Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed baroclinic convection in a cavity

We study the convective patterns that arise in a nearly semicylindrical cavity fed in with hot fluid at the upper boundary, bounded by a cold, porous semicircular boundary at the bottom, and infinitely extended in the third direction. While this configuration is relevant to continuous casting processes that are significantly more complex, we focus on the flow patterns associated with the particular form of mixed convection that arises in it. Linear stability analysis (LSA) and direct numerical simulations (DNS) are conducted, using the spectral-element method to identify observable states. The nature of the bifurcations is determined through Stuart–Landau analysis for completeness. The base flow consists of two counter-rotating rolls driven by the baroclinic imbalance due to the curved isothermal boundary. These are, however, suppressed by the through-flow, which is found to have a stabilising influence as soon as the Reynolds number $Re$ based on the through-flow exceeds 25. For a sufficiently high Rayleigh number, this base flow is linearly unstable to three different modes, depending on $Re$. For $Re\leqslant 75$, the rolls destabilise through a supercritical bifurcation into a travelling wave. For $100\leqslant Re\leqslant 110$, a subcritical bifurcation leads to a standing oscillatory mode, whereas for $Re\geqslant 150$, the unstable mode is non-oscillatory and grows out of a supercritical bifurcation. The DNS confirm that in all cases the dominant mode returned by the LSA precisely matches the topology and evolution of the flow patterns that arise out of the fully nonlinear dynamics.
Construct $b$-symplectic toric manifolds from toric manifolds

In \cite{btoric}, Guillemin et al. proved a Delzant-type theorem which classifies $b$-symplectic toric manifolds. More generally, in \cite{torus} they proved a similar convexity result for general Hamiltonian torus action on $b$-symplectic manifolds. In this paper, we provide a new way to construct $b$-symplectic toric manifolds from usual toric manifolds. Conversely, through this way, we can also decompose a $b$-symplectic toric manifolds to usual toric manifolds. Finally, we will try to prove that this kind of decomposition is useful, although the symplectic structure for our decomposition or construction is not canonical.
AntNet: Deep Answer Understanding Network for Natural Reverse QA.

This study refers to a reverse question answering(reverse QA) procedure, in which machines proactively raise questions and humans supply answers. This procedure exists in many real human-machine interaction applications. A crucial problem in human-machine interaction is answer understanding. Existing solutions rely on mandatory option term selection to avoid automatic answer understanding. However, these solutions lead to unnatural human-computer interaction and harm user experience. To this end, this study proposed a novel deep answer understanding network, called AntNet, for reverse QA. The network consists of three new modules, namely, skeleton extraction for questions, relevance-aware representation of answers, and multi-hop based fusion. As answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing (NLP) deep models. The effectiveness of the three new modules is also verified.
Efficient Estimation of Mixture Cure Frailty Model for Clustered Current Status Data

Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspection. Motivated by such a current status data from a periodontal study where data are inherently clustered, we propose a unified methodology to analyze such complex data. We allow the time-to-event to follow the semiparametric GOR model with a cure fraction, and develop a unified estimation scheme powered by the EM algorithm. The within-subject correlation is accounted for by a random (frailty) effect, and the non-parametric component of the GOR model is approximated via penalized splines, with a set of knot points that increases with the sample size. Proposed methodology is accompanied by a rigorous asymptotic theory, and the related semiparametric efficiency. The finite sample performance of our model parameters are assessed via simulation studies. Furthermore, the proposed methodology is illustrated via application to the oral health data, accompanied by diagnostic checks to identify influential observations. An easy to use R package CRFCSD is also available for implementation.
Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor

Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is surface following that is a key feature for robots while exploring unknown environments or workspace of inaccurate modeling. In this paper, we propose a novel end-to-end learning strategy, by directly mapping the raw tactile data acquired from a GelSight tactile sensor to the motion of the robot end-effector.Experiments on a KUKA youBot platform equipped with theGelSight sensor show that 80% of the actions generated by a fully trained SFDQN model are proper surface following actions; the autonomous surface following test also indicates that the proposed solution works well on a test surface.
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variational Autoencoder based score approximation yield a theoretically better grounded and more faithful estimate. In our experiments, Variational Autoencoder gradient-based rating outperforms other approaches on unsupervised pixel-wise tumor detection on the BraTS-2017 dataset with a ROC-AUC of 0.94.
A Method for Computing Class-wise Universal Adversarial Perturbations

