Reviewerly/exHarmony huggingface dataset:
Semimodules and the (syntactically-)linear lambda calculus

In a recent paper, the $\mathcal L^{\mathcal S}$-calculus has been defined. It is a proof-language for a significant fragment of intuitionistic linear logic. Its main feature is that the linearity properties can be expressed in its syntax, since it has interstitial logical rules whose proof-terms are a sum and a multiplication by scalar. The calculus is parametrized on the structure $\mathcal S$. This structure was originally identified with the field of complex numbers, since the calculus is designed as a quantum lambda calculus. However, in this paper we show that a semiring is enough, and we provide a categorical semantics for this calculus in the category of cancellative semimodules over the given semiring. We prove the semantics to be sound and adequate.
Quantum Extremal Learning

We propose a quantum algorithm for `extremal learning', which is the process of finding the input to a hidden function that extremizes the function output, without having direct access to the hidden function, given only partial input-output (training) data. The algorithm, called quantum extremal learning (QEL), consists of a parametric quantum circuit that is variationally trained to model data input-output relationships and where a trainable quantum feature map, that encodes the input data, is analytically differentiated in order to find the coordinate that extremizes the model. This enables the combination of established quantum machine learning modelling with established quantum optimization, on a single circuit/quantum computer. We have tested our algorithm on a range of classical datasets based on either discrete or continuous input variables, both of which are compatible with the algorithm. In case of discrete variables, we test our algorithm on synthetic problems formulated based on Max-Cut problem generators and also considering higher order correlations in the input-output relationships. In case of the continuous variables, we test our algorithm on synthetic datasets in 1D and simple ordinary differential functions. We find that the algorithm is able to successfully find the extremal value of such problems, even when the training dataset is sparse or a small fraction of the input configuration space. We additionally show how the algorithm can be used for much more general cases of higher dimensionality, complex differential equations, and with full flexibility in the choice of both modeling and optimization ansatz. We envision that due to its general framework and simple construction, the QEL algorithm will be able to solve a wide variety of applications in different fields, opening up areas of further research.
FINDING DATA DEDUPLICATION USING CLOUD

Data grows at the emotional rate of 50% per time, and 75% of the digital world is a copy1 Although keeping multiple clones of data is necessary to guarantee their availability and high continuity and the quantum of data redundancy is inordinate. By keeping a single dupe of repeated data, data deduplication is one of the most promising results to reduce the storage costs, and improve users experience by saving network bandwidth and reducing provisory time. However, this result must now solve many security issues to be fully satisfying. In this project we target the attacks from malicious clients that are grounded on the manipulation of data identifiers and those based on backup time and network traffic observation. Our system provides global storage space savings, per-customer bandwidth network savings between clients and deduplication proxies, and saving global network bandwidth between deduplication proxies and the storage server. The evaluation of our result compared to a classic system shows that the overhead introduced by our scheme is mostly due to data encryption which is necessary to ensure data confidentiality. Data deduplication allows the cloud users to manage their cloud storage space for storing effectively by avoiding storage of repeated data’s and save bandwidth. Here we use the Cloud Me for the data storage. For data confidentiality the data are stored in an encrypted form using Advanced Encryption Standard (AES) algorithm.
TV and Video Game Streaming with a Quantum Receiver: A Study on a Rydberg atom-based receivers bandwidth and reception clarity

We demonstrate the ability to receive live color analog television and video game signals with the use of the Rydberg atom receiver. The typical signal expected for traditional 480i NSTC format video signals requires a bandwidth of over 3 MHz. We determine the beam sizes, powers, and detection method required for the Rydberg atoms to receive this type of signal. The beam size affects the average time the atoms remain in the interaction volume, which is inversely proportional to the bandwidth of the receiver. We find that small beam diameters (less than 100 {\mu}m) lead to much faster responses and allow for color reception. We demonstrate the effect of beam size on bandwidth by receiving a live 480i video stream with the Rydberg atom receiver. The best fidelity was achieved with a beam width of 85 {\mu}m full-width at half-max.
A Periscopic IT Outlook

