Miracle-dz/UniFAR-FTUs huggingface dataset:
Should Decorators Preserve the Component Interface?

Decorator design pattern is a well known pattern that allows dynamical attachment of additional functionality to an object. Decorators have been proposed as flexible alternative to subclassing for extending functionality. Still, the Decorator pattern has certain limitations, especially related to the fact that in its classical form it is constrained to a single interface, which is implicitly defined by the type of the concrete components that we intend to decorate. Another problem associated to the Decorator pattern is related to the linear composition of the decorations, which could lead to problems in accessing the newly added responsibilities. In this context, the paper presents variants of the Decorator pattern: MixDecorator and D2Decorator, and a variant specific only to C++ language based on templates - HybridDecorator. MixDecorator could be considered a new enhanced version of the Decorator pattern that eliminates some constraints of the Decorator pattern, but also it could be used as a base of a general extension mechanism. The main advantage of using MixDecorator is that it allows direct access to all newly added responsibilities, and so, we may combine different interface-responsibilities (newly added public methods) and operate with them directly and in any order, hiding the linear composition of the decorations. D2Decorator is a variant based on a double-dispatch mechanism. The C++ metaprogramming mechanism based on templates allows an interesting hybrid variant of the Decorator - HybridDecorator, which mixes on-demand defined inheritance with composition. Using these variants of the Decorator pattern we are not longer limited to one single interface; the set of the messages that could be sent to an object could be enlarged, and so, we may consider that using them, we can dynamically change the type of objects.
Mass-energy threshold dynamics for dipolar quantum gases

We consider a Gross-Pitaevskii equation which appears as a model in the description of dipolar Bose-Einstein condensates, without a confining external trapping potential. We describe the asymptotic dynamics of solutions to the corresponding Cauchy problem in the energy space in different configurations with respect to the mass-energy threshold, namely for initial data above and at the mass-energy threshold. We first establish a scattering criterion for the equation that we prove by means of the concentration/compactness and rigidity scheme. This criterion enables us to show the energy scattering for solutions with data above the mass-energy threshold, for which only blow-up was known. We also prove a blow-up/grow-up criterion for the equation with general data in the energy space. As a byproduct of scattering and blow-up criteria, and the compactness of minimizing sequences for the Gagliardo-Nirenberg's inequality, we study long time dynamics of solutions with data lying exactly at the mass-energy threshold.
Icassp 2022 Deep Noise Suppression Challenge

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS challenge, with the previous editions held at INTERSPEECH 2020 [1], ICASSP 2021 [2], and INTERSPEECH 2021 [3]. We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P.835 to rate and rank-order the challenge entries. We provide access to DNS-MOS P.835 and word accuracy (WAcc) APIs to challenge participants to help with iterative model improvements. In this challenge, we introduced the following changes: (i) Included mobile device scenarios in the blind test set; (ii) Included a personalized noise suppression track with baseline; (iii) Added WAcc as an objective metric; (iv) Included DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz). We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get the final score for ranking the DNS models. We believe that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-world scenarios.
Increasing the accuracy of binary neutron star simulations with an improved vacuum treatment

Numerical relativity simulations are essential to study the last stages of the binary neutron star coalescence. Unfortunately, for stable simulations there is the need to add an artificial low-density atmosphere. Here we discuss a new framework in which we can effectively set the density surrounding the neutron stars to zero to ensure a more accurate simulation. We test our method with a number of single star test cases and for an equal mass binary neutron star simulation. While the bulk motion of the system is not influenced, and hence, there is no improvement with respect to the emitted gravitational-wave signal, we find that the new approach is superior with respect to mass conservation and it allows a much better tracking of outward moving material. This will allow a more accurate simulation of the ejected material and supports the interpretation of present and future multi-messenger observations with more accurate numerical relativity simulations.
Spectroscopic study of the HII regions in the NGC 1232 galaxy

