectively, for future DRAM chips with increased density and RowHammer vulnerability.
Long-Lived Accurate Keypoints in Event Streams

We present a novel end-to-end approach to keypoint detection and tracking in an event stream that provides better precision and much longer keypoint tracks than previous methods. This is made possible by two contributions working together. First, we propose a simple procedure to generate stable keypoint labels, which we use to train a recurrent architecture. This training data results in detections that are very consistent over time. Moreover, we observe that previous methods for keypoint detection work on a representation (such as the time surface) that integrates events over a period of time. Since this integration is required, we claim it is better to predict the keypoints’ trajectories for the time period rather than single locations, as done in previous approaches. We predict these trajectories in the form of a series of heatmaps for the integration time period. This improves the keypoint localization. Our architecture can also be kept very simple, which results in very fast inference times. We demonstrate our approach on the HVGA ATIS Corner dataset as well as “The Event-Camera Dataset and Simulator” dataset, and show it results in keypoint tracks that are three times longer and nearly twice as accurate as the best previous state-of-the-art methods. We believe our approach can be generalized to other event-based camera problems, and we release our source code to encourage other authors to explore it.
Real-Time Guarantees in Routerless Networks-on-Chip

This article considers the use of routerless networks-on-chip as an alternative on-chip interconnect for multi-processor systems requiring hard real-time guarantees for inter-processor communication. It presents a novel analytical framework that can provide latency upper bounds to real-time packet flows sent over routerless networks-on-chip, and it uses that framework to evaluate the ability of such networks to provide real-time guarantees. Extensive comparative analysis is provided, considering different architectures for routerless networks and a state-of-the-art wormhole network based on priority-preemptive routers as a baseline.
Audit and Improve Robustness of Private Neural Networks on Encrypted Data

Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service. Compared with regular neural networks deployed for machine-learning-as-a-service, PNet requires additional encoding, e.g., quantized-precision numbers, and polynomial activation. Encrypted input also introduces novel challenges such as adversarial robustness and security. To the best of our knowledge, we are the first to study questions including (i) Whether PNet is more robust against adversarial inputs than regular neural networks? (ii) How to design a robust PNet given the encrypted input without decryption? We propose PNet-Attack to generate black-box adversarial examples that can successfully attack PNet in both target and untarget manners. The attack results show that PNet robustness against adversarial inputs needs to be improved. This is not a trivial task because the PNet model owner does not have access to the plaintext of the input values, which prevents the application of existing detection and defense methods such as input tuning, model normalization, and adversarial training. To tackle this challenge, we propose a new fast and accurate noise insertion method, called RPNet, to design Robust and Private Neural Networks. Our comprehensive experiments show that PNet-Attack reduces at least $2.5\times$ queries than prior works. We theoretically analyze our RPNet methods and demonstrate that RPNet can decrease $\sim 91.88\%$ attack success rate.
Comparing direct limit and inverse limit of even $K$-groups in multiple $\mathbb{Z}_p$-extensions

Iwasawa ﬁrst established a duality relating the direct limit and the inverse limit of class groups in a Z p extension, and this result has recently been extended to multiple Z p -extensions by many authors. In this paper, we establish an analogous duality for the direct limit and the inverse limit of higher even K -groups in a Z dp -extension.
Comparative analysis of real bugs in open-source Machine Learning projects - A Registered Report

Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data processing pipeline, serving pipeline, and model training. Existing research on software maintenance has studied the issue-reporting needs and resolution process for different types of issues, such as performance and security issues. However, ML systems have specific classes of faults, and reporting ML issues requires domain-specific information. Because of the different characteristics between ML and traditional Software Engineering systems, we do not know to what extent the reporting needs are different, and to what extent these differences impact the issue resolution process. Objective: Our objective is to investigate whether there is a discrepancy in the distribution of resolution time between ML and non-ML issues and whether certain categories of ML issues require a longer time to resolve based on real issue reports in open-source applied ML projects. We further investigate the size of fix of ML issues and non-ML issues. Method: We extract issues reports, pull requests and code files in recent active applied ML projects from Github, and use an automatic approach to filter ML and non-ML issues. We manually label the issues using a known taxonomy of deep learning bugs. We measure the resolution time and size of fix of ML and non-ML issues on a controlled sample and compare the distributions for each category of issue.
Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model

Deep learning based image quality assessment (IQA) models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes. To account for this, mixed datasets training can be an effective way to enhance the generalization capability of the model. However, it is nontrivial to combine different IQA datasets, as their quality evaluation criteria, score ranges, view conditions, as well as subjects are usually not shared during the image quality annotation. In this paper, instead of aligning the annotations, we propose a monotonic neural network for IQA model learning with different datasets combined. In particular, our model consists of a dataset-shared quality regressor and several dataset-specific quality transformers. The quality regressor aims to obtain the perceptual qualities of each dataset while each quality transformer maps the perceptual qualities to the corresponding dataset annotations with their monotonicity maintained. The experimental results verify the effectiveness of the proposed learning strategy and our code is available at https://github.com/fzp0424/MonotonicIQA.
PTSG: a test generation tool based on extended finite state machine

The Extended Finite State Machine (EFSM) is one of the most popular modeling approaches for model-based testing. However, EFSM-based test case generation is susceptible to the infeasible (inexecutable) path problem, which stems from the conflict of predicates (guards) between transitions in the path. Therefore, in order to derive feasible test cases, a test generation algorithm needs to dynamically acquire information about the model and verify the feasibility of the generated test path through the simulation execution of the model. The traditional method of constructing executable models using hard-coding for different EFSM models under test has limitations such as inflexibility, time-consuming and error-prone. To address this issue, this paper develops an open source test generation tool for testing EFSM-specified systems, PTSG, to support the automatic generation of executable test cases. It decouples the EFSM model description, parsing and simulation execution functions from the test generation algorithm, which can effectively improve the efficiency and quality of test generation. In particular, PTSG first uses a well-designed JSON syntax to describe the specific EFSM under test. Then, based on the model description file, it uses lexical and syntactic parsers to dynamically extract model information to construct various model objects in memory such as state configurations, transitions, etc. Finally, the tool provide a series of service interfaces to support model information acquisition, transition feasibility evaluation, and model simulation execution. A case study of test sequence generation for the SCP protocol model demonstrates the capability and promise of the PTSG to serve executable test cases.