Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault-tolerant Quantum Computation with Constant Error Rate *

Shor has showed how to perform fault tolerant quantum computation when the probability for an error in a qubit or a gate, η, decays with the size of the computation polylogarithmically, an assumption which is physically unreasonable. This paper improves this result and shows that quantum computation can be made robust against errors and inaccuracies, when the error rate, η, is smaller than a constant threshold, ηc. The cost is polylogarithmic in space and time. The result holds for a very general noise model, which includes probabilistic errors, decoherence, amplitude damping, depolarization, and systematic inaccuracies in the gates. Moreover, we allow exponentially decaying correlations between the errors both in space and in time. Fault tolerant computation can be performed with any universal set of gates. The result also holds for quantum particles with p > 2 states, namely qupits, and is also generalized to one dimensional quantum computers with only nearest neighbor interactions. No measurements, or classical operations, are required during the quantum computation. We use Calderbank-Shor Steane (CSS) quantum error correcting codes, generalized to qupits, and introduce a new class of CSS codes over Fp, called polynomial codes. It is shown how to apply a universal set of gates fault tolerantly on states encoded by general CSS codes, based on modifications of Shor's procedures, and on states encoded by polynomial codes, where the procedures for polynomial codes have a simple and systematic structure based on the algebraic properties of the code. Geometrical and group theoretical arguments are used to prove the universality of the two sets of gates. Our key theorem asserts that applying computation on encoded states recursively achieves fault tolerance against constant error rate. The generalization to general noise models is done using the framework of quantum circuits with density matrices. Finally, we calculate the threshold to be ηc ≃ 10 −6 , in the best case. The paper contains new and significantly simpler proofs for most of the known results which we use. For example, we give a simple proof that it suffices to correct bit and phase flips, we significantly simplify Calderbank and Shor's original proof of the correctness of CSS codes. We also give a simple proof of the fact that two-qubit gates are universal. The paper thus provides a self contained and complete proof for universal fault tolerant quantum computation. 1. Outline. Quantum computation has recently gained a lot of attention, due to …
τ -U-FACTORIZATIONS AND THEIR GRAPHS IN COMMUTATIVE RINGS WITH ZERO-DIVISORS

Let R be a commutative ring with identity, and let τ be a relation on the nonzero, non-unit elements of R. In this paper we generalize the defi- nitions of a factorization and a U-factorization via a relation τ and construct a variety of graphs based on these generalizations. These graphs are then examined in an effort to determine ring-theoretic properties.
M-estimation with the Trimmed l1 Penalty

We study high-dimensional estimators with the trimmed $\ell_1$ penalty, which leaves the $h$ largest parameter entries penalty-free. While optimization techniques for this nonconvex penalty have been studied, the statistical properties have not yet been analyzed. We present the first statistical analyses for $M$-estimation and characterize support recovery, $\ell_\infty$ and $\ell_2$ error of the trimmed $\ell_1$ estimates as a function of the trimming parameter $h$. Our results show different regimes based on how $h$ compares to the true support size. Our second contribution is a new algorithm for the trimmed regularization problem, which has the same theoretical convergence rate as the difference of convex (DC) algorithms, but in practice is faster and finds lower objective values. Empirical evaluation of $\ell_1$ trimming for sparse linear regression and graphical model estimation indicate that trimmed $\ell_1$ can outperform vanilla $\ell_1$ and non-convex alternatives. Our last contribution is to show that the trimmed penalty is beneficial beyond $M$-estimation, and yields promising results for two deep learning tasks: input structures recovery and network sparsification.
Modelisation et inversion de la forme d'onde en domaine frequentiel : applications a la tomographie multi-offsets

