Reviewerly/exHarmony huggingface dataset:
A log-log speedup for exponent one-fifth deterministic integer factorisation

Building on techniques recently introduced by the second author, and further developed by the first author, we show that a positive integer <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper N"> <mml:semantics> <mml:mi>N</mml:mi> <mml:annotation encoding="application/x-tex">N</mml:annotation> </mml:semantics> </mml:math> </inline-formula> may be rigorously and deterministically factored into primes in at most <disp-formula content-type="math/mathml"> \[ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper O left-parenthesis StartFraction upper N Superscript 1 slash 5 Baseline log Superscript 16 slash 5 Baseline upper N Over left-parenthesis log log upper N right-parenthesis Superscript 3 slash 5 Baseline EndFraction right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mfrac> <mml:mrow> <mml:msup> <mml:mi>N</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> <mml:msup> <mml:mi>log</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>16</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>log</mml:mi> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>N</mml:mi> <mml:msup> <mml:mo stretchy="false">)</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mfrac> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:annotation encoding="application/x-tex">O\left ( \frac {N^{1/5} \log ^{16/5} N}{(\log \log N)^{3/5}}\right )</mml:annotation> </mml:semantics> </mml:math> \] </disp-formula> bit operations. This improves on the previous best known result by a factor of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="left-parenthesis log log upper N right-parenthesis Superscript 3 slash 5"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>log</mml:mi> <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>N</mml:mi> <mml:msup> <mml:mo stretchy="false">)</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:annotation encoding="application/x-tex">(\log \log N)^{3/5}</mml:annotation> </mml:semantics> </mml:math> </inline-formula>.
Deep Learning-Based 2D and 3D Human Pose Estimation: A Survey

In the real world, estimation of human pose has gained considerable consideration owed to its diverse application. Here, 2D pose estimation has remarkable research and achieves targeted output however challenges still remain in 3D pose estimation. As deep learning can improve the presentation of human pose estimation, it also brings very closest result. A literature review of deep learning methods for human pose estimation presented and analyzes the methodology used by this paper. It also includes real-world video with crowded scene pose estimation with latest research information. With a methodology-based taxonomy, we sum up and discuss recent works. It also addresses and compares the datasets used in this function. Thus, this survey makes interpretable each phase in the approximation pipeline and help to reader with easy comprehensive information. Future work and Challenges are detected.
Sentence context modulates the neighborhood frequency effect in Chinese reading: Evidence from eye movements.

In the current study, we conducted 2 eye-tracking reading experiments to explore whether sentence context can influence neighbor effects in word recognition during Chinese reading. Chinese readers read sentences in which the targets' orthographic neighbors were either plausible or implausible with the pretarget context. The results revealed that the neighbor effect was influenced by context: The context in the biased condition (where only targets but not neighbors can fit in the pretarget context) evoked a significantly weaker inhibitory neighbor effect than in the neutral condition (where both targets and neighbors can fit in the pretarget context). These results indicate that contextual information can be used to modulate neighbor effects during online sentence reading in Chinese. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Productive education in the mathematical school

The article examines the reasons why mathematical schools (mathschools) have become an important and very productive phenomenon in Russian education in recent decades. A brief description of the modern model of productive education traced back to the established tradition of teaching in mathschools, which can lead to the construction of a reproducible model of productive education not only for specialized teaching of mathematics, but also for other areas of Russian general education
Effects of time delay in no-knowledge quantum feedback control

Abstract The no-knowledge quantum feedback, introduced in Phys. Rev. Lett., 113, 020407 (2014), is a measurement-based feedback protocol for decoherence suppression in a quantum system coupled to noisy environment. By continuously measuring the environmental noise, without directly gathering any information about the system, the decoherence effect can be suppressed by feeding back quantum controls proportional to the measured signal. In the original work, the feedback control was assumed instantaneous, leading to perfect cancellation of noise backaction on the quantum system. However, the instantaneous feedback is difficult to achieve in practice, and close-loop feedback protocols are always associated with finite delayed time. Therefore, in this work, we consider the effects of the delay between the time at which the measurement signal is acquired and the time that such signal is fed back to the system. We investigate the problem with an example of a two-level system (qubit) coupled to a Markovian reservoir, via a Hermitian coupling operator, where a homodyne detection is used to measure the environmental noise. We numerically simulate quantum stochastic trajectories of the qubit and analyse their averaged dynamics. We find that the feedback control with time delay can either enhance or reduce the decoherence effects, depending on whether the delayed time is in-phase or out-of-phase with the unitary dynamics of the qubit system.
Function Contrastive Learning of Transferable Meta-Representations

Few-shot-learning seeks to find models that are capable of fast-adaptation to novel tasks which are not encountered during training. Unlike typical few-shot learning algorithms, we propose a contrastive learning method which is not trained to solve a set of tasks, but rather attempts to find a good representation of the underlying data-generating processes (\emph{functions}). This allows for finding representations which are useful for an entire series of tasks sharing the same function. In particular, our training scheme is driven by the self-supervision signal indicating whether two sets of samples stem from the same underlying function. Our experiments on a number of synthetic and real-world datasets show that the representations we obtain can outperform strong baselines in terms of downstream performance and noise robustness, even when these baselines are trained in an end-to-end manner.
What Makes Multimodal Learning Better than Single (Provably)