We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fraction of images of a specific class. Unlike previous methods that use iterative optimization for computing a universal perturbation, the proposed method employs a perturbation that is a linear function of weights of the neural network and hence can be computed much faster. The method does not require any training data and has no hyper-parameters. The attack obtains 34% to 51% fooling rate on state-of-the-art deep neural networks on ImageNet and transfers across models. We also study the characteristics of the decision boundaries learned by standard and adversarially trained models to understand the universal adversarial perturbations.
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling

Learning with kernels is an important concept in machine learning. Standard approaches for kernel methods often use predefined kernels that require careful selection of hyperparameters. To mitigate this burden, we propose in this paper a framework to construct and learn a data-dependent kernel based on random features and implicit spectral distributions that are parameterized by deep neural networks. The constructed network (called KernelNet) can be applied to deep generative modeling in various scenarios, including two popular learning paradigms in deep generative models, MMD-GAN and implicit Variational Autoencoder (VAE). We show that our proposed kernel indeed exists in applications and is guaranteed to be positive definite. Furthermore, the induced Maximum Mean Discrepancy (MMD) can endow the continuity property in weak topology by simple regularization. Extensive experiments indicate that our proposed KernelNet consistently achieves better performance compared to related methods.
The NANOGrav 11 yr Data Set: Constraints on Planetary Masses Around 45 Millisecond Pulsars

We search for extrasolar planets around millisecond pulsars using pulsar timing data and seek to determine the minimum detectable planetary masses as a function of orbital period. Using the 11 yr data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), we look for variations from our models of pulse arrival times due to the presence of exoplanets. No planets are detected around the millisecond pulsars in the NANOGrav 11 yr data set, but taking into consideration the noise levels of each pulsar and the sampling rate of our observations, we develop limits that show we are sensitive to planetary masses as low as that of the moon. We analyzed potential planet periods, P, in the range 7 days < P < 2000 days, with somewhat smaller ranges for some binary pulsars. The planetary-mass limit for our median-sensitivity pulsar within this period range is .
A Restricted Second-Order Logic for Non-deterministic Poly-Logarithmic Time

We introduce a restricted second-order logic $\textrm{SO}^{\textit{plog}}$ for finite structures where second-order quantification ranges over relations of size at most poly-logarithmic in the size of the structure. We demonstrate the relevance of this logic and complexity class by several problems in database theory. We then prove a Fagin’s style theorem showing that the Boolean queries which can be expressed in the existential fragment of $\textrm{SO}^{\textit{plog}}$ correspond exactly to the class of decision problems that can be computed by a non-deterministic Turing machine with random access to the input in time $O((\log n)^k)$ for some $k \ge 0$, i.e. to the class of problems computable in non-deterministic poly-logarithmic time. It should be noted that unlike Fagin’s theorem which proves that the existential fragment of second-order logic captures NP over arbitrary finite structures, our result only holds over ordered finite structures, since $\textrm{SO}^{\textit{plog}}$ is too weak as to define a total order of the domain. Nevertheless, $\textrm{SO}^{\textit{plog}}$ provides natural levels of expressibility within poly-logarithmic space in a way which is closely related to how second-order logic provides natural levels of expressibility within polynomial space. Indeed, we show an exact correspondence between the quantifier prefix classes of $\textrm{SO}^{\textit{plog}}$ and the levels of the non-deterministic poly-logarithmic time hierarchy, analogous to the correspondence between the quantifier prefix classes of second-order logic and the polynomial-time hierarchy. Our work closely relates to the constant depth quasipolynomial size AND/OR circuits and corresponding restricted second-order logic defined by David A. Mix Barrington in 1992. We explore this relationship in detail.
Is Discriminator a Good Feature Extractor?

The discriminator from generative adversarial nets (GAN) has been used by researchers as a feature extractor in transfer learning and appeared worked well. However, there are also studies that believe this is the wrong research direction because intuitively the task of the discriminator focuses on separating the real samples from the generated ones, making features extracted in this way useless for most of the downstream tasks. To avoid this dilemma, we first conducted a thorough theoretical analysis of the relationship between the discriminator task and the features extracted. We found that the connection between the task of the discriminator and the feature is not as strong as was thought, for that the main factor restricting the feature learned by the discriminator is not the task, but is the need to prevent the entire GAN model from mode collapse during the training. From this perspective and combined with further analyses, we found that to avoid mode collapse, the features extracted by the discriminator are not guided to be different for the real samples, but divergence without noise is indeed allowed and occupies a large proportion of the feature space. This makes the features more robust and helps answer the question as to why the discriminator can succeed as a feature extractor in related research. Consequently, to expose the essence of the discriminator extractor as different from other extractors, we analyze the counterpart of the discriminator extractor, the classifier extractor that assigns the target samples to different categories. We found the performance of the discriminator extractor may be inferior to the classifier based extractor when the source classification task is similar to the target task, which is the common case, but the ability to avoid noise prevents the discriminator from being replaced by the classifier.
On the exact entropy of $\mathcal{N}=2$ black holes