Je presente dans cette these le developpement d'une technique de modelisation et d'inversion de la forme d'onde en domaine frequence-espace dans des milieux visco-acoustiques avec applications a l'imagerie sismique. Le calcul de champs d'ondes repose sur un schema implicite de differences finies qui requiert la resolution d'un systeme lineaire. Ceci est accompli, au prix d'un cout numerique eleve, par une methode directe de factorisation lu qui permet un calcul rapide des solutions de l'equation d'onde lorsque de nombreuses sources sismiques sont impliquees. Le cout est cependant fortement attenue par l'adoption d'un algorithme de reordonnancement du systeme base sur une methode de degre minimum. Concernant le probleme inverse, une approche d'optimisation par critere de moindres carres est adoptee. La convergence vers le minimum de la fonction cout repose sur une technique de gradient. La principale difficulte a la resolution du probleme inverse reside dans sa non linearite. Celle-ci est plus particulierement marquee pour les donnees enregistrees a grands offsets, ainsi qu'il est montre. Il en resulte une limitation dans la reconstruction des bas nombres d'ondes d'un modele de vitesses sismiques. La formulation en domaine frequentiel du probleme inverse permet d'attenuer la non linearite en inversant iterativement les donnees par frequences croissantes. Elle permet en outre une reduction tres significative du volume de donnees a traiter. Des limitations subsistent neanmoins concernant le macro-modele de vitesses. Deux applications sont presentees. La premiere est un traitement de donnees synthetiques simulant l'imagerie d'un modele geologique par un dispositif d'obh, la seconde porte sur un jeu de donnees reelles de tomographie de diffraction multi-angles.
Convergence and Variance Analysis of Semi-Parametric Weighted Null-Space Fitting

Standard system identification methods often provide biased estimates with closed-loop data. With the prediction error method (PEM), the bias issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) can cause additional numerical problems for PEM. In this paper, we perform a theoretical analysis of the weighted nullspace fitting (WNSF) method when a parametric noise model is not estimated. With this method, the system is first captured using a non-parametric ARX model, which is then reduced to a parametric model of interest. In the reduction step, a noise model does not need to be estimated if it is not of interest. In open loop, this still provides asymptotically efficient estimates of the dynamic model. In closed loop, the estimates are consistent, and their covariance is optimal for a non-parametric noise model. In this paper, we prove these results, which require additional technical details compared with the case with a full parametric model structure. In particular, we use a geometric approach for variance analysis, deriving a new result that will be instrumental to our end. Finally, we use a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.
Stratified Transformer for 3D Point Cloud Segmentation

As shown in Fig. 1, we demonstrate more visualizations of Effective Recetive Field (ERF) [18]. The illustrations are similar to Fig. 1 of the submission file. We follow the definition of ERF in [18] to calculate the gradient of every input point xi with regard to the feature of interest, i.e., ∂y ∂xi , which depicts how much the feature changes as input point xi changes by a small amount. Afterwards, we adopt the colormap CV2.COLORMAP JET for colorization, therefore, red region corresponds to high contribution.
Strategic thinking in turbulent times

Abstract The purpose of this paper is to present a structural analysis of strategic thinking spectrum in turbulent times. Business excellence cannot be achieved without a well-defined strategic thinking spectrum able to elaborate and implement strategies in a fast changeable and unpredictable business environment. Strategic thinking means to think for a desirable future which can be ahead 4-5 years of the present time and to make decisions to the best of our knowledge for that unknown business environment. Thus, the research question is: How can we conceive the spectrum of strategic thinking such that we shall be able to deal with a complex and unknown future in achieving a competitive advantage? The methodology used to answer this question is based on metaphorical thinking, and multidimensional analysis. I shall consider four main dimensions: time, complexity, uncertainty, and novelty. On each of these dimensions I shall analyze the known thinking models and their attributes with respect to request formulated in the research question. Then, I shall choose those thinking models that correspond to the future characteristics and integrate them in a continuous spectrum. On each dimension I shall consider three basic thinking models. On the time dimension they are: inertial, dynamic and entropic thinking. On the complexity dimension they are: linear, nonlinear and systemic thinking. On the uncertainty dimension they are: deterministic, probabilistic and chaotic thinking. Finally, on the novelty dimension we have: template, intelligent and creative thinking. Considering all requirements for the unknown future, we conclude that strategic thinking spectrum should contain: entropic, nonlinear and systemic, probabilistic and chaotic, intelligent and creative thinking models. Such a spectrum increases the capacity of our understanding and as a consequence it enhances the capability of making adequate decisions in conditions of complexity and uncertainty.
Hybrid Particle Swarm Optimization Based Optimal Reactive Power Dispatch