Context. NGC 1232 is a face-on spiral galaxy that serves as an excellent laboratory for the study of star formation due to its proximity. Recent studies have revealed interesting features about this galaxy: X-ray observations suggest that it recently collided with a dwarf galaxy, however, no apparent remnant is observed. Aims. In this study, we search for evidence of this possible collision. Methods. We used long-slit optical spectra of the galaxy in two different positions obtained with the Goodman spectrograph at the SOAR telescope. Results. We detected 18 HII regions in the north-south direction and 22 HII regions in the east-west direction and a background galaxy, NGC 1232B, for which we present the first redshift measurement and spectral analysis. We used the stellar population fitting technique to study the underlying stellar population of NGC 1232 and NGC 1232B and to subtract it from the spectra to measure the emission lines. The emission lines were used to determine the extinction, electron density, chemical abundance, and the star-formation rate gradient of NGC 1232. Conclusions. As is common in spiral galaxies, we found a stellar population gradient with older populations at the central regions and younger ones towards the outskirts, along with a negative oxygen abundance gradient of −0.16 dex/re. Due to the difficulty of measuring important emission lines, the number of objects for the abundance gradient is small, but there is a hint that this galaxy has a broken gradient profile, with a drop towards the center. Some authors have explained this effect as the result of a satellite collision, but observations of a large sample of spiral galaxies shows evidence that goes against such a mechanism. If the collision caused any disturbance in the galaxy, we believe it would be small and hard to detect with a limited number of objects. From all the other measurements, we found no deviations from a typical spiral galaxy and no significant difference between different directions in the galaxy. The stellar population and emission line analysis of NGC 1232B suggest that it is a starburst galaxy.
A Simple and Efficient Framework for Identifying Relation-gaps in Ontologies

Though many ontologies have huge number of classes, one cannot find a good number of object properties connecting the classes in most of the cases. Adding object properties makes an ontology richer and more applicable for tasks such as Question Answering. In this context, the question of which two classes should be considered for discovering object properties becomes very important. We address the above question in this paper. We propose a simple machine learning framework which exhibits low time complexity and yet gives promising results with respect to both precision as well as number of class-pairs retrieved.
Updated Spin and Orbital Parameters and Energy Dependent Pulse Behaviors of the Accreting Millisecond X-Ray Pulsar IGR J17591–2342

We present our work in updating the spin and orbital parameters for the newly discovered accreting millisecond X-ray pulsar (AMXP) IGR J17591–2342 through pulsar timing and analyzing its energy dependent pulse behaviors. The data being analyzed were collected by Neutron Star Interior Composition ExploreR, which observed this AMXP from 2018 August to October. Using the pulse arrival time delay technique, more accurate spin and orbital parameters were evaluated. From the measured spin frequency derivative, it is estimated that the magnetic field of the neutron star in IGR J17591–2342 is approximately 4 × 108 G. Precise pulse profiles can be made using the updated spin and orbital parameters. The soft phase lag phenomenon that is usually seen in other AMXPs is also observed from ∼4 to 12 keV with a value of 0.06 cycles (1.14 μs). Additionally, the pulsed fractional amplitude increases from 1 to ∼5 keV and then decreases for higher energy bands. We found that these phenomena, as well as the energy spectrum, can be explained by the two-component model with a relatively strong blackbody component and an additional unpulsed disk blackbody component.
Semantics-Preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a reinforcement learning based semantics-preserving (i.e. functionality-preserving) attack against black-box GNNs (Graph Neural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semantic Nops and their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these “how to select” decisions. To evaluate the attack, we have trained two kinds of GNNs with three types (e.g., Backdoor, Trojan, and Virus) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than four baseline attacks, namely the binary diversification attack, the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.
X-Ray Spectroscopy of the Highly Magnetized Pulsar PSR J1846-0258, Its Wind Nebula, and Hosting Supernova Remnant Kes 75

