Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared11https://github.com/basedrhys/obfuscated-code2vec for further ML research on source code.
A global view on the colliding-wind binary WR 147

We present results from a global view on the colliding-wind binary WR 147. We analysed new optical spectra of WR 147 obtained with the Gran Telescopio Canarias and archive spectra from the Hubble Space Telescope by making use of modern atmosphere models accounting for optically thin clumping. We adopted a grid-modelling approach to derive some basic physical characteristics of both stellar components in WR 147. For the currently accepted distance of 630 pc to WR 147, the values of mass-loss rate derived from modelling its optical spectra are in acceptable correspondence with that from modelling its X-ray emission. However, they give a lower radio flux than observed. A plausible solution for this problem could be the volume filling factor at large distances from the star (radio-formation region) being smaller than that close to the star (optical-formation region). Adopting this, the model can match well both optical and thermal radio emission from WR 147. The global view on the colliding-wind binary WR 147 thus shows that its observational properties in different spectral domains can be explained in a self-consistent physical picture.
Direct loss minimization for sparse Gaussian processes

The Gaussian process (GP) is an attractive Bayesian model for machine learning which combines an elegant formulation with model flexibility and uncertainty quantification. Sparse Gaussian process (sGP) algorithms provide an approximate solution that mitigates the high computational complexity of GP and the variational approximation is the current best practice for such approximations. Recent theoretical work has shown that an alternative approach, direct loss minimization (DLM), which directly minimizes predictive loss, comes with strong guarantees on the expected loss of the algorithm. In this paper we explore this approach experimentally. We develop the DLM algorithm for sGP and show that with appropriate hyperparameter optimization it provides a significant improvement over the variational approach. In particular, optimizing sGP for log loss provides better calibrated predictions for regression, classification and count prediction, and optimizing sGP for square loss improves the mean square error in regression.
Spectroscopic properties of radio-loud and radio-quiet quasars

Abstract Surveys have shown radio-loud (RL) quasars constitute 10%-15% of the total quasar population and rest are radio-quiet (RQ). However, it is unknown if this radio-loud fraction (RLF) remains consistent among different parameter spaces. This study shows that RLF increases for increasing full width half maximum (FWHM) velocity of the Hβ broad emission line (z < 0.75). To analyse the reason, we compared bolometric luminosity of RL and RQ quasars sample which have FWHM of Hβ broad emission line greater than 15000km/s (High Broad Line or HBL) with which have FWHM of Hβ emission line less than 2500km/s (Low Broad Line or LBL). From the distributions we can conclude for the HBL, RQ and RL quasars are peaking separately and RL quasars are having higher values whereas for the LBL the peaks are almost indistinguishable. We predicted selection effects could be the possible reason but to conclude anything more analysis is needed. Then we compared our result with Wills & Brotherton (1995) and have shown that some objects from our sample do not follow the pattern of the logR vs FWHM plot where R is the ratio of 5 GHz radio core flux density with the extended radio lobe flux density.
Robust Wavelet-Based Watermarking Using Dynamic Strength Factor

In unsecured network environments, ownership protection of digital contents, such as images, is becoming a growing concern. Different watermarking methods have been proposed to address the copyright protection of digital materials. Watermarking methods are challenged with conflicting parameters of imperceptibility and robustness. While embedding a watermark with a high strength factor increases robustness, it also decreases imperceptibility of the watermark. Thus embedding in visually less sensitive regions, i.e., complex image blocks could satisfy both requirements. This paper presents a new wavelet-based watermarking technique using an adaptive strength factor to tradeoff between watermark transparency and robustness. We measure variations of each image block to adaptively set a strength-factor for embedding the watermark in that block. On the other hand, the decoder uses the selected coefficients to safely extract the watermark through a voting algorithm. The proposed method shows better results in terms of PSNR and BER in comparison to recent methods for attacks, such as Median Filter, Gaussian Filter, and JPEG compression.
Exact Single-Source SimRank Computation on Large Graphs