The world provides us with data of multiple modalities. Intuitively, models fusingdata from different modalities outperform unimodal models, since more informationis aggregated. Recently, joining the success of deep learning, there is an influentialline of work on deep multimodal learning, which has remarkable empirical resultson various applications. However, theoretical justifications in this field are notablylacking.Can multimodal provably perform better than unimodal? In this paper, we answer this question under a most popular multimodal learningframework, which firstly encodes features from different modalities into a commonlatent space and seamlessly maps the latent representations into the task space. Weprove that learning with multiple modalities achieves a smaller population risk thanonly using its subset of modalities. The main intuition is that the former has moreaccurate estimate of the latent space representation. To the best of our knowledge,this is the first theoretical treatment to capture important qualitative phenomenaobserved in real multimodal applications. Combining with experiment results, weshow that multimodal learning does possess an appealing formal guarantee.
Assumption of the application of block chain technology in engineering project management

Abstract In recent years, along with the rapid development of construction industry, gradually exposed shortcomings existing in the project management process, block chain technology as a new technology, has a decentralized, transparency and traceability, if can block chain technology, introduced in the engineering project management, some existing problems will be improved, such as information back difficulties, poor supervision. Based on this point of view, this paper discusses the application of block chain technology in engineering project management.
High‐Frequency Trading and Financial Time‐Series Prediction with Spiking Neural Networks

A novel new method is introduced - “unsupervised spike learning” - which predicts spikes in price time series instead of price movement and direction. Three rewarding high-frequency trading strategies are developed and backtested by Kang Gao, Wayne Luk, and Stephen Weston.
Temporal Predictive Coding For Model-Based Planning In Latent Space

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. To handle high-dimensional sensory inputs, existing approaches use representation learning to map high-dimensional observations into a lower-dimensional latent space that is more amenable to dynamics estimation and planning. In this work, we present an information-theoretic approach that employs temporal predictive coding to encode elements in the environment that can be predicted across time. Since this approach focuses on encoding temporally-predictable information, we implicitly prioritize the encoding of task-relevant components over nuisance information within the environment that are provably task-irrelevant. By learning this representation in conjunction with a recurrent state space model, we can then perform planning in latent space. We evaluate our model on a challenging modification of standard DMControl tasks where the background is replaced with natural videos that contain complex but irrelevant information to the planning task. Our experiments show that our model is superior to existing methods in the challenging complex-background setting while remaining competitive with current state-of-the-art models in the standard setting.
Threat and Vulnerability Analysis of Cloud Platform: A User Perspective

Cloud technology is a shared pool of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort over the internet. It has enormous advantages but also has many security risks. Cloud users require understanding emerging threats, vulnerabilities, and plan possible countermeasures before transferring their computing, storage, and application to remote locations. Due to its rising threats, identification of the vulnerabilities and most appropriate solution directives to strengthen security in the cloud environment becomes paramount for all operations in it. In our study, we have scrutinized the threats along with vulnerable area and classified them based on three domains from the perspective of users. To mitigate the threats, possible existing countermeasures are listed against each threat in this paper.
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes Deep Learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts Deep feature from several Convolution Neural Network models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using Principal Component Analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an SVM classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47%, 98.32% and 97.87% respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: this https URL
Classification of Demographic Attributes from Facial Image by using CNN

Human facial appearance is emphatically influenced by demo-graphical features like definite age, ethnicity and sexual orientation with each category advance apportioned into categories White, Dark, East Asian, Male, Female, Child (1-18), Young (19-36), Center Age (37-54) and old (55-above). Most subjects share a more comparative appearance with the possess demographic class than with other demographic class. We assess here the precision of automatic facial verification for subjects having a place to changing age, ethnicity, and gender categories. For this reason, we utilize transfer learning technique in which pre-trained convolutional neural network is used for feature mining and to present that our strategy yields a satisfactory execution on person demographics for development of a viable facial recognition system. We have used IMDB data set for training of our system. We have concluded that results on ethnicity group white are relatively lower than other ethnicity group's result. We talk about the outcomes and make recommendations for improving facial image classification over changing demographics, in expansion to the advancement of a framework.
A Review of Human Evaluation for Style Transfer

This paper reviews and summarizes human evaluation practices described in 97 style transfer papers with respect to three main evaluation aspects: style transfer, meaning preservation, and fluency. In principle, evaluations by human raters should be the most reliable. However, in style transfer papers, we find that protocols for human evaluations are often underspecified and not standardized, which hampers the reproducibility of research in this field and progress toward better human and automatic evaluation methods.
Emphatic Algorithms for Deep Reinforcement Learning

Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms can become unstable when combined with function approximation and off-policy sampling - this is known as the ''deadly triad''. Emphatic temporal difference (ETD($\lambda$)) algorithm ensures convergence in the linear case by appropriately weighting the TD($\lambda$) updates. In this paper, we extend the use of emphatic methods to deep reinforcement learning agents. We show that naively adapting ETD($\lambda$) to popular deep reinforcement learning algorithms, which use forward view multi-step returns, results in poor performance. We then derive new emphatic algorithms for use in the context of such algorithms, and we demonstrate that they provide noticeable benefits in small problems designed to highlight the instability of TD methods. Finally, we observed improved performance when applying these algorithms at scale on classic Atari games from the Arcade Learning Environment.
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track.