We present broadband X-ray spectroscopy of the energetic components that make up the supernova remnant (SNR) Kesteven 75 using concurrent 2017 August 17–20 XMM-Newton and NuSTAR observations, during which the pulsar PSR J1846−0258 is found to be in the quiescent state. The young remnant hosts a bright pulsar wind nebula powered by the highly energetic ( erg s−1) isolated, rotation-powered pulsar, with a spin-down age of only . Its inferred magnetic field (B s = 4.9 × 1013 G) is the largest known for these objects, and is likely responsible for intervals of flare and burst activity, suggesting a transition between/to a magnetar state. The pulsed emission from PSR J1846−0258 is well-characterized in the 2–50 keV range by a power-law model with photon index ΓPSR = 1.24 ± 0.09 and a 2–10 keV unabsorbed flux of (2.3 ± 0.4) × 10−12 erg s−1 cm−2. We find no evidence for an additional non-thermal component above 10 keV in the current state, as would be typical for a magnetar. Compared to the Chandra pulsar spectrum, the intrinsic pulsed fraction is 71 ± 16% in 2–10 keV band. A power-law spectrum for the pulsar wind nebulae (PWN) yields ΓPWN = 2.03 ± 0.02 in the 1–55 keV band, with no evidence of curvature in this range, and a 2–10 keV unabsorbed flux (2.13 ± 0.02) × 10−11 erg s−1 cm−2. The NuSTAR data reveal evidence for a hard X-ray component dominating the SNR spectrum above 10 keV that we attribute to a dust-scattered PWN component. We model the dynamical and radiative evolution of the Kes 75 system to estimate the birth properties of the neutron star, the energetics of its progenitor, and properties of the PWN. This suggests that the progenitor of Kes 75 was originally in a binary system which transferred most of its mass to a companion before exploding.
Dynamics of Companion Galaxies of Early-type Galaxies

We estimated the dynamical masses of 115 early-type galaxies (ETGs) by analyzing the dynamics of satellite and companion galaxies of these ETGs. We selected galaxies with absolute magnitudes between −22 and −25 in the K s -band from the Extragalactic Distance Database. We also selected 216 spiral galaxies for comparison. We employed a simple model to simulate the observed dynamical mass from satellite galaxies at various distances. Our simulations showed that the dynamical masses derived from satellite galaxies with elliptical orbits would be smaller than those with circular orbits even though they contain the same dark mass halos. Therefore, relationships between the observed M dyn/M b distributions and distances would depend on orbital shapes. From the relationships between our observed M dyn/M b distributions and distances, we suggest that the satellite galaxies of the ETGs have relatively more elliptical orbits than those of the spiral galaxies and the M dyn/M b of the ETGs are greater than those of the spiral galaxies.
Cascade Network for Self-Supervised Monocular Depth Estimation

It is a classical compute vision problem to obtain real scene depth maps by using a monocular camera, which has been widely concerned in recent years. However, training this model usually requires a large number of artificially labeled samples. To solve this problem, some researchers use a self-supervised learning model to overcome this problem and reduce the dependence on manually labeled data. Nevertheless, the accuracy and reliability of these methods have not reached the expected standard. In this paper, we propose a new self-supervised learning method based on cascade networks. Compared with the previous self-supervised methods, our method has improved accuracy and reliability, and we have proved this by experiments. We show a cascaded neural network that divides the target scene into parts of different sight distances and trains them separately to generate a better depth map. Our approach is divided into the following four steps. In the first step, we use the self-supervised model to estimate the depth of the scene roughly. In the second step, the depth of the scene generated in the first step is used as a label to divide the scene into different depth parts. The third step is to use models with different parameters to generate depth maps of different depth parts in the target scene, and the fourth step is to fuse the depth map. Through the ablation study, we demonstrated the effectiveness of each component individually and showed high-quality, state-of-the-art results in the KITTI benchmark.
Variance-Reduced Off-Policy Memory-Efficient Policy Search

Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators, which results in poor sample efficiency, and have issues with convergence. A few variance-reduced on-policy policy gradient algorithms have been recently proposed that use methods from stochastic optimization to reduce the variance of the gradient estimate in the REINFORCE algorithm. However, these algorithms are not designed for the off-policy setting and are memory-inefficient, since they need to collect and store a large ``reference'' batch of samples from time to time. To achieve variance-reduced off-policy-stable policy optimization, we propose an algorithm family that is memory-efficient, stochastically variance-reduced, and capable of learning from off-policy samples. Empirical studies validate the effectiveness of the proposed approaches.
W*-Algebras as a complete axiomatisation of Von-Neumann algebras

