Miracle-dz/UniFAR-FTUs huggingface dataset:
To The Point: Correspondence-driven monocular 3D category reconstruction

We present To The Point (TTP), a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision. We recover a 3D shape from a 2D image by first regressing the 2D positions corresponding to the 3D template vertices and then jointly estimating a rigid camera transform and non-rigid template deformation that optimally explain the 2D positions through the 3D shape projection. By relying on 3D-2D correspondences we use a simple per-sample optimization problem to replace CNN-based regression of camera pose and non-rigid deformation and thereby obtain substantially more accurate 3D reconstructions. We treat this optimization as a differentiable layer and train the whole system in an end-to-end manner. We report systematic quantitative improvements on multiple categories and provide qualitative results comprising diverse shape, pose and texture prediction examples. Project website: https://fkokkinos.github. io/to_the_point/.
DT-grams: Structured Dependency Grammar Stylometry for Cross-Language Authorship Attribution

Cross-language authorship attribution problems rely on either translation to enable the use of single-language features, or language-independent feature extraction methods. Until recently, the lack of datasets for this problem hindered the development of the latter, and single-language solutions were performed on machine-translated corpora. In this paper, we present a novel language-independent feature for authorship analysis based on dependency graphs and universal part of speech tags, called DT-grams (dependency tree grams), which are constructed by selecting specific sub-parts of the dependency graph of sentences. We evaluate DT-grams by performing cross-language authorship attribution on untranslated datasets of bilingual authors, showing that, on average, they achieve a macro-averaged F1 score of 0.081 higher than previous methods across five different language pairs. Additionally, by providing results for a diverse set of features for comparison, we provide a baseline on the previously undocumented task of untranslated cross-language authorship attribution.
Differentiator for Noisy Sampled Signals With Best Worst-Case Accuracy

This letter proposes a differentiator for sampled signals with bounded noise and bounded second derivative. It is based on a linear program derived from the available sample information and requires no further tuning beyond the noise and derivative bounds. A tight bound on the worst-case accuracy, i.e., the worst-case differentiation error, is derived, which is the best among all causal differentiators and is moreover shown to be obtained after a fixed number of sampling steps. Comparisons with the accuracy of existing high-gain and sliding-mode differentiators illustrate the obtained results.
The KMOS galaxy evolution survey (KGES): the angular momentum of star-forming galaxies over the last ≈10 Gyr

We present the KMOS Galaxy Evolution Survey (KGES), a K-band Multi-Object Spectrograph (KMOS) study of the Hα and [N ii] emission from 288 K band-selected galaxies at 1.2 . z . 1.8, with stellar masses in the range log10(M∗/M ) ≈ 9 – 11.5. In this paper, we describe the survey design, present the sample, and discuss the key properties of the KGES galaxies. We combine KGES with appropriately matched samples at lower redshifts from the KMOS Redshift One Spectroscopic Survey (KROSS) and the SAMI Galaxy Survey. Accounting for the effects of sample selection, data quality, and analysis techniques between surveys, we examine the kinematic characteristics and angular momentum content of star-forming galaxies at z ≈ 1.5, ≈ 1 and ≈ 0. We find that stellar mass, rather than redshift, most strongly correlates with the disc fraction amongst star-forming galaxies at z . 1.5, observing only a modest increase in the prevalence of discs between z ≈ 1.5 and z ≈ 0.04 at fixed stellar mass. Furthermore, typical star-forming galaxies follow the same median relation between specific angular momentum and stellar mass, regardless of their redshift, with the normalisation of the relation depending more strongly on how disc-like a galaxy’s kinematics are. This suggests that massive star-forming discs form in a very similar manner across the ≈ 10 Gyr encompassed by our study and that the inferred link between the angular momentum of galaxies and their haloes does not change significantly across the stellar mass and redshift ranges probed in this work.
Thermoelectric transport of type-I, II, and III massless Dirac fermions in a two-dimensional lattice model

