Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning with little mixing

We study square loss in a realizable time-series framework with martingale difference noise. Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data matches the iid rate order-wise after a burn-in time. In comparison, many existing results in learning from dependent data have rates where the effective sample size is deflated by a factor of the mixing-time of the underlying process, even after the burn-in time. Furthermore, our results allow the covariate process to exhibit long range correlations which are substantially weaker than geometric ergodicity. We call this phenomenon learning with little mixing, and present several examples for when it occurs: bounded function classes for which the $L^2$ and $L^{2+\epsilon}$ norms are equivalent, ergodic finite state Markov chains, various parametric models, and a broad family of infinite dimensional $\ell^2(\mathbb{N})$ ellipsoids. By instantiating our main result to system identification of nonlinear dynamics with generalized linear model transitions, we obtain a nearly minimax optimal excess risk bound after only a polynomial burn-in time.
Rotational Variation Allows for Narrow Age Spread in the Extended Main-sequence Turnoff of Massive Cluster NGC 1846

The color–magnitude diagrams (CMDs) of intermediate-age star clusters (≲2 Gyr) are much more complex than those predicted by coeval, nonrotating stellar evolution models. Their observed extended main-sequence turnoffs (eMSTOs) could result from variations in stellar age, stellar rotation, or both. The physical interpretation of eMSTOs is largely based on the complex mapping between stellar models—themselves functions of mass, rotation, orientation, and binarity—and the CMD. In this paper, we compute continuous probability densities in three-dimensional color, magnitude, and vesini space for individual stars in a cluster’s eMSTO, based on a rotating stellar evolution model. These densities enable the rigorous inference of cluster properties from a stellar model, or, alternatively, constraints on the stellar model from the cluster’s CMD. We use the MIST stellar evolution models to jointly infer the age dispersion, the rotational distribution, and the binary fraction of the Large Magellanic Cloud cluster NGC 1846. We derive an age dispersion of ∼70–80 Myr, approximately half the earlier estimates due to nonrotating models. This finding agrees with the conjecture that rotational variation is largely responsible for eMSTOs. However, MIST models do not provide a satisfactory fit to all stars in the cluster and achieve their best agreement at an unrealistically high binary fraction. The lack of agreement near the main-sequence turnoff suggests specific physical changes to the stellar evolution models, including a lower mass for the Kraft break and potentially enhanced main-sequence lifespans for rapidly rotating stars.
Comparative analysis of error mitigation techniques for variational quantum eigensolver implementations on IBM quantum system

Quantum computers are anticipated to transcend classical supercomputers for computationally intensive tasks by exploiting the principles of quantum mechanics. However, the capabilities of the current generation of quantum devices are limited due to noise or errors, and therefore implementation of error mitigation and/or correction techniques is pivotal to reliably process quantum algorithms. In this work, we have performed a comparative analysis of the error mitigation capability of the [[4,2,2]] quantum error-detecting code (QEC method), duplicate circuit technique, and the Bayesian read-out error mitigation (BREM) approach in the context of proof-of-concept implementations of variational quantum eigensolver (VQE) algorithm for determining the ground state energy of H 2 molecule. Based on experiments on IBM quantum device, our results show that the duplicate circuit approach performs superior than the QEC method in the presence of the hardware noise. A signiﬁcant impact of cross-talk noise was observed when multiple mappings of the duplicate circuit and the QEC method were implemented simultaneously – again the duplicate circuit approach overall performed better than the QEC method. To gain further insights into the performance of the studied error mitigation techniques, we also performed quantum simulations on IBM system with varying strengths of depolarising circuit noise and read-out errors which further supported the main ﬁnding of our work that the duplicate circuit oﬀer superior performance towards mitigating of errors when compared to the QEC method. Our work reports a ﬁrst assessment of the duplicate circuit approach for a quantum algorithm implementation and the documented evidence will pave the way for future scalable implementations of the duplicated circuit techniques for the error-mitigated practical applications of near-term quantum computers. our results provide key new insights into the working of the duplicate circuit, the QEC method and BREM technique for noise mitigation in quantum chemistry simulations which is anticipated as one of the killer applications of quantum computing.
Sudoku number of graphs

