Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger

Limited data access is a substantial barrier to data-driven networking research and development. Although many organizations are motivated to share data, privacy concerns often prevent the sharing of proprietary data, including between teams in the same organization and with outside stakeholders (e.g., researchers, vendors). Many researchers have therefore proposed synthetic data models, most of which have not gained traction because of their narrow scope. In this work, we present DoppelGANger, a synthetic data generation framework based on generative adversarial networks (GANs). DoppelGANger is designed to work on time series datasets with both continuous features (e.g. traffic measurements) and discrete ones (e.g., protocol name). Modeling time series and mixed-type data is known to be difficult; DoppelGANger circumvents these problems through a new conditional architecture that isolates the generation of metadata from time series, but uses metadata to strongly influence time series generation. We demonstrate the efficacy of DoppelGANger on three real-world datasets. We show that DoppelGANger achieves up to 43% better fidelity than baseline models, and captures structural properties of data that baseline methods are unable to learn. Additionally, it gives data holders an easy mechanism for protecting attributes of their data without substantial loss of data utility.
Hydrodynamic response of a surfactant-laden interface to a radial flow

We study the features of a radial Stokes flow due to a submerged jet directed toward a liquid-air interface. The presence of surface-active impurities confers to the interface an in-plane elasticity that resists the incident flow. Both analytical and numerical calculations show that a minute amount of surfactants is enough to profoundly alter the morphology of the flow. The hydrodynamic response of the interface is affected as well, shifting from slip to no-slip boundary condition as the surface compressibility decreases. We argue that the competition between the divergent outward flow and the elastic response of the interface may actually be used as a practical way to detect and quantify a small amount of impurities.
PolarMask: Single Shot Instance Segmentation With Polar Representation

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as predicting contour of instance through instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on the challenging COCO dataset. For the first time, we show that the complexity of instance segmentation, in terms of both design and computation complexity, can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task.
Chiral modular bootstrap

Using modular bootstrap we show the lightest primary fields of a unitary compact two-dimensional conformal field theory (with [Formula: see text], [Formula: see text]) has a conformal weight [Formula: see text]. This implies that the upper bound on the dimension of the lightest primary fields depends on their spin. In particular if the set of lightest primary fields includes extremal or near extremal states whose spin to dimension ratio [Formula: see text], the corresponding dimension is [Formula: see text]. From AdS/CFT correspondence, we obtain an upper bound on the spectrum of black hole in three-dimensional gravity. Our results show that if the first primary fields have large spin, the corresponding three-dimensional gravity has extremal or near extremal BTZ black hole.
Ergodic Inference: Accelerate Convergence by Optimisation

Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias. In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation. The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency. We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.
Groups with ALOGTIME-hard Word Problems and PSPACE-complete Compressed Word Problems

We give lower bounds on the complexity of the word problem for a large class of non-solvable infinite groups that we call strongly efficiently non-solvable groups. This class includes free groups, Grigorchuk’s group, and Thompson’s groups. We prove that these groups have an NC1-hard word problem and that for some of them (including Grigorchuk’s group and Thompson’s groups) the compressed word problem (which is equivalent to the circuit evaluation problem) is PSPACE-complete.
Off-policy Multi-step Q-learning

In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control. Deep Q-learning, however, still suffers from poor data-efficiency which is limiting with regard to real-world applications. We follow the idea of multi-step TD-learning to enhance data-efficiency while remaining off-policy by proposing two novel Temporal-Difference formulations: (1) Truncated Q-functions which represent the return for the first n steps of a policy rollout and (2) Shifted Q-functions, acting as the farsighted return after this truncated rollout. We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm. We show the efficacy of Composite Q-learning in the tabular case and compare our approach in the function-approximation setting with TD3, Model-based Value Expansion and TD3(Delta), which we introduce as an off-policy variant of TD(Delta). We show on three simulated robot tasks that Composite TD3 outperforms TD3 as well as state-of-the-art off-policy multi-step approaches in terms of data-efficiency.
Smoothed residual stopping for statistical inverse problems via truncated SVD estimation

This work examines under what circumstances adaptivity for truncated SVD estimation can be achieved by an early stopping rule based on the smoothed residuals $ \| ( A A^{\top} )^{\alpha / 2} ( Y - A \hat{\mu}^{( m )}) \|^{2} $. Lower and upper bounds for the risk are derived, which show that moderate smoothing of the residuals can be used to adapt over classes of signals with varying smoothness, while oversmoothing yields suboptimal convergence rates. The theoretical results are illustrated by Monte-Carlo simulations.
Stellar Population and Structural Properties of Dwarf Galaxies and Young Stellar Systems in the M81 Group

