Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical realization of one-dimensionalgeneralized split-step quantum walks

Quantum walks are more than tools for building quantum algorithms. They have been used effectively to model and simulate quantum dynamics in many complex physical processes. Particularly, a variant of discrete-time quantum walk known as split-step quantum walk is closely related to Dirac cellular automata and topological insulators whose realizations rely on position-dependent control of evolution operators. Owing to the ease of manipulating multiple degrees of freedom of photons, we provide an optical setup of split-step operators which in combination with position-dependent coin (PDC) operation can accomplish a table-top setup of generalized split-step walks. Also, we propose an optical implementation for PDC operation that allows, for instance, to realize electric quantum walks, control localization dynamics, and emulate space-time curvature effects. In addition, we propose a setup to realize {\it any} $t$-step split-step quantum walk involving 2 $J$-plates, 2 variable waveplates, a half-waveplate, an optical switch, and an optical delay line.
A very sharp threshold for first order logic distinguishability of random graphs

In this paper we ﬁnd an integer h = h ( n ) such that the minimum number of variables of a ﬁrst order sentence that distinguishes between two independent uniformly distributed random graphs of size n with the asymptotically largest possible probability 14 − o (1) belongs to { h, h + 1 , h + 2 , h + 3 } . We also prove that the minimum (random) k such that two independent random graphs are distinguishable by a ﬁrst order sentence with k variables belongs to { h, h + 1 , h + 2 } with probability 1 − o (1).
Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks

Multi-goal policy learning for robotic manipu-lation is challenging. Prior successes have used state-based representations of the objects or provided demonstration data to facilitate learning. In this paper, by hand-coding a high-level discrete representation of the domain, we show that policies to reach dozens of goals can be learned with a single network using Q-learning from pixels. The agent focuses learning on simpler, local policies which are sequenced together by planning in the abstract space. We compare our method against standard multi-goal RL baselines, as well as other methods that leverage the discrete representation, on a challenging block construction domain. We find that our method can build more than a hundred different block structures, and demonstrate forward transfer to structures with novel objects. Lastly, we deploy the policy learned in simulation on a real robot.
A"Galactic Disk"-Model for Three-Dimensional Bernstein-Greene-Kruskal Modes in a Finite Magnetic Field

A new model, inspired by the structure of galactic disks, for three-dimensional Bernstein-Greene-Kruskal (BGK) modes in a plasma with a uniform finite background magnetic field is presented. These modes are exact nonlinear solutions of the steady-state Vlasov equation, with an electric potential and a magnetic potential perturbation localized in all three spatial dimensions that satisfies the Poisson equation, and the Ampere Law, self-consistently. The existence of solutions is shown analytically in the limit of small electric and magnetic field perturbations associated with the disk species, and numerically using an iterative method that converges up to moderately strong field perturbations.
Lie structure of the Heisenberg-Weyl algebra

As an associative algebra, the Heisenberg--Weyl algebra $\HWeyl$ is generated by two elements $A$, $B$ subject to the relation $AB-BA=1$. As a Lie algebra, however, where the usual commutator serves as Lie bracket, the elements $A$ and $B$ are not able to generate the whole space $\HWeyl$. We identify a non-nilpotent but solvable Lie subalgebra $\coreLie$ of $\HWeyl$, for which, using some facts from the theory of bases for free Lie algebras, we give a presentation by generators and relations. Under this presentation, we show that, for some algebra isomorphism $\isoH:\HWeyl\into\HWeyl$, the Lie algebra $\HWeyl$ is generated by the generators of $\coreLie$, together with their images under $\isoH$, and that $\HWeyl$ is the sum of $\coreLie$, $\isoH(\coreLie)$ and $\lbrak \coreLie,\isoH(\coreLie)\rbrak$.
Tilting Uranus via Spin–Orbit Resonance with Planet Nine

