Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case for Task Sampling based Learning for Cluster Job Scheduling

The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this paper, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our study focuses on two key questions in comparing task-sampling-based learning (learning in space) and history-based learning (learning in time): (1) Can learning in space be more accurate than learning in time? (2) If so, can delaying scheduling the remaining tasks of a job till the completion of sampled tasks be more than compensated by the improved accuracy and result in improved job performance? Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28x, 1.56x, and 1.32x compared to the prior-art history-based predictor.
CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis

Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them incorrectly. This observation prompts us to devise an automated approach to monitor the condition of people wearing masks. Unlike previous studies, our work goes beyond mask detection;it focuses on generating a personalized demonstration on proper mask-wearing, which helps people use masks better through visual demonstration rather than text explanation. The pipeline starts from the detection of face covering. For images where faces are improperly covered, our mask overlay module incorporates statistical shape analysis (SSA) and dense landmark alignment to approximate the geometry of a face and generates corresponding face-covering examples. Our results show that the proposed system successfully identifies images with faces covered properly. Our ablation study on mask overlay suggests that the SSA model helps to address variations in face shapes, orientations, and scales. The final face-covering examples, especially half profile face images, surpass previous arts by a noticeable margin.
Discretization of parameter identification in PDEs using neural networks

We consider the ill-posed inverse problem of identifying a nonlinearity in a time-dependent partial differential equation model. The nonlinearity is approximated by a neural network (NN), and needs to be determined alongside other unknown physical parameters and the unknown state. Hence, it is not possible to construct input–output data pairs to perform a supervised training process. Proposing an all-at-once approach, we bypass the need for training data and recover all the unknowns simultaneously. In the general case, the approximation via a NN can be realized as a discretization scheme, and the training with noisy data can be viewed as an ill-posed inverse problem. Therefore, we study discretization of regularization in terms of Tikhonov and projected Landweber methods for discretization of inverse problems, and prove convergence when the discretization error (network approximation error) and the noise level tend to zero.
Learning Sparse Analytic Filters for Piano Transcription

In recent years, filterbank learning has become an increasingly popular strategy for various audio-related machine learning tasks. This is partly due to its ability to discover task-specific audio characteristics which can be leveraged in downstream processing. It is also a natural extension of the nearly ubiquitous deep learning methods employed to tackle a diverse array of audio applications. In this work, several variations of a frontend filterbank learning module are investigated for piano transcription, a challenging low-level music information retrieval task. We build upon a standard piano transcription model, modifying only the feature extraction stage. The filterbank module is designed such that its complex filters are unconstrained 1D convolutional kernels with long receptive fields. Additional variations employ the Hilbert transform to render the filters intrinsically analytic and apply variational dropout to promote filterbank sparsity. Transcription results are compared across all experiments, and we offer visualization and analysis of the filterbanks.
Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.
A refined Einstein–Gauss–Bonnet inflationary theoretical framework

We provide a refined and much more simplified Einstein–Gauss–Bonnet inflationary theoretical framework, which is compatible with the GW170817 observational constraints on the gravitational wave speed. As in previous works, the constraint that the gravitational wave speed is cT2=1 in natural units, results to a constraint differential equation that relates the coupling function of the scalar field to the Gauss–Bonnet invariant ξ(ϕ) and the scalar potential V(ϕ). Adopting the slow-roll conditions for the scalar field and the Hubble rate, and in contrast to previous works, by further assuming that κξ′ξ″≪1 , which is motivated by slow-roll arguments, we succeed in providing much more simpler expressions for the slow-roll indices and for the tensor and scalar spectral indices and for the tensor-to-scalar ratio. We exemplify our refined theoretical framework by using an illustrative example with a simple power-law scalar coupling function ξ(ϕ) ∼ ϕ ν and as we demonstrate the resulting inflationary phenomenology is compatible with the latest Planck data. Moreover, this particular model produces a blue-tilted tensor spectral index, so we discuss in brief the perspective of describing the NANOGrav result with this model as is indicated in the recent literature.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization

Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods. In this paper, we propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. In order to fully utilize the rich context information, we design a novel graph neural network based context model to predict the relationship among objects and room layout, and a differentiable relationship-based optimization module to optimize object arrangement with well-designed objective functions on-the-fly. Realizing the existing data are either with incomplete ground truth or overly-simplified scene, we present a new synthetic dataset with good diversity in room layout and furniture placement, and realistic image quality for total panoramic 3D scene understanding. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement. Code is available at https://chengzhag.github. io/publication/dpc.
Nonadiabatic decay of Rydberg-atom–ion molecules

The decay of Rydberg-atom-ion molecules (RAIMs) due to non-adiabatic couplings between electronic potential energy surfaces is investigated. We employ the Born-Huang representation and perform numerical simulations using a Crank-Nicolson algorithm. The non-adiabatic lifetimes of rubidium RAIMs for the lowest ten vibrational states, ν, are computed for selected Rydberg principal quantum numbers, n. The non-adiabatic lifetimes are found to generally exceed the radiative Rydberg-atom lifetimes. We observe and explain a trend of the lifetimes as a function of ν and n, and attribute irregularities to quantum interference arising from a shallow potential well in an inner potential surface. Our results will be useful for future spectroscopic studies of RAIMs.
CDK4 has the ability to regulate Aurora B and Cenpp expression in mouse keratinocytes