We use Hyper Suprime-Cam on the Subaru Telescope to investigate the structural and photometric properties of early-type dwarf galaxies and young stellar systems at the center of the M81 Group. We have mapped resolved stars to ∼2 mag below the tip of the red giant branch (RGB) over almost 6.5 square degrees, corresponding to a projected area of 160 × 160 kpc at the distance of M81. The resulting stellar catalog enables a homogeneous analysis of the member galaxies with unprecedented sensitivity to low surface brightness emission. The radial profiles of the dwarf galaxies are well described by Sérsic and King profiles, and show no obvious signatures of tidal disruption. The measured radii for most of these systems are larger than the existing literature values, and we find the total luminosity of IKN (MV,0 = −14.29) to be almost 3 mag brighter than previously thought. We identify new dwarf satellite candidates, d1006+69 and d1009+68, which we estimate to lie at a distance of 4.3 ± 0.2 Mpc and 3.5 ± 0.5 Mpc. With MV,0 = −8.91 ± 0.40 and [M/H] = −1.83 ± 0.28, d1006+69 is one of the faintest and most metal-poor dwarf satellites currently known in the M81 Group. The luminosity functions of young stellar systems in the outlying tidal neutral hydrogen (H i) debris imply continuous star formation in the recent past and the existence of populations as young as 30 Myr old. We find no evidence for old RGB stars coincident with the young MS/cHeB stars that define these objects, supporting the idea that they are genuinely new stellar systems resulting from triggered star formation in gaseous tidal debris.
Probing muonic forces with neutron star binaries

We show that gravitational wave emission from neutron star binaries can be used to discover any generic long-ranged muonic force due to the large inevitable abundance of muons inside neutron stars. As a minimal consistent example, we focus on a gauged U(1)$_{L_\mu - L_\tau}$ symmetry. In pulsar binaries, such U(1)$_{L_\mu - L_\tau}$ vectors induce an anomalously fast decay of the orbital period through the emission of dipole radiation. We study a range of different pulsar binaries, finding the most powerful constraints for vector masses below ${\cal O}(10^{-18} {\rm eV})$. For merging binaries the presence of muons in neutron stars can result in dipole radiation as well as a modification of the chirp mass during the inspiral phase. We make projections for a prospective search using both the GW170817 and S190814bv events and find that current data can discover light vectors with masses below ${\cal O}(10^{-10} {\rm eV})$. In both cases, the limits attainable with neutron stars reach gauge coupling $g^\prime\lesssim 10^{-20}$, which are many orders of magnitude stronger than previous constraints. We also show projections for next generation experiments, such as Einstein Telescope and Cosmic Explorer.
Multi-modal Attention Network Learning for Semantic Source Code Retrieval

Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories given a user query (e.g., a short natural language text describing the functionality for retrieving a particular code snippet). Despite the existing efforts in improving the effectiveness of code retrieval, there are still two main issues hindering them from being used to accurately retrieve satisfiable code fragments from large-scale repositories when answering complicated queries. First, the existing approaches only consider shallow features of source code such as method names and code tokens, but ignoring structured features such as abstract syntax trees (ASTs) and control-flow graphs (CFGs) of source code, which contains rich and well-defined semantics of source code. Second, although the deep learning-based approach performs well on the representation of source code, it lacks the explainability, making it hard to interpret the retrieval results and almost impossible to understand which features of source code contribute more to the final results. To tackle the two aforementioned issues, this paper proposes MMAN, a novel Multi-Modal Attention Network for semantic source code retrieval. A comprehensive multi-modal representation is developed for representing unstructured and structured features of source code, with one LSTM for the sequential tokens of code, a Tree-LSTM for the AST of code and a GGNN (Gated Graph Neural Network) for the CFG of code. Furthermore, a multi-modal attention fusion layer is applied to assign weights to different parts of each modality of source code and then integrate them into a single hybrid representation. Comprehensive experiments and analysis on a large-scale real-world dataset show that our proposed model can accurately retrieve code snippets and outperforms the state-of-the-art methods.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Video object segmentation plays a vital role to many robotic tasks, beyond the satisfied accuracy, quickly adapt to the new scenario with very limited annotations and conduct a quick inference are also important. In this paper, we are specifically concerned with the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available, this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called "ridge regression", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named "block splitting", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having highly comparable performance compared to the best performing methods on the widely used datasets. Video demo can be found here 1.
The Source-Target Domain Mismatch Problem in Machine Translation

While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that this causes the domains of the source and target language to greatly mismatch. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence for its existence. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems on low resource languages. While this may severely affect back-translation, the degradation can be alleviated by combining back-translation with self-training and by increasing the amount of target side monolingual data.
Unmixed and Cohen–Macaulay Weighted Oriented Kőnig Graphs

Let D be a weighted oriented graph, whose underlying graph is G, and let I (D) be its edge ideal. If G has no 3-, 5-, or 7-cycles, or G is Kőnig, we characterize when I (D) is unmixed. If G has no 3- or 5-cycles, or G is Kőnig, we characterize when I (D) is Cohen–Macaulay. We prove that I (D) is unmixed if and only if I (D) is Cohen–Macaulay when G has girth greater than 7 or G is Kőnig and has no 4-cycles.
SN 2016gsd: an unusually luminous and linear Type II supernova with high velocities