Uranus’s startlingly large obliquity of 98° has yet to admit a satisfactory explanation. The most widely accepted hypothesis involving a giant impactor that tipped Uranus onto its side encounters several difficulties with regard to Uranus’s spin rate and prograde satellite system. An obliquity increase that was driven by capture of Uranus into a secular spin–orbit resonance remains a possible alternative hypothesis that avoids many of the issues associated with a giant impact. We propose that secular spin–orbit resonance could have excited Uranus’s obliquity to its present-day value if it was driven by the outward migration of an as-yet-undetected outer solar system body commonly known as Planet Nine. We draw support for our hypothesis from an analysis of 123 N-body simulations with varying parameters for Planet Nine and its migration. We find that in multiple instances, a simulated Planet Nine drives Uranus’s obliquity past 98°, with a significant number falling within 10% of this value. We note a significant caveat to our results in that a much faster than present-day spin axis precession rate for Uranus is required in all cases for it to reach high obliquities. We conclude that while it was, in principle, possible for Planet Nine (if it exists) to have been responsible for Uranus’s obliquity, the feasibility of such a result hinges on Uranus’s primordial precession rate.
A FUZZY RULE - BASED PREDICTION THE COMPETITIVE STRENGTH INDEX OF BRAND PRODUCT

A fuzzy model of brand competitive strength index is proposed. The model takes into account 10 factors via 52 fuzzy rules. The paper also consists of a problem statement of fuzzy model tuning on the basis of an experimental data set. Examples show applications of the model for the prediction of the brand competitiveness index of Ukrainian vodkas as well as for an optimal management of the brand products.
Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning

In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand spacetime association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.
μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications

This paper presents $\mu\text{KG}$, an open-source Python library for representation learning over knowledge graphs. $\mu\text{KG}$ supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embedding tasks (link prediction, entity alignment, entity typing, and multi-source link prediction), and multiple parallel computing modes (multi-process and multi-GPU computing). It currently implements 26 popular knowledge graph embedding models and supports 16 benchmark datasets. $\mu\text{KG}$ provides advanced implementations of embedding techniques with simplified pipelines of different tasks. It also comes with high-quality documentation for ease of use. $\mu\text{KG}$ is more comprehensive than existing knowledge graph embedding libraries. It is useful for a thorough comparison and analysis of various embedding models and tasks. We show that the jointly learned embeddings can greatly help knowledge-powered downstream tasks, such as multi-hop knowledge graph question answering. We will stay abreast of the latest developments in the related fields and incorporate them into $\mu\text{KG}$.
Path Tracing in 2D, 3D, and Physicalized Networks

It is common to advise against using 3D to visualize abstract data such as networks, however Ware and Mitchell's 2008 study showed that path tracing in a network is less error prone in 3D than in 2D. It is unclear, however, if 3D retains its advantage when the 2D presentation of a network is improved using edge-routing, and when simple interaction techniques for exploring the network are available. We address this with two studies of path tracing under new conditions. The first study was preregistered, involved 34 users, and compared 2D and 3D layouts that the user could rotate and move in virtual reality with a handheld controller. Error rates were lower in 3D than in 2D, despite the use of edge-routing in 2D and the use of mouse-driven interactive highlighting of edges. The second study involved 12 users and investigated data physicalization, comparing 3D layouts in virtual reality versus physical 3D printouts of networks augmented with a Microsoft HoloLens headset. No difference was found in error rate, but users performed a variety of actions with their fingers in the physical condition which can inform new interaction techniques.
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation

This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative methods lack the training data and representation capacity to synthesize plausible, fine-grained details with complex geometry and topology. Our key insight is to copy and deform patches from the partial input to complete missing regions. This enables us to preserve the style of local geometric features, even if it drastically differs from the training data. Our fully automatic approach proceeds in two stages. First, we learn to retrieve candidate patches from the input shape. Second, we select and deform some of the retrieved candidates to seamlessly blend them into the complete shape. This method combines the advantages of the two most common completion methods: similarity-based single-instance completion, and completion by learning a shape space. We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps. Experimental results show our approach considerably outperforms baselines across multiple datasets and shape categories. Code and data are available at https://github.com/GitBoSun/PatchRD.
Simultaneous Inference for Time Series Functional Linear Regression

