Reviewerly/exHarmony huggingface dataset:
Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learning

5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of deep learning and computer vision tools has the means to increase the environmental awareness of the network with information from visual data. Information extracted via computer vision tools such as user position, movement direction, and speed can be promptly available for the network. However, the network must have a mechanism to match the identity of a user in both visual and radio systems. This mechanism is absent in the present literature. Therefore, we propose a framework to match the information from both visual and radio domains. This is an essential step to practical applications of computer vision tools in communications. We detail the proposed framework training and deployment phases for a presented setup. We carried out practical experiments using data collected in different types of environments. The work compares the use of Deep Neural Network and Random Forest classifiers and shows that the former performed better across all experiments, achieving classification accuracy greater than 99%.
Generation and storage of spin squeezing via learning-assisted optimal control

The generation and storage of spin squeezing is an attractive topic in quantum metrology and the foundation of quantum mechanics. The major models to realize the spin squeezing are the one- and two-axis twisting models. Here, we consider a collective spin system coupled to a bosonic field, and show that proper constant-value controls in this model can simulate the dynamical behavior of these two models. More interestingly, a better performance of squeezing can be obtained when the control is time varying, which is generated via a reinforcement learning algorithm. However, this advantage becomes limited if the collective noise is involved. To deal with this, we propose a four-step strategy for the construction of a type of combined controls, which include both constant-value and time-varying controls, but performed at different time intervals. Compared to the full time-varying controls, the combined controls not only give a comparable minimum value of the squeezing parameter over time, but also provide a better lifetime and larger full amount of squeezing. Moreover, the amplitude form of a combined control is simpler and more stable than the full time-varying control. Therefore, our scheme is very promising to be applied in practice to improve the generation and storage performance of squeezing.
Profiling the Chinese causative construction with<i>rang</i>(讓),<i>shi</i>(使) and<i>ling</i>(令) using frame semantic features

Abstract This behavioural profiling (BP) study examines the use of the near-synonyms rang (讓), shi (使) and ling (令), three ways to express cause-effect relationships in Chinese. Instead of using an out-of-the-box BP design, we present a modified approach to profiling that includes a range of frame semantic features that aim to capture variation of slot fillers of this construction. The study investigates the intricate semantic variation of rang , shi and ling through a comprehensive analysis of 38 contextual features (ID tags) that characterize the collocational, lexical semantic and frame semantic environment of the near-synonyms. Our dataset consists of around 100.000 data points based on the annotation of 1002 sentences of Mandarin Chinese of three varieties. The BPs of each near-synonym are compared using multidimensional scaling and hierarchical cluster analysis. The results show that rang , shi and ling are each characterized by a combination of distinctive features and how different feature types contribute to setting the near-synonyms apart based on their usage patterns. Methodologically, this study illustrates how behavioural profiling can be modified to include frame semantic features in accordance with the method’s emphasis on producing empirically verifiable results and how these features can aid a comparative analysis of near-synonyms.
Self-supervised Discovery of Anatomical Shape Landmarks

Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.
DartsReNet: Exploring New RNN Cells in ReNet Architectures

We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architecture, which is a RNN based approach presented as an alternative for convolutional and pooling steps. ReNet can be defined using any standard RNN cells, such as LSTM and GRU. One limitation is that standard RNN cells were designed for one dimensional sequential data and not for two dimensions like it is the case for image classification. We overcome this limitation by using DARTS to find new cell designs. We compare our results with ReNet that uses GRU and LSTM cells. Our found cells outperform the standard RNN cells on CIFAR-10 and SVHN. The improvements on SVHN indicate generalizability, as we derived the RNN cell designs from CIFAR-10 without performing a new cell search for SVHN. (The source code of our approach and experiments is available at https://github.com/LuckyOwl95/DartsReNet/.).
VVS: Fast Similarity Measuring of FoV-Tagged Videos

In this article, we propose a novel method that can measure the similarity of FoV-tagged videos in two dimensions. Recently many researchers have focused on measuring the similarity of FoV-tagged videos. The similarity measurement of FoV-tagged videos plays an important role in various societal applications, including urban road networks, traffic, and geographic information systems. Our preliminary work introduced the Largest Common View Subsequences (LCVS) algorithm for computing the similarity of FoV-tagged videos. However, LCVS requires a high computational cost for calculating common viewable regions between two FoV-tagged videos. To handle this limitation, we propose the largest View Vector Subsequence (VVS) algorithm for reducing the computational cost of FoV-tagged videos. VVS uses the movement distances and the viewable direction distances to support the simplified vector-based similarity measurement. We demonstrate the superiority of our approach by comparing it with the Longest Common Subsequences (LCSS) and our prior work (LCVS). Our experimental results show that VVS outperforms the prior work in terms of the computational cost and enhances the versatility and stability of the similarity measurement.
ABID: Angle Based Intrinsic Dimensionality