Cyclin-dependent kinase 4 (CDK4) is a critical molecule that regulates key aspects of cell proliferation through phosphorylation of the retinoblastoma (Rb) family of proteins. In the last few years, it has been suggested that CDK4 plays alternative roles in cell proliferation and tumorigenesis. The main aim of the present study was to define a novel CDK4 function as a transcriptional regulator of genes involved in chromosome segregation, contributing to the G2/M phase transition. Herein, chromatin-immunoprecipitation reverse transcription-quantitative PCR assays were performed to demonstrate that CDK4 could occupy the promoter region of genes associated with chromosomal segregation, such as Aurora-B (Aurkb) and Centromere Protein P (CENP-P). Moreover, gain- and loss-of-function experiments showed that CDK4 participated in the transcriptional regulation of Aurkb and CENP-P. The finding that Aurkb may have a crucial role in chromosome bi-orientation and the spindle assembly checkpoint, and that CENP-P could be required for proper kinetochore function suggests that dysregulation of CDK4 expression induces chromosomal instability and, in some cases, cancer development.
Recurrent multiple shared layers in Depth for Neural Machine Translation

Learning deeper models is usually a simple and effective approach to improve model performance, but deeper models have larger model parameters and are more difficult to train. To get a deeper model, simply stacking more layers of the model seems to work well, but previous works have claimed that it cannot benefit the model. We propose to train a deeper model with recurrent mechanism, which loops the encoder and decoder blocks of Transformer in the depth direction. To address the increasing of model parameters, we choose to share parameters in different recursive moments. We conduct our experiments on WMT16 English-to-German and WMT14 English-to-France translation tasks, our model outperforms the shallow Transformer-Base/Big baseline by 0.35, 1.45 BLEU points, which is 27.23% of Transformer-Big model parameters. Compared to the deep Transformer(20-layer encoder, 6-layer decoder), our model has similar model performance and infer speed, but our model parameters are 54.72% of the former.
Sonic: A Sampling-based Online Controller for Streaming Applications

Many applications in important problem domains such as machine learning and computer vision are streaming applications that take a sequence of inputs over time. It is challenging to find knob settings that optimize the run-time performance of such applications because the optimal knob settings are usually functions of inputs, computing platforms, time as well as user's requirements, which can be very diverse. Most prior works address this problem by offline profiling followed by training models for control. However, profiling-based approaches incur large overhead before execution; it is also difficult to redeploy them in other run-time configurations. In this paper, we propose Sonic, a sampling-based online controller for long-running streaming applications that does not require profiling ahead of time. Within each phase of a streaming application's execution, Sonic utilizes the beginning portion to sample the knob space strategically and aims to pick the optimal knob setting for the rest of the phase, given a user-specified constrained optimization problem. A hybrid approach of machine learning regressions and Bayesian optimization are used for better overall sampling choices. Sonic is implemented independent of application, device, input, performance objective and constraints. We evaluate Sonic on traditional parallel benchmarks as well as on deep learning inference benchmarks across multiple platforms. Our experiments show that when using Sonic to control knob settings, application run-time performance is only 5.3% less than if optimal knob settings were used, demonstrating that Sonic is able to find near-optimal knob settings under diverse run-time configurations without prior knowledge quickly.
Current Concepts on Subtalar Instability

Subtalar instability remains a topic of debate, and its precise cause is still unknown. The mechanism of injury and clinical symptoms of ankle and subtalar instabilities largely overlap, resulting in many cases of isolated or combined subtalar instability that are often misdiagnosed. Neglecting the subtalar instability may lead to failure of conservative or surgical treatment and result in chronic ankle instability. Understanding the accurate anatomy and biomechanics of the subtalar joint, their interplay, and the contributions of the different subtalar soft tissue structures is fundamental to correctly diagnose and manage subtalar instability. An accurate diagnosis is crucial to correctly identify those patients with instability who may require conservative or surgical treatment. Many different nonsurgical and surgical approaches have been proposed to manage combined or isolated subtalar instability, and the clinician should be aware of available treatment options to make an informed decision. In this current concepts narrative review, we provide a comprehensive overview of the current knowledge on the anatomy, biomechanics, clinical and imaging diagnosis, nonsurgical and surgical treatment options, and outcomes after subtalar instability treatment.
Gravitational wave propagation beyond general relativity: waveform distortions and echoes