By the Gelfand-Naimark theorem, any C*-algebra is isometrically isomorphic to a *-algebra of bounded operators on a Hilbert space which is closed with respect to the topology induced by the operator norm. Hence, the C*-algebras furnish an axiomatisation of the structure of those operator algebras. In fact, the axiomatisation is complete in the sense that the entire structural information considered on the closed *-algebra -- namely addition, scalar multiplication, adjunction and composition of operators as well as the operator norm -- is reflected in the C*-algebra. From this point of view, we treat Von-Neumann algebras, another class of operator algebras also defined as closed *-algebras, but with respect to another topology -- one possibility is the weak operator topology. The problem asking for an axiomatisation of Von-Neumann algebras was solved by S. Sakai who introduced so-called W*-algebras. They are C*-algebras which, considered as a Banach space, are isometrically isomorphic to the dual space of another Banach space called the predual. Sakai's theorem asserts that the W*-algebras provide an axiomatisation of Von-Neumann algebras. In this master's thesis, we give a proof of Sakai's theorem. In fact, we provide a stronger version of Sakai's theorem which shows that W*-algebras indeed form a complete axiomatisation analogous to the situation for C*-algebras. Additionally, we show uniqueness of the predual up to isometric isomorphism, simplifying Sakai's original proof by using our strengthened version of Sakai's theorem.
Thermodynamics of the Euler-Heisenberg-AdS black hole

We generalize the solution to the Euler-Heisenberg (EH) theory coupled to gravity that represents a nonlinearly charged static black hole (BH) introducing the cosmological constant; the obtained solution is characterized by four parameters: mass $M$, electric charge $Q$, cosmological constant $\Lambda$ (positive or negative) and the Euler-Heisenberg theory parameter $a$. Then we briefly analyze some BH features like horizons, electromagnetic field and geodesics. We mainly focus on its thermodynamic properties in the extended space where the anti-de Sitter parameter is interpreted as the pressure; we show the consistency between the Smarr formula and the first law of BH thermodynamics, interpreting the parameter of the EH theory as the vacuum polarization. We determine the equation of state and the critical points; the critical volume determines two branches of BHs, one near Maxwell behavior and a second one manifestly nonlinear electromagnetic. Moreover, the analysis of the Gibbs free energy indicates that two phase transitions can occur; we also construct the coexistence curve $P$-$T$ where the different phases of the BH can be observed; the critical point is characterized by the standard mean field theory exponents, and the critical variables satisfy $P_{\rm crit} v_{\rm crit}/ T_{\rm crit} = 3/8 $ plus terms of the ten thousandths order in the Euler-Heisenberg parameter $a$
Synbols: Probing Learning Algorithms with Synthetic Datasets

Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols -- Synthetic Symbols -- a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.
Slide Multiplicity Free Key Polynomials

Schubert polynomials are refined by the key polynomials of Lascoux-Schützen-berger, which in turn are refined by the fundamental slide polynomials of Assaf-Searles. In this paper we determine which fundamental slide polynomial refinements of key polynomials, indexed by strong compositions, are multiplicity free. We also give a recursive algorithm to determine all terms in the fundamental slide polynomial refinement of a key polynomial indexed by a strong composition. From here, we apply our results to begin to classify which fundamental slide polynomial refinements, indexed by weak compositions, are multiplicity free. We completely resolve the cases when the weak composition has at most two nonzero parts or the sum has at most two nonzero terms.
Efficient Competitive Self-Play Policy Optimization

Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterative policy improvement. In previous work, heuristic rules are designed to choose an opponent for the current learner. Typical rules include choosing the latest agent, the best agent, or a random historical agent. However, these rules may be inefficient in practice and sometimes do not guarantee convergence even in the simplest matrix games. In this paper, we propose a new algorithmic framework for competitive self-play reinforcement learning in two-player zero-sum games. We recognize the fact that the Nash equilibrium coincides with the saddle point of the stochastic payoff function, which motivates us to borrow ideas from classical saddle point optimization literature. Our method trains several agents simultaneously, and intelligently takes each other as opponent based on simple adversarial rules derived from a principled perturbation-based saddle optimization method. We prove theoretically that our algorithm converges to an approximate equilibrium with high probability in convex-concave games under standard assumptions. Beyond the theory, we further show the empirical superiority of our method over baseline methods relying on the aforementioned opponent-selection heuristics in matrix games, grid-world soccer, Gomoku, and simulated robot sumo, with neural net policy function approximators.