Presents the introductory editorial for this issue of the publication.
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition

Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. In particular, we form ensemble methods based on deep representations and handcrafted 3D shape descriptors. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios. For the evaluation purpose, in addition to real object datasets, we generate a large synthetic household objects dataset consisting of 27000 views of 90 objects. Experimental results demonstrate the effectiveness of the proposed method on online few-shot 3D object recognition tasks, as well as its superior performance over the state-of-the-art open-ended learning approaches. Furthermore, our results show that while ensemble learning is modestly beneficial in offline settings, it is significantly beneficial in lifelong few-shot learning situations. Additionally, we demonstrated the effectiveness of our approach in both simulated and real-robot settings, where the robot rapidly learned new categories from limited examples.
Video Extrapolation in Space and Time

Novel view synthesis (NVS) and video prediction (VP) are typically considered disjoint tasks in computer vision. However, they can both be seen as ways to observe the spatial-temporal world: NVS aims to synthesize a scene from a new point of view, while VP aims to see a scene from a new point of time. These two tasks provide complementary signals to obtain a scene representation, as viewpoint changes from spatial observations inform depth, and temporal observations inform the motion of cameras and individual objects. Inspired by these observations, we propose to study the problem of Video Extrapolation in Space and Time (VEST). We propose a model that leverages the self-supervision and the complementary cues from both tasks, while existing methods can only solve one of them. Experiments show that our method achieves performance better than or comparable to several state-of-the-art NVS and VP methods on indoor and outdoor real-world datasets.
Blockchain-Based Crowdfunding Platform for Disaster Relief and Effective Charity

Charity plays an essential role in our society, and often recognized as a type of social debt, leading to the circulation of a significant amount of money globally. We have seen a increased growth of organizations and public charity funds through recent years, collecting donations for various philanthropic needs. Unfortunately, most of the charity funds frequently gain much funds from the unethical organization, resulting in significant damage for industry's reputation, which results in reducing trust level which affects the ability to raise donations. We strongly believe that utilizing this technology will boost trust , increase efficiency, and encourage more donations. The Charity project, a blockchain-based charity foundation platform that facilitates the trustful network's formation and is accountable for collecting donation funds. The blockchain network contains publically recognized, trustful, and prestigious organizations. The complete system will be decentralised using Blockchain Technology, Smart Contracts and Cryptocurrency. We strongly believe that utilizing our technology will boost trust , increase efficiency, and encourage more donations. All organization’s operations and functions within the platform will become fully transparent and visible, leveraging properties of immutability, provenance, and non- repudiation to the users. All organizations operations and functions within the platform will become fully transparent and visible, therefore the platform will reduce the results of dishonest actions, revealing fraudulent organizations' activity.
Reproducing Kernels and New Approaches in Compositional Data Analysis

Compositional data, such as human gut microbiomes, consist of non-negative variables whose only the relative values to other variables are available. Analyzing compositional data such as human gut microbiomes needs a careful treatment of the geometry of the data. A common geometrical understanding of compositional data is via a regular simplex. Majority of existing approaches rely on a log-ratio or power transformations to overcome the innate simplicial geometry. In this work, based on the key observation that a compositional data are projective in nature, and on the intrinsic connection between projective and spherical geometry, we re-interpret the compositional domain as the quotient topology of a sphere modded out by a group action. This re-interpretation allows us to understand the function space on compositional domains in terms of that on spheres and to use spherical harmonics theory along with reflection group actions for constructing a compositional Reproducing Kernel Hilbert Space (RKHS). This construction of RKHS for compositional data will widely open research avenues for future methodology developments. In particular, well-developed kernel embedding methods can be now introduced to compositional data analysis. The polynomial nature of compositional RKHS has both theoretical and computational benefits. The wide applicability of the proposed theoretical framework is exemplified with nonparametric density estimation and kernel exponential family for compositional data.
The Ludii Game Description Language is Universal