We introduce a new concept in graph coloring motivated by the popular Sudoku puzzle. Let $G=(V,E)$ be a graph of order $n$ with chromatic number $\chi(G)=k$ and let $S\subseteq V. $ Let $\mathscr C_0$ be a $k$-coloring of the induced subgraph $G[S]. $ The coloring $\mathscr C_0$ is called an extendable coloring if $\mathscr C_0$ can be extended to a $k$-coloring of $G. $ We say that $\mathscr C_0$ is a Sudoku coloring of $G$ if $\mathscr C_0$ can be uniquely extended to a $k$-coloring of $G. $ The smallest order of such an induced subgraph $G[S]$ of $G$ which admits a Sudoku coloring is called the Sudoku number of $G$ and is denoted by $sn(G). $ In this paper we initiate a study of this parameter. We first show that this parameter is related to list coloring of graphs. In Section 2, basic properties of Sudoku coloring that are related to color dominating vertices, chromatic numbers and degree of vertices, are given. Particularly, we obtained necessary conditions for $\mathscr C_0$ being uniquely extendable, and for $\mathscr C_0$ being a Sudoku coloring. In Section 3, we determined the Sudoku number of various familes of graphs. Particularly, we showed that a connected graph $G$ has $sn(G)=1$ if and only if $G$ is bipartite. Consequently, every tree $T$ has $sn(T)=1$. Moreover, a graph $G$ with small chromatic number may have arbitrarily large Sudoku number. Extendable coloring and Sudoku coloring are nice tools for providing a $k$-coloring of $G$.
FixEval: Execution-based Evaluation of Program Fixes for Competitive Programming Problems

Source code repositories consist of large codebases, often containing error-prone programs. The increasing complexity of software has led to a drastic rise in time and costs for identifying and ﬁxing these defects. Various methods exist to automatically generate ﬁxes for buggy code. However, due to the large combinatorial space of possible solutions for a particular bug, there are not many tools and datasets available to evaluate generated code effectively. In this work, we introduce F IX E VAL , a benchmark comprising of buggy code submissions to competitive programming problems and their respective ﬁxes. F IX E VAL contains a rich test suite to evaluate and assess the correctness of model-generated program ﬁxes, in addition to further information regarding time and memory constraints and acceptance based on a verdict. We consider two Transformer language models pretrained on programming languages as our baselines, and compare them using match-based and execution-based evaluation metrics. Our experiments show that match-based metrics do not reﬂect model-generated program ﬁxes accurately, while execution-based methods evaluate programs through all cases and scenarios speciﬁcally designed for that solution. Therefore, we believe F IX E VAL provides a step towards real-world automatic bug ﬁxing and model-generated code evaluation. Dataset and models are open-sourced at https://github.com/mahimanzum/FixEval .
Text normalization for low-resource languages: the case of Ligurian

Text normalization is a crucial technology for low-resource languages which lack rigid spelling conventions or that have undergone multiple spelling reforms. Low-resource text normalization has so far relied upon hand-crafted rules, which are perceived to be more data efficient than neural methods. In this paper we examine the case of text normalization for Ligurian, an endangered Romance language. We collect 4,394 Ligurian sentences paired with their normalized versions, as well as the first open source monolingual corpus for Ligurian. We show that, in spite of the small amounts of data available, a compact transformer-based model can be trained to achieve very low error rates by the use of backtranslation and appropriate tokenization.
Polynomial point counts and odd cohomology vanishing on moduli spaces of stable curves

We compute the number of F_q-points on M_{4,n}, for n less than or equal to 3, and show that it is a polynomial in q, using a sieve based on Hasse-Weil zeta functions. As an application, we prove that the rational singular cohomology groups of moduli spaces of stable curves of genus g with n marked points vanish in all odd degrees less than or equal to 9, for all g and n. Both results confirm predictions of the Langlands program, via the conjectural correspondence with polarized algebraic cuspidal automorphic representations of conductor 1, which are classified in low weight. Our vanishing result for odd cohomology resolves a problem posed by Arbarello and Cornalba in the 1990s.
Real-World Single Image Super-Resolution Under Rainy Condition

Image super-resolution is an important research area in computer vision that has a wide variety of applications including surveillance, medical imaging etc. Real-world signal image super-resolution has become very popular now-a-days due to its real-time application. There are still a lot of scopes to improve real-world single image super-resolution specially during challenging weather scenarios. In this paper, we have proposed a new algorithm to perform real-world single image superresolution during rainy condition. Our proposed method can mitigate the influence of rainy conditions during image superresolution. Our experiment results show that our proposed algorithm can perform image super-resolution decreasing the negative effects of the rain. Keywords—image super-resolution, rainy condition, deep
When a RF Beats a CNN and GRU, Together - A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification

—Internet trafﬁc classiﬁcation is widely used to facilitate network management. It plays a crucial role in Quality of Services (QoS), Quality of Experience (QoE), network visibility, intrusion detection, and trafﬁc-trend anal-yses. While there is no theoretical guarantee that deep learning (DL)-based solutions perform better than classic machine learning (ML)-based ones, DL-based models have become the common default. This paper compares well-known DL-based and ML-based models and shows that in the case of malicious trafﬁc classiﬁcation, state-of-the-art DL-based solutions do not necessarily outperform the classical ML-based ones. We exemplify this ﬁnding using two well-known datasets for a varied set of tasks, such as: malware detection, malware family classiﬁcation, detection of zero-day attacks, and classiﬁcation of an iteratively growing dataset. Note that, it is not feasible to evaluate all possible models to make a concrete statement, thus, the above ﬁnding is not a recommendation to avoid DL-based models, but rather empirical proof that in some cases, there are more simplistic solutions, that may perform even better.
Thermodynamic origins of topological protection in nonequilibrium stochastic systems

Topological protection has emerged as an organizing principle for understanding and engineering robust collective behavior in electronic and material systems. Recent work suggests that topology may also play a role in organizing stochastic processes relevant to biology and self-assembly. Here, we show that topological protection in chemical networks can be understood entirely in terms of nonequilibrium thermodynamics. We illustrate these ideas using simple examples inspired by the literature.
Sample generation for the spin-fermion model using neural networks

Quantum Monte-Carlo simulations of hybrid quantum-classical models such as the double exchange Hamiltonian require calculating the density of states of the quantum degrees of freedom at every step. Unfortunately, the computational complexity of exact diagonalization grows O ( N 3 ) as a function of the system’s size N , making it prohibitively expensive for any realistic system. We con-sider leveraging data-driven methods, namely neural networks, to replace the exact diagonalization step in order to speed up sample generation. We explore a model that learns the free energy for each spin conﬁguration and a second one that learns the Hamiltonian’s eigenvalues. We implement data augmentation by taking advantage of the Hamiltonian’s symmetries to artiﬁcially enlarge our training set and benchmark the diﬀerent models by evaluating several thermodynamic quantities. While all models considered here perform exceedingly well in the one-dimensional case, only the neural network that outputs the eigenvalues is able to capture the right behavior in two dimensions. The simplicity of the architecture we use in conjunction with the model agnostic form of the neural networks can enable fast sample generation without the need of a researcher’s intervention.
Physics-Infused Fuzzy Generative Adversarial Network for Robust Failure Prognosis

Prognostics aid in the longevity of fielded systems or products. Quantifying the system's current health enable prognosis to enhance the operator's decision-making to preserve the system's health. Creating a prognosis for a system can be difficult due to (a) unknown physical relationships and/or (b) irregularities in data appearing well beyond the initiation of a problem. Traditionally, three different modeling paradigms have been used to develop a prognostics model: physics-based (PbM), data-driven (DDM), and hybrid modeling. Recently, the hybrid modeling approach that combines the strength of both PbM and DDM based approaches and alleviates their limitations is gaining traction in the prognostics domain. In this paper, a novel hybrid modeling approach for prognostics applications based on combining concepts from fuzzy logic and generative adversarial networks (GANs) is outlined. The FuzzyGAN based method embeds a physics-based model in the aggregation of the fuzzy implications. This technique constrains the output of the learning method to a realistic solution. Results on a bearing problem showcases the efficacy of adding a physics-based aggregation in a fuzzy logic model to improve GAN's ability to model health and give a more accurate system prognosis.
Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data. However, the requirement of simultaneous access to labeled source and unlabeled target renders them unsuitable for the challenging source-free DA setting. The trivial solution of realizing an effective original to generic domain mapping improves transferability but degrades task discriminability. Upon analyzing the hurdles from both theoretical and empirical standpoints, we derive novel insights to show that a mixup between original and corresponding translated generic samples enhances the discriminability-transferability trade-off while duly respecting the privacy-oriented source-free setting. A simple but effective realization of the proposed insights on top of the existing source-free DA approaches yields state-of-the-art performance with faster convergence. Beyond single-source, we also outperform multi-source prior-arts across both classification and semantic segmentation benchmarks.
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is covisible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple down-stream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios11Project page: https://people.csail. mit.edu/weichium/virtual-correspondence/.
First line raltegravir/emtricitabine/tenofovir combination in HIV-2 infection: phase 2 non-comparative trial (ANRS 159 HIV-2)

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. First line raltegravir/emtricitabine/tenofovir combination in HIV-2 infection: phase 2 non-comparative trial (ANRS 159 HIV-2) S. Matheron, D. Descamps, S. Gallien, Amel Besseghir, P. Sellier, L. Blum, E. Mortier, C. Charpentier, R. Tubiana, F. Damond, et al.
Enhancing Performance of Advanced Encryption Standard for Data Security