SimRank is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-k SimRank queries have received increasing attention due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than 106 nodes. Consequently, no existing work has evaluated the actual trade-offs between query time and accuracy on large real-world graphs. In this paper, we present ExSim, the first algorithm that computes the exact single-source and top-k SimRank results on large graphs. With high probability, this algorithm produces ground truths with a rigorous theoretical guarantee. We conduct extensive experiments on real-world datasets to demonstrate the efficiency of ExactSim. The results show that ExactSim provides the ground truth for any single-source SimRank query with a precision up to 7 decimal places within a reasonable query time.
Quasi-nondegenerate pump–probe magnetooptical experiment in GaAs/AlGaAs heterostructure based on spectral filtration

We report on a quasi-nondegenerate pump–probe technique that is based on spectral-filtration of femtosecond laser pulses by a pair of mutually-spectrally-disjunctive commercially available interference filters. The described technique enables to obtain pump and probe pulses with wavelengths that are spectrally close but distinct. These contradictory requirements, which are dictated, for example, by a suppression of stray pump photons from the probe beam in spin-sensitive magneto-optical experiments in non-magnetic semiconductors, can be fulfilled at very low cost and basically no requirement on space. Especially the second feature is important in pump–probe microscopy where collinear propagation of pump and probe pulses is dictated by utilization of a microscopic objective and where the setups are typically quite complex but suffer from a limited size of optical breadboards. Importantly, this spectral-filtration of 100 fs long laser pulses does not affect considerably the resulting time-resolution, which remains well below 500 fs. We demonstrate the practical applicability of this technique by performing spin-sensitive magnetooptical Kerr effect (MOKE) experiment in GaAs/AlGaAs heterostructure, where a high-mobility spin system is formed after optical injection of electrons at wavelengths close to the MOKE resonance. In particular, we studied the time- and spatial-evolutions of spin-related (MOKE) and charge-related (reflectivity) signals. We revealed that they evolve in a similar but not exactly the same way which we attributed to interplay of several electron many-body effects in GaAs.
Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission With Statistical CSIT

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.
Plug-and-Play ISTA Converges With Kernel Denoisers

Plug-and-play (PnP) method is a recent paradigm for image regularization, where the proximal operator (associated with some given regularizer) in an iterative algorithm is replaced with a powerful denoiser. Algorithmically, this involves repeated inversion (of the forward model) and denoising until convergence. Remarkably, PnP regularization produces promising results for several restoration applications. However, a fundamental question in this regard is the theoretical convergence of the PnP iterations, since the algorithm is not strictly derived from an optimization framework. This question has been investigated in recent works, but there are still many unresolved problems. For example, it is not known if convergence can be guaranteed if we use generic kernel denoisers (e.g. nonlocal means) within the ISTA framework (PnP-ISTA). We prove that, under reasonable assumptions, fixed-point convergence of PnP-ISTA is indeed guaranteed for linear inverse problems such as deblurring, inpainting and superresolution (the assumptions are verifiable for inpainting). We compare our theoretical findings with existing results, validate them numerically, and explain their practical relevance.
The Evolution of the Inner Regions of Protoplanetary Disks

We present a study of the evolution of the inner few astronomical units of protoplanetary disks around low-mass stars. We consider nearby stellar groups with ages spanning from 1 to 11 Myr, distributed into four age bins. Combining PANSTARSS photometry with spectral types, we derive the reddening consistently for each star, which we use (1) to measure the excess emission above the photosphere with a new indicator of IR excess and (2) to estimate the mass accretion rate ( Ṁ ) from the equivalent width of the Hα line. Using the observed decay of Ṁ as a constraint to fix the initial conditions and the viscosity parameter of viscous evolutionary models, we use approximate Bayesian modeling to infer the dust properties that produce the observed decrease of the IR excess with age, in the range between 4.5 and 24 μm. We calculate an extensive grid of irradiated disk models with a two-layered wall to emulate a curved dust inner edge and obtain the vertical structure consistent with the surface density predicted by viscous evolution. We find that the median dust depletion in the disk upper layers is ϵ∼3×10−3 at 1.5 Myr, consistent with previous studies, and it decreases to ϵ∼3×10−4 by 7.5 Myr. We include photoevaporation in a simple model of the disk evolution and find that a photoevaporative wind mass-loss rate of ∼1–3×10−9M⊙yr−1 agrees with the decrease of the disk fraction with age reasonably well. The models show the inward evolution of the H2O and CO snowlines.
Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226,779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Andersons rule, which is based on the relative band-alignments of the non-interacting monolayers. We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3), to compare the band-alignment description with the predictions from Andersons rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://jarvis.nist.gov/jarvish/). Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function (WF) 2D-metal contacts.
Dense Regression Network for Video Grounding