There are several different game description languages (GDLs), each intended to allow wide ranges of arbitrary games (i.e., general games) to be described in a single higher-level language than general-purpose programming languages. Games described in such formats can subsequently be presented as challenges for automated general game playing agents, which are expected to be capable of playing any arbitrary game described in such a language without prior knowledge about the games to be played. The language used by the Ludii general game system was previously shown to be capable of representing equivalent games for any arbitrary, finite, deterministic, fully observable extensive-form game. In this paper, we prove its universality by extending this to include finite non-deterministic and imperfect-information games.
Research and analysis of pruning algorithm

In recent years, thanks to the development of GPU (Graphic Processing Unit) computing power, deep neural network (DNN) has drawn extensive attention from academia and industry and has been widely used in various fields of artificial intelligence. However, the mainstream deep learning models have some problems such as large storage space consumption and high computational complexity, which makes it difficult for the deep neural network model to be deployed to mobile devices with limited computing resources or applications with strict delay requirements. Therefore, on the premise of minimizing the loss of model accuracy, the algorithm researches of deep neural network compression and acceleration which can lighten the model has been carried out. In recent years, model compression methods such as pruning, quantification and knowledge distillation have emerged. This paper focuses on neural network pruning algorithm. Firstly, this paper expounds the basic methods and classification of neural network pruning algorithms. Then according to the classification of pruning granularity, a more detailed classification, analysis and comparison of specific models are carried out from the algorithm level. Then based on cifar-10 data set, validity tests of some neural network pruning algorithms and contrastive analyses of the experimental data from different models such as the parameters are carried out. Finally, in combination of the recent results of neural network pruning algorithm, we put forward research dilemmas and problems to be solved. Then the future research trends and methods of neural network pruning algorithm are prospected.
Matrix Representation of Arbitrarily Controlled Quantum Gates

Controlled operations allow for the entanglement of quantum registers. In particular, a controlled-$U$ gate allows an operation, $U$, to be applied to the target register and entangle the results to certain values in the control register. This can be generalised by making use of the classical notion of conditional statements, where if a value (or state) satisfies some condition then a sequence of operations can be performed. A method is introduced to represent these generalised controlled operations that are based on classical conditional statements. Throughout examples are given to highlight the use of introduced gates.
Classical and Quantum Solvers for Joint Network/Servers Power Optimization

The digital transformation that Telecommunications and ICT domains are crossing today, is posing several new challenges to Telecom Operators. These challenges require solving complex problems such as: dimensioning and scheduling of virtual/real resources in data centers; automating real-time management/control and orchestration of networks processes; optimizing energy consumption; and overall, ensuring networks and services stability. These problems are usually tackled with methods and algorithms that find suboptimal solutions, for computational efficiency reasons. In this work, we consider a Virtual Data Center scenario where virtual machine consolidation must be performed with joint minimization of network/servers power consumption. For this scenario, we provide an ILP model, the equivalent binary model and the steps towards the equivalent Quadratic Unconstrained Binary Optimization (QUBO) model that is suitable for being solved by means of quantum optimization algorithms. Finally, we compare the computational complexity of classical and quantum solvers from a theoretical perspective.
Solvable non-Hermitian skin effect in many-body unitary dynamic

Studying unitary evolution of bipartite entanglement in a circuit with nearest-neighbor random gates we show that being exact is actually wrong, while being slightly wrong is correct. Deriving a non-unitary description of purity dynamics on qudits we find a transition in the purity relaxation rate whose origin is in the underlying boundary localized eigenmodes -- the skin effect. We provide the full solution of the problem, being one of the simplest iterations of two-site matrices, namely, each is a sum of only two projectors. This leads to rich dynamics influenced by the Jordan normal form of the kernel and, most importantly, a spectrum that is completely discontinuous in the thermodynamic limit. It provides a simple example of how a seemingly innocuous many-body unitary evolution can harbor all oddities that mathematics can offer.
Solve single photon detector problems