In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as our basic models. We train each model by 8-fold cross-validation, and additionally train two Graphormer models on the union of training and validation sets with different random seeds. For final submission, we use a naive ensemble for these 18 models by taking average of their outputs. Using our method, our team MachineLearning achieved 0.1200 MAE on test set, which won the first place in KDD Cup graph prediction track.
Introduction

Ron Pelias shows us how to be comfortable with the surprises the readers encounter as they meander through story—or at least how to stop trying to control the journey and focus more on the road; to lead, as some wayfinding scholars have suggested, from the belly rather than the head. A range of scholarly literature has addressed the term and practice of wayfinding. This scholarship includes, but is not limited to, the fields of design, architecture, digital media, engineering, gaming, virtual reality, and in proliferating disciplines of artificial intelligence including smart homes and smart cities. A range of scholarly literature has addressed term and practice of wayfinding. This scholarship includes, but is not limited to, the fields of design, architecture, digital media, engineering, gaming, virtual reality, and in the proliferating disciplines of artificial intelligence including smart homes and smart cities. Deploying critical auto-ethnography as a decolonizing methodology guides her as works to undermine Western essentialisms around space, body, and mind.
MoDist: Motion Distillation for Self-supervised Video Representation Learning.

We present MoDist as a novel method to explicitly distill motion information into self-supervised video representations. Compared to previous video representation learning methods that mostly focus on learning motion cues implicitly from RGB inputs, we show that the representation learned with our MoDist method focus more on foreground motion regions and thus generalizes better to downstream tasks. To achieve this, MoDist enriches standard contrastive learning objectives for RGB video clips with a cross-modal learning objective between a Motion pathway and a Visual pathway. We evaluate MoDist on several datasets for both action recognition (UCF101/HMDB51/SSv2) as well as action detection (AVA), and demonstrate state-of-the-art self-supervised performance on all datasets. Furthermore, we show that MoDist representation can be as effective as (in some cases even better than) representations learned with full supervision. Given its simplicity, we hope MoDist could serve as a strong baseline for future research in self-supervised video representation learning.
A Mobile Ad-hoc Strategy to Enhance ICT Based Education in Zimbabwean Rural Schools

The following topics are dealt with: Internet; educational institutions; mobile computing; decision making; computer aided instruction; health care; educational courses; medical information systems; learning (artificial intelligence); further education.
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network

Incorporating a so-called dynamic in gradient descent methods is widely used in neural net training as it has been broadly observed that, at least empirically, it often leads to significantly faster convergence. At the same time, there are very few theoretical guarantees in the literature to explain this apparent acceleration effect. Even for the classical strongly convex quadratic problems, several existing results only show Polyak's momentum has an accelerated linear rate asymptotically. In this paper, we first revisit the quadratic problems and show a non-asymptotic accelerated linear rate of Polyak's momentum. Then, we provably show that Polyak's momentum achieves acceleration for training a one-layer wide ReLU network and a deep linear network, which are perhaps the two most popular canonical models for studying optimization and deep learning in the literature. Prior work Du at al. 2019 and Wu et al. 2019 showed that using vanilla gradient descent, and with an use of over-parameterization, the error decays as $(1- \Theta(\frac{1}{ \kappa'}))^t$ after $t$ iterations, where $\kappa'$ is the condition number of a Gram Matrix. Our result shows that with the appropriate choice of parameters Polyak's momentum has a rate of $(1-\Theta(\frac{1}{\sqrt{\kappa'}}))^t$. For the deep linear network, prior work Hu et al. 2020 showed that vanilla gradient descent has a rate of $(1-\Theta(\frac{1}{\kappa}))^t$, where $\kappa$ is the condition number of a data matrix. Our result shows an acceleration rate $(1- \Theta(\frac{1}{\sqrt{\kappa}}))^t$ is achievable by Polyak's momentum. All the results in this work are obtained from a modular analysis, which can be of independent interest. This work establishes that momentum does indeed speed up neural net training.
Tourism Application Using Augmented Reality

Tourism is one of the important factors for economic and social development of the cities today. It is important that local and foreign tourists get information about the important areas and places of tourism and how to reach those places. If this information can be retrieved up-to-date and instantaneously, this will significantly increase the satisfaction of the tourists. Use of augmented reality technology for innovation in day-to-day life is getting increasingly broad. This innovation is additionally being applied in the field of the travel industry. Currently, the model of portable mobile application for enhancing the use of technology in the travel industry has been created by utilizing enlarged augmented reality innovation. The necessities, the skills and deficiency of the strategies is portrayed. This application expects to give interactivity to the client when the item gets distinguished so more data can be viewed. Unity 3D software with Vuforia Engine will be used for implementing augmented reality technology and also Android application development tools such as Android Studio will be used for development of the mobile application.