We study the exact entropy of four-dimensional $\mathcal{N}=2$ black holes in M-theory both from the brane and supergravity points of view. On the microscopic side the degeneracy is given by a Fourier coefficient of the elliptic genus of the dual two-dimensional $\mathcal{N}=(0,4)$ SCFT and can be extracted via a Rademacher expansion. We show how this expansion is mapped to a modified OSV formula derived by Denef and Moore. On the macroscopic side the degeneracy is computed by applying localization techniques to Sen's quantum entropy functional reducing it to a finite number of integrals. The measure for this finite dimensional integral is determined using a connection with Chern-Simons theory on AdS$_2 \times$S$^1$. The leading answer is a Bessel function in agreement with the microscopic answer. Other subleading corrections can be explained in terms of instanton contributions.
Validation of Automatically Generated Patches: An Appetizer

In the context of test case based automated program repair (APR), the research community call the patches that pass all the test cases but fail to actually fix the bug test case overfitted patches. Currently, overfitted patches has to be manually inspected by the users. Being a labor intensive activity that hinders widespread adoption of APR tools, automatic validation of APR-generated patches has been the topic of research in recent years. In this paper, we point out the limitations of the existing techniques/methodologies that call for further research, and introduce two promising directions toward effective automatic patch validation: (1) motivated by the relative effectiveness of anti-patterns, we propose to use statistical techniques to avoid the uncomputability of applying some of the anti-pattern rules and automate the technique. Our results show that we achieve at least 57% precision. (2) We present a proposal for a semi-automatic technique that helps the programmers in finding properties of the patched methods and stress testing the patches based on those properties so as to filter out overfitted ones as many as possible.
EMAP: Explanation by Minimal Adversarial Perturbation

Modern instance-based model-agnostic explanation methods (LIME, SHAP, L2X) are of great use in data-heavy industries for model diagnostics, and for end-user explanations. These methods generally return either a weighting or subset of input features as an explanation of the classification of an instance. An alternative literature argues instead that counterfactual instances provide a more useable characterisation of a black box classifier's decisions. We present EMAP, a neural network based approach which returns as Explanation the Minimal Adversarial Perturbation to an instance required to cause the underlying black box model to missclassify. We show that this approach combines the two paradigms, recovering the output of feature-weighting methods in continuous feature spaces, whilst also indicating the direction in which the nearest counterfactuals can be found. Our method also provides an implicit confidence estimate in its own explanations, adding a clarity to model diagnostics other methods lack. Additionally, EMAP improves upon the speed of sampling-based methods such as LIME by an order of magnitude, allowing for model explanations in time-critical applications, or at the dataset level, where sampling-based methods are infeasible. We extend our approach to categorical features using a partitioned Gumbel layer, and demonstrate its efficacy on several standard datasets.
Inverse Cognitive Radar - A Revealed Preferences Approach

We consider an adversarial signal processing problem involving us versus an enemy radar equipped with a Bayesian tracker. By observing the emissions of the enemy radar, how can we detect if the radar is cognitive (constrained utility maximizer)? Given knowledge of our state and the observed sequence of actions taken by the enemy radar, we consider three problems: (i) Are the enemy radar actions (waveform choice, beam scheduling) consistent with constrained utility maximization? If so how can we estimate the cognitive radar utility function that is consistent with its actions. We formulate and solve the problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and the algebraic Riccati equation. (ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar actions in noise or the radar observes our probe signal in noise? We propose a statistical detector with a tight Type 2 error bound. (iii) How can we optimally probe (interrogate) the enemy radar by choosing our state to minimize the Type 2 error of detecting if the radar is deploying an economic rational strategy, subject to a constraint on the Type 1 detection error? We present a stochastic optimization algorithm to optimize our probe signal. Our state can be viewed as a probe signal which causes the enemy's radar to act; so choosing the optimal state sequence is an input design problem. The main analysis framework used in this paper is that of revealed preferences from microeconomics.
Joint Resource and Admission Management for Slice-enabled Networks