We study longitudinal electric and thermoelectric transport coefficients of Dirac fermions on a simple lattice model where tuning of a single parameter enables us to change the type of Dirac cones from type-I to type-II. We pay particular attention to the behavior of the critical situation, i.e., the type-III Dirac cone. We find that the transport coefficients of the type-III Dirac fermions behave as the limiting case of neither the type-I nor type-II. On the one hand, the qualitative behaviors of the type-III case are similar to those of the type-I case. On the other hand, the transport coefficients do not change monotonically upon increasing the tilting; namely, the largest thermoelectric response is obtained not for the type-III case but for the optimally tilted type-I case. For the optimal case, the sizable transport coefficients are obtained; for example, the dimensionless figure of merit is 0.18.
Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

An attempt was made to simulate the structure, track, landfall and a few dynamical aspects of the tropical cyclone Bijli that formed over the Bay of Bengal using WRF-ARW model. WRF model was run in a single domain using KF cumulus parameterization schemes with WSM 3 micro physics and YSU planetary boundary layer scheme. The ARW model was run for 24, 48, 72 and 96 hrs to simulate structure, track and landfall of tropical cyclones Bijli. The different simulated parameters viz . minimum sea level pressure, maximum wind speed, convective available potential energy and relative vorticity have been studied. The results showed that the model is capable to forecast the formation of the first depression 60 - 78 hrs in advance. This indicates the high and unique predictive power of ARW model for predicting the tropical cyclone formation. The model generates a realistic structure of the tropical cyclones with high spatial details. This was possible due to the higher spatial resolution of the regional model. One of the outstanding findings of the study is that the model was successfully predicted the tracks, recurvature and probable areas and time of landfall of the selected tropical cyclone Bijli with high accuracy even in the 96 hrs predictions. Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 157-167, 2015
Simplifying Deep Reinforcement Learning via Self-Supervision

Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks. We are motivated to study how we can take full advantage of supervised loss functions for stably training deep reinforcement learning agents. This is a challenging task because it is unclear how the training data could be collected to enable policy improvement. In this work, we propose Self-Supervised Reinforcement Learning (SSRL), a simple algorithm that optimizes policies with purely supervised losses. We demonstrate that, without policy gradient or value estimation, an iterative procedure of ``labeling"data and supervised regression is sufficient to drive stable policy improvement. By selecting and imitating trajectories with high episodic rewards, SSRL is surprisingly competitive to contemporary algorithms with more stable performance and less running time, showing the potential of solving reinforcement learning with supervised learning techniques. The code is available at https://github.com/daochenzha/SSRL
Kähler–Einstein metrics near an isolated log-canonical singularity

Abstract We construct Kähler–Einstein metrics with negative scalar curvature near an isolated log canonical (non-log terminal) singularity. Such metrics are complete near the singularity if the underlying space has complex dimension 2. We also establish a stability result for Kähler–Einstein metrics near certain types of isolated log canonical singularity. As application, for complex dimension 2 log canonical singularity, we show that any complete Kähler–Einstein metric of negative scalar curvature near an isolated log canonical (non-log terminal) singularity is smoothly asymptotically close to model Kähler–Einstein metrics from hyperbolic geometry.
How rare are counter Evershed flows?

One of the main characteristics of sunspot penumbrae is the radially outward-directed Evershed flow. Only recently have penumbral regions been reported with similar characteristics to normal penumbral filaments but with an opposite direction of the flow. Such flows directed toward the umbra are known as counter Evershed flows (CEFs). We aim to determine the occurrence frequency of CEFs in active regions (ARs) and to characterize their lifetime and the prevailing conditions in the ARs. We analyzed the continuum images, Dopplergrams, and magnetograms recorded by SDO/HMI of 97 ARs that appeared from 2011 to 2017. We followed the ARs for 9.6 ± 1.4 days on average. We found 384 CEFs in total, with a median value of six CEFs per AR. Counter Evershed flows are a rather common feature, occurring in 83.5% of all ARs regardless of the magnetic complexity of the AR. However, CEFs were only observed, on average, during 5.9% of the mean total duration of all the observations analyzed here. The lifetime of CEFs follows a log-normal distribution with a median value of 10.6−6.0+12.4 h. In addition, we report two populations of CEFs, those that are associated with light bridges and those that are not. We explain that the rarity of reports of CEFs in the literature is a result of highly incomplete coverage of ARs with spectropolarimetric data. By using the continuous observations now routinely available from space, we are able to overcome this limitation.
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach

In this paper, we study the application of quasi-Newton methods for solving empirical risk minimization (ERM) problems defined over a large dataset. Traditional deterministic and stochastic quasi-Newton methods can be executed to solve such problems; however, it is known that their global convergence rate may not be better than first-order methods, and their local superlinear convergence only appears towards the end of the learning process. In this paper, we use an adaptive sample size scheme that exploits the superlinear convergence of quasi-Newton methods globally and throughout the entire learning process. The main idea of the proposed adaptive sample size algorithms is to start with a small subset of data points and solve their corresponding ERM problem within its statistical accuracy, and then enlarge the sample size geometrically and use the optimal solution of the problem corresponding to the smaller set as an initial point for solving the subsequent ERM problem with more samples. We show that if the initial sample size is sufficiently large and we use quasi-Newton methods to solve each subproblem, the subproblems can be solved superlinearly fast (after at most three iterations), as we guarantee that the iterates always stay within a neighborhood that quasi-Newton methods converge superlinearly. Numerical experiments on various datasets confirm our theoretical results and demonstrate the computational advantages of our method.
Thermodynamics of accelerating and supersymmetric 
AdS4
 black holes

We study the thermodynamics of AdS 4 black hole solutions of Einstein-Maxwell theory that are accelerating, rotating, and carry electric and magnetic charges. We focus on the class for which the black hole horizon is a spindle and can be uplifted on regular Sasaki-Einstein spaces to give solutions of D ¼ 11 supergravity that are free from conical singularities. We use holography to calculate the Euclidean on-shell action and to define a set of conserved charges which give rise to a first law. We identify a complex locus of supersymmetric and nonextremal solutions, defined through an analytic continuation of the parameters, upon which we obtain a simple expression for the on-shell action. A Legendre transform of this action combined with a reality constraint then leads to the Bekenstein-Hawking entropy for the class of supersymmetric and extremal black holes.
Lifting The Grey Curtain: A First Look at the Ecosystem of CULPRITWARE

Mobile apps are extensively involved in cyber-crimes. Some apps are malware which compromise users' devices, while some others may lead to privacy leakage. Apart from them, there also exist apps which directly make profit from victims through deceiving, threatening or other criminal actions. We name these apps as CULPRITWARE. They have become emerging threats in recent years. However, the characteristics and the ecosystem of CULPRITWARE remain mysterious. This paper takes the first step towards systematically studying CULPRITWARE and its ecosystem. Specifically, we first characterize CULPRITWARE by categorizing and comparing them with benign apps and malware. The result shows that CULPRITWARE have unique features, e.g., the usage of app generators (25.27%) deviates from that of benign apps (5.08%) and malware (0.43%). Such a discrepancy can be used to distinguish CULPRITWARE from benign apps and malware. Then we understand the structure of the ecosystem by revealing the four participating entities (i.e., developer, agent, operator and reaper) and the workflow. After that, we further reveal the characteristics of the ecosystem by studying the participating entities. Our investigation shows that the majority of CULPRITWARE (at least 52.08%) are propagated through social media rather than the official app markets, and most CULPRITWARE (96%) indirectly rely on the covert fourth-party payment services to transfer the profits. Our findings shed light on the ecosystem, and can facilitate the community and law enforcement authorities to mitigate the threats. We will release the source code of our tools to engage the community.
A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems

Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for knowledge-based task-oriented dialog systems, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.
Distributionally Robust Prescriptive Analytics with Wasserstein Distance

In prescriptive analytics, the decision-maker observes historical samples of $(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the concurrent covariate, without knowing the joint distribution. Given an additional covariate observation $x$, the goal is to choose a decision $z$ conditional on this observation to minimize the cost $\mathbb{E}[c(z,Y)|X=x]$. This paper proposes a new distributionally robust approach under Wasserstein ambiguity sets, in which the nominal distribution of $Y|X=x$ is constructed based on the Nadaraya-Watson kernel estimator concerning the historical data. We show that the nominal distribution converges to the actual conditional distribution under the Wasserstein distance. We establish the out-of-sample guarantees and the computational tractability of the framework. Through synthetic and empirical experiments about the newsvendor problem and portfolio optimization, we demonstrate the strong performance and practical value of the proposed framework.
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents

Text-based games (TBGs) have become a popular proving ground for the demonstration of learning-based agents that make decisions in quasi real-world settings. The crux of the problem for a reinforcement learning agent in such TBGs is identifying the objects in the world, and those objects' relations with that world. While the recent use of text-based resources for increasing an agent's knowledge and improving its generalization have shown promise, we posit in this paper that there is much yet to be learned from visual representations of these same worlds. Specifically, we propose to retrieve images that represent specific instances of text observations from the world and train our agents on such images. This improves the agent's overall understanding of the game scene and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship. We show that incorporating such images improves the performance of agents in various TBG settings.
Variational truncated Wigner approximation for weakly interacting Bose fields: Dynamics of coupled condensates

The truncated Wigner approximation is an established approach that describes the dynamics of weakly interacting Bose gases beyond the mean-field level. Although it allows a quantum field to be expressed by a stochastic c-number field, the simulation of the time evolution is still very demanding for most applications. Here, we develop a numerically inexpensive scheme by approximating the c-number field with a variational ansatz. The dynamics of the ansatz function is described by a tractable set of coupled ordinary stochastic differential equations for the respective variational parameters. We investigate the non-equilibrium dynamics of a three-dimensional Bose gas in a one-dimensional optical lattice with a transverse isotropic harmonic confinement. The accuracy and computational inexpensiveness of our method are demonstrated by comparing its predictions to experimental data.
DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact [Ly et al. 2020]. We draw inspiration from previous work [Du et al. 2021] to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.
Distance Metric Learning through Minimization of the Free Energy

Distance metric learning has attracted a lot of interest for solving machine learning and pattern recognition problems over the last decades. In this work we present a simple approach based on concepts from statistical physics to learn optimal distance metric for a given problem. We formulate the task as a typical statistical physics problem: distances between patterns represent constituents of a physical system and the objective function corresponds to energy. Then we express the problem as a minimization of the free energy of a complex system, which is equivalent to distance metric learning. Much like for many problems in physics, we propose an approach based on Metropolis Monte Carlo to find the best distance metric. This provides a natural way to learn the distance metric, where the learning process can be intuitively seen as stretching and rotating the metric space until some heuristic is satisfied. Our proposed method can handle a wide variety of constraints including those with spurious local minima. The approach works surprisingly well with stochastic nearest neighbors from neighborhood component analysis (NCA). Experimental results on artificial and real-world data sets reveal a clear superiority over a number of state-of-the-art distance metric learning methods for nearest neighbors classification.
A Comparison and Combination of Unsupervised Blind Source Separation Techniques

Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based source separation. The unsupervised techniques can be categorized in two classes, those building upon the sparsity of speech in the Short-Time Fourier transform domain and those exploiting non-Gaussianity or non-stationarity of the source signals. In this contribution, spatial mixture models which fall in the first category and independent vector analysis (IVA) as a representative of the second category are compared w.r. t. their separation performance and the performance of a downstream speech recognizer on a reverberant dataset of reasonable size. Furthermore, we introduce a serial concatenation of the two, where the result of the mixture model serves as initialization of IVA, which achieves significantly better WER performance than each algorithm individually and even approaches the performance of a much more complex neural network based technique.
Learning Based Proximity Matrix Factorization for Node Embedding

Node embedding learns a low-dimensional representation for each node in the graph. Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes. Existing approaches first define a proximity matrix and then learn the embeddings that fit the proximity by matrix factorization. Most existing matrix factorization methods adopt the same proximity for different tasks, while it is observed that different tasks and datasets may require different proximity, limiting their representation power. Motivated by this, we propose Lemane, a framework with trainable proximity measures, which can be learned to best suit the datasets and tasks at hand automatically. Our method is end-to-end, which incorporates differentiable SVD in the pipeline so that the parameters can be trained via backpropagation. However, this learning process is still expensive on large graphs. To improve the scalability, we train proximity measures only on carefully subsampled graphs, and then apply standard proximity matrix factorization on the original graph using the learned proximity. Note that, computing the learned proximities for each pair is still expensive for large graphs, and existing techniques for computing proximities are not applicable to the learned proximities. Thus, we present generalized push techniques to make our solution scalable to large graphs with millions of nodes. Extensive experiments show that our proposed solution outperforms existing solutions on both link prediction and node classification tasks on almost all datasets.