In this paper, a two-phase hybrid particle swarm optimization (PSO) approach is used to solve optimal reactive power dispatch (ORPD) problem. In this hybrid approach, PSO is used to explore the optimal region and direct search is used as local optimization technique for finer convergence. The performance of the proposed hybrid approach is demonstrated with the IEEE 30-bus and IEEE 57-bus systems and also the performance of this hybrid PSO is compared with that of PSO, Evolutionary Programming (EP) and hybrid EP. The performance of the proposed method is compared with the previous approaches reported in the literature. The performance of hybrid PSO seems to be better in terms of solution quality and computational time.
A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations.
Mobile Power Network for Ultimate Mobility without Battery Life Anxiety

Similar to the evolution from the wired Internet to mobile Internet (MI), the growing demand for power delivery anywhere and anytime appeals for power grid transformation from wired to mobile domain. We propose here the next generation of power delivery network -- mobile power network (MPN) for wireless power transfer within a mobile range from several meters to tens of meters. At first, we present the MPN's concept evolution and application scenarios. Then, we introduce the MPN's supporting technology, namely resonant beam charging (RBC). As a long-range wireless power transfer (WPT) method, RBC can safely deliver multi-Watt power to multiple devices concurrently. Meanwhile, the recent progress in RBC research has been summarized. Next, we specify the MPN's architecture to provide the wide-area WPT coverage. Finally, we discuss the MPN's features and challenges. MPN can enable the ultimate mobility by cutting the final cord of mobile devices, realizing the "last-mile" mobile power delivery.
Terahertz cavity cooling for cuprate superconductors

Optical cooling is a well-established class of methods for reducing the temperature of a wide variety of atomic, mechanical and condensed matter systems. In this thesis, we demonstrate that it may be possible to cool the fluctuations of the Josephson phase in a cuprate single crystal by the application of coherent terahertz radiation. Such cooling strengthens the superconducting state as measured by switching current distribution, and may lead to an enhancement of the transition temperature T c We develop a parametric cooling scheme for bilayer cuprates in which intense terahertz driving produces a modulation in the dielectric material properties. This modulates the coupling between interlayer and intralayer Josephson plasmons which we exploit to upconvert thermal fluctuations of the Josephson phase and reduce their effective temperature. We quantify this as the suppression of the fluctuations in both position and momentum quadratures, and additionally via simulated switching current measurements. We predict a reduction in temperature of 30p given realistic material parameters, and identify the cuprates YBCO and TBCCO-2201 as candidate materials in which this effect may be observed experimentally. With a view to developing cavity cooling schemes, we study the coupling between the Josephson modes and the electromagnetic modes of an external cavity. Following a review of the literature, we examine in detail a specific microcavity array geometry which is expected to provide a large coupling through minimisation of the mode volume. Numerical calculations using finite element methods and an effective dielectric model suggest that strong coupling with g / ω p T 0.2 can be achieved using this geometry.
Hierarchical Actor -

The ability to learn at different resolutions in time may help overcome one of the main challenges in deep reinforcement learning — sample efficiency. Hierarchical agents that operate at different levels of temporal abstraction can learn tasks more quickly because they can divide the work of learning behaviors among multiple policies and can also explore the environment at a higher level. In this paper, we present a novel approach to hierarchical reinforcement learning called Hierarchical Actor-Critic (HAC) that enables agents to learn to break down problems involving continuous action spaces into simpler subproblems belonging to different time scales. HAC has two key advantages over most existing hierarchical learning methods: (i) the potential for faster learning as agents learn short policies at each level of the hierarchy and (ii) an end-to-end approach. We demonstrate that HAC significantly accelerates learning in a series of tasks that require behavior over a relatively long time horizon and involve sparse rewards.
Myelin: An asynchronous, message-driven parallel framework for extreme-scale deep learning