We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS).
Imaginary multiquadratic number fields of exponent $3$ and $5$

In this paper we obtain a complete list of imaginary $n$-quadratic fields with class groups of exponent $3$ and $5$ under ERH for every positive integer $n$ where an $n$-quadratic field is a number field of degree $2^n$ represented as the composite of $n$ quadratic fields.
New Turán Exponents for Two Extremal Hypergraph Problems

An $r$-uniform hypergraph is called $t$-cancellative if for any $t+2$ distinct edges $A_1,\ldots,A_t,B,C$, it holds that $(\cup_{i=1}^t A_i)\cup B\neq (\cup_{i=1}^t A_i)\cup C$. It is called $t$-union-free if for any two distinct subsets $\mathcal{A},\mathcal{B}$, each consisting of at most $t$ edges, it holds that $\cup_{A\in\mathcal{A}} A\neq \cup_{B\in\mathcal{B}} B$. Let $C_t(n,r)$ (resp. $U_t(n,r)$) denote the maximum number of edges of a $t$-cancellative (resp. $t$-union-free) $r$-uniform hypergraph on $n$ vertices. Among other results, we show that for fixed $r\ge 3,t\ge 3$ and $n\rightarrow\infty$ $$\Omega(n^{\lfloor\frac{2r}{t+2}\rfloor+\frac{2r\pmod{t+2}}{t+1}})=C_t(n,r)=O(n^{\lceil\frac{r}{\lfloor t/2\rfloor+1}\rceil})\text{ and } \Omega(n^{\frac{r}{t-1}})=U_t(n,r)=O(n^{\lceil\frac{r}{t-1}\rceil}),$$ thereby significantly narrowing the gap between the previously known lower and upper bounds. In particular, we determine the Turan exponent of $C_t(n,r)$ when $2\mid t \text{ and } (t/2+1)\mid r$, and of $U_t(n,r)$ when $(t-1)\mid r$. The main tool used in proving the two lower bounds is a novel connection between these problems and sparse hypergraphs.
Pose Estimation for Facilitating Movement Learning from Online Videos

There exists a multitude of online video tutorials to teach physical movements such as exercises. Yet, users lack support to verify the accuracy of their movements when following such videos and have to rely on their own perception. To address this, we developed a web-based application that performs human pose estimation using both video inputs from the online video and web camera, then provides different types of visual feedback to a user. Our study suggests that a user's skeleton overlaid on the user's camera feed improves user performance, whereas a user's skeleton on its own or trainer's skeleton with the trainer video offered limited benefits. Our application demonstrates the potential to enhance learning physical movements from online videos and provides a basis for other guidance systems to design suitable visualizations.
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment

To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, composition-preserving and parameter-free, is developed to tackle this issue natively in convolutional kernel level. Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integer dilated kernels are used to cope with the misalignment of fractional sampling. Moreover, we provide a concise formulation for mini-batch training and utilize a grouping strategy to reduce computational overhead. As a result, it can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance on image aesthetics assessment over the AVA dataset.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.
Challenges and Opportunities in CPS Security: A Physics-based Perspective

The integration of cyber technologies (computing and communication) with the physical world gives rise to complex systems referred to as Cyber Physical Systems (CPS), for example, manufacturing, transportation, smart grid, and water treatment. Many of those systems are part of the critical infrastructure and need to perform safely, reliably, and securely in real-time. CPS security is challenging as compared to the conventional IT systems. An adversary can compromise the system in both the cyber and the physical domains. However, the unique set of technologies and processes being used in a CPS also bring up opportunities for defense. CPS security has been approached in several ways due to the complex interaction of physical and cyber components. In this work, a comprehensive study is taken to summarize the challenges and the proposed solutions for securing CPS from a Physics-based perspective.
Assessing the Efficiency of Different Control Strategies for the Coronavirus (COVID-19) Epidemic

The goal of this work is to analyse the effects of control policies for the coronavirus (COVID-19) epidemic in Brazil. This is done by considering an age-structured SEIR model with a quarantine class and two types of controls. The first one studies the sensitivity with regard to the parameters of the basic reproductive number R0 which is calculated by the next generation method. The second one evaluates different quarantine strategies by comparing their relative total number of deaths.