Single photon detector(SPD) problems arise in most quantum tasks, especially for measuring states going through high-lost channels. They are particularly prominent in quantum key distribution(QKD), which could be the most significant application in quantum information theory. In recent years, QKD distance has been improved dramatically but is still restricted because the bit error rate(QBER) caused by SPD dark counts will be out of control as the distance increases. If this problem can be solved, QKD can be implemented over arbitrarily long distances. However, previous solutions often result in impractical requirements such as superconductors while they can only reduce the dark count rate to finite low levels. In this paper, we solve SPD problems with today's technologies only. Although it is the no-cloning theorem that prevents a state from being measured multiple times to obtain a more reliable result, we propose a scheme circumventing the no-cloning theorem in certain tasks to allow a single state to be employed several times. The scheme demonstrates that imperfect detectors can provide nearly perfect results, namely, the QBER caused by dark counts can be reduced to arbitrarily low while in the meantime, detective efficiency can be improved to arbitrarily high. Consequently, QKD distance is not limited by the imperfect SPD anymore and can be improved from hundreds of kilometers to thousands without high-technology detectors. Furthermore, similar schemes can be applied for reducing measurement errors or improving the performance of sources. Finally, it is worth noting that although the paper is mainly discussed in the context of QKD, our scheme is an independent scheme that could be employed in other protocols wherever SPD are employed.
User-Centric Gender Rewriting

In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) - first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M2 F0.5. We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users' grammatical gender preferences. We make our code, data, and models publicly available.
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal predictions, although, with only moderate accuracy in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph neural network model for learning to infer unsteady continuum mechanics in problems encompassing a range of length scales and complex boundary geometries. We demonstrate this method on advection problems and incompressible fluid dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our results show good extrapolation to new domain geometries and parameters for long-term temporal simulations. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.
Post-selection in noisy Gaussian boson sampling: part is better than whole

Gaussian boson sampling is originally proposed to show quantum advantage with quantum linear optical elements. Recently, several experimental breakthroughs based on Gaussian boson sampling pointing to quantum computing supremacy have been presented. However, due to technical limitations, the outcomes of Gaussian boson sampling devices are influenced severely by photon loss. Here, we present an efficient and practical method to reduce the negative effect caused by photon loss. With no hardware modifications, our method takes the data post-selection process that discards low-quality data according to our criterion to improve the performance of the final computational results, say part is better than whole. As an example, we show that the post-selection method can turn a GBS experiment that would otherwise fail in a ``non-classical test" into one that can pass that test. Besides improving the robustness of computation results of current GBS devices, this photon loss mitigation method may also benefit the further development of GBS-based quantum algorithms.
QUERY STAND WEB DEVELOPMENT WITH USER AUTHENTICATION

Web development is that the process of making an internet site from the bottom up. Web development is divided into two categories: front-end development (also known as client-side development) and back-end development (also called as Server-side development). Front-end development is that the process of making the content, design, and interaction that a user sees once they open an internet application. HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), and JavaScript are used to do this. Back-end development is in charge of what happens behind the scenes of a website. The Front-End is frequently generated by the Back-End using a database. This is where the info is kept, and without it, the front would be useless. The web's back-end consists of the server that hosts the website, as well as an application for interacting with it
FUTURE OF BLOCKCHAIN TECHNOLOGY AND ITS CHALLENGES

Blockchain, often known as the Internet of Value, is a new technology. And like many other new technologies, no one knows it’s potential, many claims that it’s going to revolutionize the way we live. It will deliver more radical changes than the Internet, with some questioning its significance. Despite dire predictions, blockchain is a game changing, new technology that have the ability to revolutionize the way people conduct business because of its ability to ensure security. The immutability of records is ensured by confidence among unknown individuals, while intermediaries are excluded. The popularity of digital currency, the enormous number of published blockchain studies, all attest to blockchain's importance. This paper provides an overview of blockchain, a rapidly emerging topic that discusses its benefits and downsides, as well as their potential consequences on the Internet, and societies in general.