We study the cosmological propagation of gravitational waves (GWs) beyond general relativity (GR) across homogeneous and isotropic backgrounds. We consider scenarios in which GWs interact with an additional tensor field and use a parametrized phenomenological approach that generically describes their coupled equations of motion. We analyze four distinct classes of derivative and non-derivative interactions: mass, friction, velocity, and chiral. We apply the WKB formalism to account for the cosmological evolution and obtain analytical solutions to these equations. We corroborate these results by analyzing numerically the propagation of a toy GW signal. We then proceed to use the analytical results to study the modified propagation of realistic GWs from merging compact binaries, assuming that the GW signal emitted is the same as in GR. We generically find that tensor interactions lead to copies of the originally emitted GW signal, each one with its own possibly modified dispersion relation. These copies can travel coherently and interfere with each other leading to a scrambled GW signal, or propagate decoherently and lead to echoes arriving at different times at the observer that could be misidentified as independent GW events. Depending on the type of tensor interaction, the detected GW signal may exhibit amplitude and phase distortions with respect to a GW waveform in GR, as well as birefringence effects. We discuss observational probes of these tensor interactions with both individual GW events, as well as population studies for both ground- and space-based detectors.
Dynamic Properties of Water inside Graphene Oxide Membranes

Separation of salt ions from seawater using graphene oxide (GO) membrane is an emerging desalination method. Here, we investigate the dynamic behavior of water, sodium, and chloride ions inside GO membrane using a computational approach. First, we developed four different models of GO membrane with different distances between GO sheets (0.7, 0.9, 1.1, and 1.3 nm), and performed molecular dynamics (MD) simulations of water inside each membrane for 20 ns. With the analysis of the mean squared displacement of water and the Einstein relation equation for diffusion, we measured the diffusion coefficient of water in each membrane. We found that the diffusion coefficient of water inside the GO membrane decreased as the distance between sheets (d) decreased. The measured diffusion coefficients (in unit of 0.00001 (cm*cm)/sec) of water are 0.03 (d = 0.7 nm), 0.91 (d = 0.9 nm), 1.18 (d = 1.1 nm), and 1.49 (d = 1.3 nm), whereas it is 2.49 for bulk water. Second, for analyzing the desalination performance of each membrane, we also calculated the free energy profile of penetration of water, sodium, and chloride ions via the interlayer of the membrane. The free energy profile of penetration revealed that the optimum interlayer distance between GO sheets for desalination is 0.9 ~ 1.1 nm. With 0.9 and 1.1 nm interlayer distance between GO sheets, the free energy barrier of membrane penetration of water is negligible, where it is 2 ~ 4 kcal/mol for sodium or chloride ions. We believe that our simulation results would be a significant contribution to designing a new GO-based membrane for desalination.
Image-based Insider Threat Detection via Geometric Transformation

Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.
Toward Precise Galaxy Evolution: A Comparison between Spectral Indices of z ∼1 Galaxies in the IllustrisTNG Simulation and the LEGA-C Survey

We present the first comparison of observed stellar continuum spectra of high-redshift galaxies and mock galaxy spectra generated from hydrodynamical simulations. The mock spectra are produced from the IllustrisTNG TNG100 simulation combined with stellar population models and take into account dust attenuation and realistic observational effects (aperture effects and noise). We compare the simulated Dn4000 and EW(Hδ) of galaxies with 10.5≤log(M*/M⊙)≤11.5 at 0.6 ≤ z ≤ 1.0 to the observed distributions from the LEGA-C survey. TNG100 globally reproduces the observed distributions of spectral indices, implying that the age distribution of galaxies in TNG100 is generally realistic. Yet there are small but significant differences. For old galaxies, TNG100 shows small Dn4000 when compared to LEGA-C, while LEGA-C galaxies have larger EW(Hδ) at fixed Dn4000 . There are several possible explanations: (1) LEGA-C galaxies have overall older ages combined with small contributions (a few percent in mass) from younger (<1 Gyr) stars, while TNG100 galaxies may not have such young subpopulations; (2) the spectral mismatch could be due to systematic uncertainties in the stellar population models used to convert stellar ages and metallicities to observables. In conclusion, the latest cosmological galaxy-formation simulations broadly reproduce the global age distribution of galaxies at z ∼ 1 and, at the same time, the high quality of the latest observed and simulated data sets help constrain stellar population synthesis models as well as the physical models underlying the simulations.
On The (In)Effectiveness of Static Logic Bomb Detection for Android Apps

Android is present in more than 85% of mobile devices, making it a prime target for malware. Malicious code is becoming increasingly sophisticated and relies on logic bombs to hide itself from dynamic analysis. In this article, we perform a large scale study of TSOpen, our open-source implementation of the state-of-the-art static logic bomb scanner TriggerScope, on more than 500k Android applications. Results indicate that the approach scales. Moreover, we investigate the discrepancies and show that the approach can reach a very low false-positive rate, 0.3%, but at a particular cost, e.g., removing 90% of sensitive methods. Therefore, it might not be realistic to rely on such an approach to automatically detect all logic bombs in large datasets. However, it could be used to speed up the location of malicious code, for instance, while reverse engineering applications. We also present TrigDB a database of 68 Android applications containing trigger-based behavior as a ground-truth to the research community.