: : Information technology has been part and parcel of the contemporary world. Data transmission is considered to be a significant pillar for it. In processing the data, data transmission has improved dramatically. It improved to complex system, where one can find advantages and disadvantages. In fact, these disadvantages are concerning the confidentiality of the data. It became a subject of debate recently. The matter that argued for a several encryption algorithms to secure crucial data against hacking. The target of this research is to tackle Advanced Encryption Standard (AES), comparing and contrasting it with prominent valid algorithms to enhance security range. With the aim to minimize processing time, it takes five rounds instead of ten, concurrently, preserving the maximum security. The comparison is in terms of efficiency, key size, complexity and time consumed. The performance evaluation is by MATLAB R2016b.
Program death-1 (PD-1) signaling and regulatory T cells (Tregs) collaborate to resist the function of adoptively transferred cytotoxic T lymphocytes (CTLs) in advanced acute myeloid leukemia (AML) Running title: PD-1 blockade and adoptive CTL therapy for AML

Masonic Cancer Center and Department of Pediatrics, Division of Blood and Marrow Transplantation and Department of Therapeutic Radiology, University of Minnesota, Minneapolis, MN; Department of Pathology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA; Department of Molecular Immunology, Tokyo Medical and Dental University, Tokyo, Japan; Abramson Family Cancer Research Institute, University of Pennsylvania Cancer Center, Philadelphia, PA; Department of Dermatology, University of California, Davis ,CA; and the Department of Pediatrics, School of Medicine, Medical College of Georgia, Augusta, Georgia. Corresponding author: Bruce R Blazar, M.D.; Department of Pediatrics MMC 109; University of Minnesota, Twin Cities; Minneapolis, MN 55455 E-mail address: blaza001@umn.edu; Phone number: 612-626-1926 Fax number: 612-624-3913 Blood First Edition Paper, prepublished online June 22, 2010; DOI 10.1182/blood-2010-03-275446
AREGU March 47/3

Chen, Chiung-Tong, Ling-Ling Hwang, Jaw-Kang Chang, and Nae J. Dun. Pressor effects of orexins injected intracisternally and to rostral ventrolateral medulla of anesthetized rats. Am. J. Physiol. Regulatory Integrative Comp. Physiol. 278: R692–R697, 2000. —Orexin A and B, two recently isolated hypothalamic peptides, have been reported to increase food consumption upon intracerebroventricular injections in rats. In addition to the hypothalamus, orexin Aimmunoreactive fibers have been observed in several areas of the medulla that are associated with cardiovascular functions. The present study was undertaken to evaluate the hypothesis that orexins may influence cardiovascular response by interacting with neurons in the medulla. Intracisternal injections of orexins A (0.0056–7.0 nmol) or B (0.028– 0.28 nmol) dose dependently increased mean arterial pressure (MAP) by 4–27 mmHg and heart rate (HR) by 26–80 beats/ min in urethan-anesthetized rats, with orexin A being more effective in this regard. MAP and HR were not changed by intravenous injection of orexins at higher concentrations. Microinjection of orexin A (14 pmol/50.6 nl) to the rostral ventrolateral medulla, which was confirmed by histological examination, increased MAP and HR. Our results indicate that, in addition to a role in positive feeding behavior, orexins may enhance cardiovascular response via an action on medullary neurons.
Security of Blockchain Consensus Protocols

—The blockchain is a decentralised technology distributing digital information through peer-to-peer, where the consensus protocol remains the most signiﬁcant part ensuring the integrity of the recorded information. The consensus works as an agreement among the network nodes determining the authenticity of the network peers and also puts forward a set of rules. Nodes that do not comply with the consensus rules, fail to take part in the network activities. However, the major consensus protocols comprise severe weaknesses allowing malicious parties to conduct activities that are against the network rules. Although blockchain is based upon a sturdy structure solving many security issues, the robustness of it is still severely affected by various attack techniques. Most of the attacks were possible due to the weaknesses in the adopted consensus protocol. Many security proposals evolved to defend against the vulnerability but fully failed to minimise the attacking possibilities encouraging attackers even more to conduct such exploitation. In this research, we analyse 19 important consensus protocols that are adopted by major cryptocurrencies. We also discuss the most dreadful consensus-based attacks and major defense mechanisms. Our analysis shows that the weaknesses in the consensus protocol result in signiﬁcant attacks.