We consider the problem of joint simultaneous conﬁdence band (JSCB) construction for regression coeﬃcient functions of time series scalar-on-function linear regression when the regression model is estimated by roughness penalization approach with ﬂexible choices of orthonormal basis functions. A simple and uniﬁed multiplier bootstrap methodology is proposed for the JSCB construction which is shown to achieve the correct coverage probability asymptotically. Furthermore, the JSCB is asymptotically robust to inconsistently estimated standard deviations of the model. The proposed methodology is applied to a time series data set of electricity market to visually investigate and formally test the overall regression relationship as well as perform model validation. A uniform Gaussian approximation and comparison result over all Euclidean convex sets for normalized sums of a class of moderately high-dimensional stationary time series is established. Finally, the proposed methodology can be applied to simultaneous inference for scalar-on-function linear regression of independent cross-sectional data.
A general-purpose method for applying Explainable AI for Anomaly Detection

The need for explainable AI (XAI) is well established but relatively little has been published outside of the supervised learning paradigm. This paper focuses on a principled approach to applying explainability and interpretability to the task of unsupervised anomaly detection. We argue that explainability is principally an algorithmic task and interpretability is principally a cognitive task, and draw on insights from the cognitive sciences to propose a general-purpose method for practical diagnosis using explained anomalies. We deﬁne Attribution Error, and demonstrate, using real-world labeled datasets, that our method based on Integrated Gradients (IG) yields signiﬁcantly lower attribution errors than alternative methods. and may even make an AD solution ineﬀective. A nondescript anomaly score provides little insight to understanding what caused the anomaly and choosing the best treatment. We hypothesize that integrating XAI techniques with AD can reduce the human workload in processing a stream of anomalies.
Unsupervised Hebbian Learning on Point Sets in StarCraft II

Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of a point set in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection

Fake news detection has become a major task to solve as there has been an increasing number of fake news on the internet in recent years. Although many classification models have been proposed based on statistical learning methods showing good results, reasoning behind the classification performances may not be enough. In the self-supervised learning studies, it has been highlighted that a quality of representation (embedding) space matters and directly affects a downstream task performance. In this study, a quality of the representation space is analyzed visually and analytically in terms of linear separability for different classes on a real and fake news dataset. To further add interpretability to a classification model, a modification of Class Activation Mapping (CAM) is proposed. The modified CAM provides a CAM score for each word token, where the CAM score on a word token denotes a level of focus on that word token to make the prediction. Finally, it is shown that the naive BERT model topped with a learnable linear layer is enough to achieve robust performance while being compatible with CAM.
Some results on maximum likelihood from incomplete data: finite sample properties and improved M-estimator for resampling

This paper presents some results on the maximum likelihood (ML) estimation from incomplete data. Finite sample properties of conditional observed information matrices are established. They possess positive definiteness and the same Loewner partial ordering as the expected information matrices do. An explicit form of the observed Fisher information (OFI) is derived for the calculation of standard errors of the ML estimates. It simplifies Louis (1982) general formula for the OFI matrix. To prevent from getting an incorrect inverse of the OFI matrix, which may be attributed by the lack of sparsity and large size of the matrix, a monotone convergent recursive equation for the inverse matrix is developed which in turn generalizes the algorithm of Hero and Fessler (1994) for the Cram\'er-Rao lower bound. To improve the estimation, in particular when applying repeated sampling to incomplete data, a robust M-estimator is introduced. A closed form sandwich estimator of covariance matrix is proposed to provide the standard errors of the M-estimator. By the resulting loss of information presented in finite-sample incomplete data, the sandwich estimator produces smaller standard errors for the M-estimator than the ML estimates. In the case of complete information or absence of re-sampling, the M-estimator coincides with the ML estimates. Application to parameter estimation of a regime switching conditional Markov jump process is discussed to verify the results. The simulation study confirms the accuracy and asymptotic properties of the M-estimator.
Bigraded Betti numbers and Generalized Persistence Diagrams