We present observations of the unusually luminous Type II supernova (SN) 2016gsd. With a peak absolute magnitude of V = −19.95 ± 0.08, this object is one of the brightest Type II SNe, and lies in the gap of magnitudes between the majority of Type II SNe and the superluminous SNe. Its light curve shows little evidence of the expected drop from the optically thick phase to the radioactively powered tail. The velocities derived from the absorption in H α are also unusually high with the blue edge tracing the fastest moving gas initially at 20 000 km s−1, and then declining approximately linearly to 15 000 km s−1 over ∼100 d. The dwarf host galaxy of the SN indicates a low-metallicity progenitor which may also contribute to the weakness of the metal lines in its spectra. We examine SN 2016gsd with reference to similarly luminous, linear Type II SNe such as SNe 1979C and 1998S, and discuss the interpretation of its observational characteristics. We compare the observations with a model produced by the jekyll code and find that a massive star with a depleted and inflated hydrogen envelope struggles to reproduce the high luminosity and extreme linearity of SN 2016gsd. Instead, we suggest that the influence of interaction between the SN ejecta and circumstellar material can explain the majority of the observed properties of the SN. The high velocities and strong H α absorption present throughout the evolution of the SN may imply a circumstellar medium configured in an asymmetric geometry.
Escape of Flare-accelerated Particles in Solar Eruptive Events

Impulsive solar energetic particle events are widely believed to be due to the prompt escape into the interplanetary medium of flare-accelerated particles produced by solar eruptive events. According to the standard model for such events, however, particles accelerated by the flare reconnection should remain trapped in the flux rope comprising the coronal mass ejection. The particles should reach the Earth only much later, along with the bulk ejecta. To resolve this paradox, we have extended our previous axisymmetric model for the escape of flare-accelerated particles to fully three-dimensional (3D) geometries. We report the results of magnetohydrodynamic simulations of a coronal system that consists of a bipolar active region embedded in a background global dipole field structured by solar wind. Our simulations show that multiple magnetic reconnection episodes occur prior to and during the coronal mass ejection (CME) eruption and its interplanetary propagation. In addition to the episodes that build up the flux rope, reconnection between the open field and the CME couples the closed corona to the open interplanetary field. Flare-accelerated particles initially trapped in the CME thereby gain access to the open interplanetary field along a trail blazed by magnetic reconnection. A key difference between these 3D results and our previous calculations is that the interchange reconnection allows accelerated particles to escape from deep within the CME flux rope. We estimate the spatial extent of the particle-escape channels. The relative timings between flare acceleration and release of the energetic particles through CME/open-field coupling are also determined. All our results compare favorably with observations.
Historical and Philosophical Insights about General Relativity and Space-time from Particle Physics

Historians recently rehabilitated Einstein’s “physical strategy” for General Relativity (GR). Independently, particle physicists similarly rederived Einstein’s equations for a massless spin 2 field. But why not a light massive spin 2, like Neumann and Seeliger did to Newton? Massive gravities are bimetric, supporting conventionalism over geometric empiricism. Nonuniqueness lets field equations explain geometry but not vice versa. Massive gravity would have blocked Schlick’s critique of Kant’s synthetic a priori. Finally in 1970 massive spin 2 gravity seemed unstable or empirically falsified. GR was vindicated, but later and on better grounds. However, recently dark energy and theoretical progress have made massive spin 2 gravity potentially viable again.
Revisiting Self-Training for Neural Sequence Generation

Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence generation tasks (e.g. machine translation) it is still unclear how self-training works due to the compositionality of the target space. In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks. Through careful examination of the performance gains, we find that the perturbation on the hidden states (i.e. dropout) is critical for self-training to benefit from the pseudo-parallel data, which acts as a regularizer and forces the model to yield close predictions for similar unlabeled inputs. Such effect helps the model correct some incorrect predictions on unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a "noisy" version of self-training. Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin.
An Unbiased Approach to Low Rank Recovery

Low rank recovery problems have been a subject of intense study in recent years. While the rank function is useful for regularization it is difficult to optimize due to its non-convexity and discontinuity. The standard remedy for this is to exchange the rank function for the convex nuclear norm, which is known to favor low rank solutions under certain conditions. On the downside the nuclear norm exhibits a shrinking bias that can severely distort the solution in the presence of noise, which motivates the use of stronger non-convex alternatives. In this paper we study two such formulations. We characterize the critical points and give sufficient conditions for a low rank stationary point to be unique. Moreover, we derive conditions that ensure global optimality of the low ranks stationary point and show that these hold under moderate noise levels.
A Copula approach for hyperparameter transfer learning

Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Despite its success, standard BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance metrics of the same algorithm across multiple datasets. In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics. The main idea is to regress the mapping from hyperparameter to metric quantiles with a semi-parametric Gaussian Copula distribution, which provides robustness against different scales or outliers that can occur in different tasks. We introduce two methods to leverage this estimation: a Thompson sampling strategy as well as a Gaussian Copula process using such quantile estimate as a prior. We show that these strategies can combine the estimation of multiple metrics such as runtime and accuracy, steering the optimization toward cheaper hyperparameters for the same level of accuracy. Experiments on an extensive set of hyperparameter tuning tasks demonstrate significant improvements over state-of-the-art methods.