Network slicing is a crucial part of the 5G networks that communication service providers (CSPs) seek to deploy. By exploiting three main enabling technologies, namely, software-defined networking (SDN), network function virtualization (NFV), and network slicing, communication services can be served to the end-users in an efficient, scalable, and flexible manner. To adopt these technologies, what is highly important is how to allocate the resources and admit the customers of the CSPs based on the predefined criteria and available resources. In this regard, we propose a novel joint resource and admission management algorithm for slice-enabled networks. In the proposed algorithm, our target is to minimize the network cost of the CSP subject to the slice requests received from the tenants corresponding to the virtual machines and virtual links constraints. Our performance evaluation of the proposed method shows its efficiency in managing CSP’s resources.
Randpay: The Technology for Blockchain Micropayments and Transactions Which Require Recipient's Consent

Randpay is a technology developed in Emercoin for blockchain micropayments that can be more effective in some scenarios than the Lightning Network as we show in the paper. The protocol is based on the concept of Ronald L. Rivest and published in the paper "Electronic Lottery Tickets as Micropayments" (1997). The "lottery ticket" was designed for centralized systems where a trusted third party is required to provide payments, and in some scenarios is also a lottery facilitator. The existing blockchain protocol cannot accommodate peer-to-peer "lottery" micropayments at least without the need to create payment channels, which is analysed in the paper. Therefore, the implementation required the development of an update to the blockchain core. In the result, RandpayUTXO was introduced - infinitely spendable zero output that requires the payee's signature to be published in the blockchain. Randpay is considered to be the first blockchain protocol to require the payee to sign the transaction by their private key. This is a significant feature to improve not only microtransactions but also extend the use of the blockchain for legal deeds that require a payee's consent to be recognised in legal applications. The second important innovation of this research is the implementation of Blum's "coin flipping by telephone" problem to design a "lottery ticket" that does not require any third party to facilitate the lottery. The paper offers an API description, an analysis of the mathematical model, and proof of how "lottery" can be beneficial. There is also an attack analysis and overview of existing solutions.
Effect of fishing tactics on the standardization of cardinalfish (Epigonus crassicaudus) catch rates in the demersal multispecies fishery off central Chile

We analyzed the allocation of effective fishing effort and the standardization of cardinalfish (Epigonus crassicaudus) catch rates in the multispecies demersal trawl fishery off central Chile. The period analyzed covered from 1997 to 2004 and included detailed information about fishing hauls. Each haul that contained cardinalfish was assigned into a particular fishing tactic (cluster) by using multivariate analysis of their catch composition. The catch rate standardization was carried out by generalized linear models (GLM). Three fishing tactics were discovered: the first directed effort at cardinalfish, the second at common hake (Merluccius gayi gayi), and the third at Patagonian grenadier (Macruronus magellanicus). Fishing tactic was used as an explanatory variable in the proposed GLM. The fishing tactic effect was one of the most important factors in explaining the variance in the GLM. These results are discussed in the context of how the assignation of a fishing tactic allows unbiased abundance indices to be obtained in this kind of multispecies demersal fishery Se analizo la separacion del esfuerzo de pesca efectivo y la estandarizacion de las tasas de captura de besugo (Epigonus crassicaudus) en la pesqueria multiespecifica de arrastre en Chile central. El periodo analizado correspondio de 1997 a 2004 y comprendio informacion detallada acerca del lance de pesca. Cada lance que contenia besugo en sus registros fue asignado a una tactica de pesca (agrupacion) particular mediante analisis multivariado de la composicion de la captura. La estandarizacion de las tasas de captura se realizo usando modelos lineales generalizados (MLG). Se descubrieron tres tacticas de pesca: una cuyo esfuerzo es dirigido sobre el besugo, otra sobre la merluza comun (Merluccius gayi gayi) y una tercera sobre la merluza de cola (Macruronus magellanicus). La tactica de pesca fue usada como variable explicatoria en el MLG propuesto. La tactica de pesca es uno de los factores que en mayor medida explican la varianza del MLG. Por ultimo, estos resultados son discutidos en el contexto de como la asignacion de una tactica de pesca permitiria obtener indices de abundancia sin sesgos en este tipo de pesquerias demersales multiespecificas.
Studies on the metabolism of pyruvic acid in normal and vitamin B1-deficient states

RESIULTS are reported in this paper on the amounts of bisulphite-binding substances (B.B.s.), particularly pyruvic acid, in blood, cerebro-spinal fluid, urine and milk obtained from adult subjects. Most of these subjects -when investigated were in various stages of vitamin B1 deficiency; for purposes of comparison, however, some observations have been made on apparently healthy persons. Comparative data have also been obtained by determining the pyruvate and B.B.S. levels after administration of vitamin B1. Particularly striking results have followed the treatment of cases of fulminating beri-beri with pure vitamin B1 (thiamin).