Commutative diagrams of vector spaces and linear maps over (cid:90) 2 are objects of interest in topological data analysis (TDA) where this type of diagrams are called 2-parameter persistence modules. Given that quiver representation theory tells us that such diagrams are of wild type, studying informative invariants of a 2-parameter persistence module M is of central importance in TDA. One of such invariants is the generalized rank invariant, recently introduced by Kim and Mémoli. Via the Möbius inversion of the generalized rank invariant of M , we obtain a collection of connected subsets I ⊂ (cid:90) 2 with signed multiplicities. This collection generalizes the well known notion of persistence barcode of a persistence module over (cid:82) from TDA. In this paper we show that the bigraded Betti numbers of M , a classi-cal algebraic invariant of M , are obtained by counting the corner points of these subsets I s. Along the way, we verify that an invariant of 2-parameter persistence modules called the interval decomposable approximation (introduced by Asashiba et al.) also encodes the bigraded Betti numbers in a similar fashion.
A Graph-Based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.
FAT-PIM: Low-Cost Error Detection for Processing-In-Memory

Processing In Memory (PIM) accelerators are promising architecture that can provide massive parallelization and high efficiency in various applications. Such architectures can instantaneously provide ultra-fast operation over extensive data, allowing real-time performance in data-intensive workloads. For instance, Resistive Memory (ReRAM) based PIM architectures are widely known for their inherent dot-product computation capability. While the performance of such architecture is essential, reliability and accuracy are also important, especially in mission-critical real-time systems. Unfortunately, the PIM architectures have a fundamental limitation in guaranteeing error-free operation. As a result, current methods must pay high implementation costs or performance penalties to achieve reliable execution in the PIM accelerator. In this paper, we make a fundamental observation of this reliability limitation of ReRAM based PIM architecture. Accordingly, we propose a novel solution--Falut Tolerant PIM or FAT-PIM, that can improve reliability for such systems significantly at a low cost. Our evaluation shows that we can improve the error tolerance significantly with only 4.9% performance cost and 3.9% storage overhead.
S IGNALING AND S CREENING OF W ORKERS ’ M OTIVATION

This paper develops a model in which workers to a certain extent enjoy working. We examine the implications of workers’ intrinsic motivation for optimal monetary incentive schemes. We show that motivated workers work harder and, for a given level of e.ort, are willing to work for a lower wage. When people di.er in their motivation to work at a particular firm, the profits of the firm depend on its capability to attract and select highly motivated workers. We show that when the firm has all the bargaining power and workers face application cost, the firm needs to commit to a minimum wage o.er in order to attract workers. A higher minimum wage increases the probability to fill the vacancy, but decreases the expected average quality of job applicants, as it induces lower motivated workers to apply. The optimal level of the minimum wage depends on whether or not the firm can observe the motivation of the applicants. If applicants can credibly signal their motivation, a minimum wage not only helps to attract workers, but also to select the best-motivated worker among the job applicants.
Optimal joint RAN selection and resource allocation strategy in HetNet under Co-channel and user centric deployment

In this article, a joint Radio Access Network selection strategy and a sub-channel resource allocation scheme are introduced, where the downlink of a two-tier HetNet system is deployed in a co-channel deployment. With this scenario in mind, a Semi Markovian Decision Process based-model is proposed to find an optimal policy for user association the channel allocation, where the long-term expected reward is maximized. For the proposed strategy, the sub-channel allocation is only considered for those new and handoff petitions that potentially can be allocated in one of the SCs. In addition, with the aim to contribute to the enhancement of the overall usage of radio resources, a traffic offloading process is considered at the departure of the sessions. In order to motivate the session offloading from the MC to the SCs at departure times, we have defined that two sub-channels will be allocated in the SC for each offloaded session. Analytical results quantify and show the effectiveness of the proposed strategy.