—In the last few years, the memory requirements to train state-of-the-art neural networks have far exceeded the DRAM capacities of modern hardware accelerators. This has necessitated the development of efﬁcient algorithms to train these neural networks in parallel on large-scale GPU-based clusters. Since computation is relatively inexpensive on modern GPUs, designing and implementing extremely efﬁcient communication in these parallel training algorithms is critical for extracting the maximum performance. This paper presents Myelin, a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU, thereby reducing GPU idle time and maximizing hardware efﬁciency. By using the CPU memory as a scratch space for ofﬂoading data periodically during training, Myelin is able to reduce GPU memory consumption by four times. This allows us to increase the number of parameters per GPU by four times, thus reducing the amount of communication and increasing performance by over 13%. When tested against large transformer models with 12–100 billion parameters on 48–384 NVIDIA Tesla V100 GPUs, Myelin achieves a per-GPU throughput of 49.4– 54.78% of theoretical peak and reduces the training time by 22-37 days (15–25% speedup) as compared to the state-of-the-art.
Embedding NP and Relation phrases Canonicalization using Clustering Source Documents OpenKB Construction

Open Information Extraction (OpenIE)methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manually-defined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) – a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI’s effectiveness.
We start by defining the recurrent architecture as implemented in S OCKEYE , following

We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks. SOCKEYE also supports a wide range of optimizers, normalization and regularization techniques, and inference improvements from current NMT literature. Users can easily run standard training recipes, explore different model settings, and incorporate new ideas. In this paper, we highlight SOCKEYE’s features and benchmark it against other NMT toolkits on two language arcs from the 2017 Conference on Machine Translation (WMT): English–German and Latvian–English. We report competitive BLEU scores across all three architectures, including an overall best score for SOCKEYE’s transformer implementation. To facilitate further comparison, we release all system outputs and training scripts used in our experiments. The SOCKEYE toolkit is free software released under the Apache 2.0 license.
Neural Argument Generation Augmented with Externally Retrieved Evidence (Supplementary Material)

Bootstrapping Algorithm for Building Domain Classifier. As described in Section 3 of the paper, we aim to use a domain classifier to identify discussion threads related to politics. We leverage the Wikipedia abstracts to automatically construct the training set. We first collect 1,285,705 abstracts from DBPedia.1 Then we manually create two lists of politics keywords and non-politics keywords, as shown in Table 1. Based on the keywords, we automatically label Wikipedia abstracts as politics or non-politics by matching keywords with their titles. The resultant dataset contains 264,670 positive samples and 827,437 negative samples. A logistic regression model is trained on this dataset with unigram TF-IDF scores as features. Initially, we observe a good amount of false negative results when tested on CMV posts. We thus continue training the model by adding training samples from CMV that are classified with high confidence. This procedure stops after three iterations when reasonably good performance is observed based on human inspection. The model labels 12,549 threads as politics and 13,731 as non-politics.
LOSSY WEIGHT ENCODING FOR DEEP NEURAL NETWORK COMPRESSION

The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496× with the same model accuracy. This results in up to a 1.51× improvement over the state-of-the-art.
Router RouterRouter Router Router Attacker Stratum 2 Stratum 1 Stratum 7 Stratum 8 Stratum 6 Stratum 5 Stratum 3 Stratum

Recently, several works conjectured the vulnerabilities of mainstream blockchains under several network attacks. All these attacks translate into showing that the assumptions of these blockchains can be violated in theory or under simulation at best. Unfortunately, previous results typically omit both the nature of the network under which the blockchain code runs and whether blockchains are private, consortium or public. In this paper, we study the public Ethereum blockchain as well as a consortium and private blockchains and quantify the feasibility of man-in-the-middle and double spending attacks against them. To this end, we list important properties of the Ethereum public blockchain topology, we deploy VMs with constrained CPU quantum to mimic the top-10 mining pools of Ethereum and we develop full-fledged attacks, that first partition the network through BGP hijacking or ARP spoofing before issuing a Balance Attack to steal coins. Our results demonstrate that attacking Ethereum is remarkably devastating in a consortium or private context as the adversary can multiply her digital assets by 200, 000× in 10 hours through BGP hijacking whereas it would be almost impossible in a public context.