The intrinsic dimensionality refers to the "true" dimensionality of the data, as opposed to the dimensionality of the data representation. For example, when attributes are highly correlated, the intrinsic dimensionality can be much lower than the number of variables. Local intrinsic dimensionality refers to the observation that this property can vary for different parts of the data set; and intrinsic dimensionality can serve as a proxy for the local difficulty of the data set.
SDF-MAN: Semi-Supervised Disparity Fusion with Multi-Scale Adversarial Networks

Refining raw disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships among pixels limit the accuracy and robustness of existing methods and there is no standard method for fusion of different kinds of depth data. In this paper, we introduce a new method to fuse disparity maps from different sources, while incorporating supplementary information (intensity, gradient, etc.) into a refiner network to better refine raw disparity inputs. A discriminator network classifies disparities at different receptive fields and scales. Assuming a Markov Random Field for the refined disparity map produces better estimates of the true disparity distribution. Both fully supervised and semi-supervised versions of the algorithm are proposed. The approach includes a more robust loss function to inpaint invalid disparity values and requires much less labeled data to train in the semi-supervised learning mode. The algorithm can be generalized to fuse depths from different kinds of depth sources. Experiments explored different fusion opportunities: stereo-monocular fusion, stereo-ToF fusion and stereo-stereo fusion. The experiments show the superiority of the proposed algorithm compared with the most recent algorithms on public synthetic datasets (Scene Flow, SYNTH3, our synthetic garden dataset) and real datasets (Kitti2015 dataset and Trimbot2020 Garden dataset).
Long-lived quantum speedup based on plasmonic hot spot systems

Abstract Long-lived quantum speedup serves as a fundamental component for quantum algorithms. The quantum walk is identified as an ideal scheme to realize the long-lived quantum speedup. However, one finds that the duration of quantum speedup is too short in real systems to implement the quantum algorithms, for instance the speedup in the photosynthetic light-harvesting systems can last only dozens of femtoseconds. Here, we construct one plasmonic system with two-level molecules embodied in the hot spots of one-dimensional nanoparticle chains to realize the long-lived quantum speedup. The coherent and incoherent coupling parameters in the system are obtained by means of Green’s tensor technique. Our results reveal that the duration of quantum speedup in our scheme can exceed 500 fs under strong coherent coupling conditions. Moreover, our plasmonic system has far prospect in realizing high-dimensional quantum walk, which is very beneficial for quantum algorithms.
The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning

Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P-T probability framework, which is assembled with Shannon's statistical probability framework for communication, Kolmogorov's probability axioms for logical probability, and Zadeh's membership functions used as truth functions. Two kinds of probabilities are connected by an extended Bayes' theorem, with which we can convert a likelihood function and a truth function from one to another. Hence, we can train truth functions (in logic) by sampling distributions (in statistics). This probability framework was developed in the author's long-term studies on semantic information, statistical learning, and color vision. This paper first proposes the P-T probability framework and explains different probabilities in it by its applications to semantic information theory. Then, this framework and the semantic information methods are applied to statistical learning, statistical mechanics, hypothesis evaluation (including falsification), confirmation, and Bayesian reasoning. Theoretical applications illustrate the reasonability and practicability of this framework. This framework is helpful for interpretable AI. To interpret neural networks, we need further study.
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution $\pi$ and a source distribution $p_z$ both defined on $\mathbb{R}^d$, we prove under some assumptions that there exists a deep neural network $g:\mathbb{R}^d\rightarrow \mathbb{R}$ with ReLU activation such that the push-forward measure $(\nabla g)_\# p_z$ of $p_z$ under the map $\nabla g$ is arbitrarily close to the target measure $\pi$. The closeness are measured by three classes of integral probability metrics between probability distributions: $1$-Wasserstein distance, maximum mean distance (MMD) and kernelized Stein discrepancy (KSD). We prove upper bounds for the size (width and depth) of the deep neural network in terms of the dimension $d$ and the approximation error $\varepsilon$ with respect to the three discrepancies. In particular, the size of neural network can grow exponentially in $d$ when $1$-Wasserstein distance is used as the discrepancy, whereas for both MMD and KSD the size of neural network only depends on $d$ at most polynomially. Our proof relies on convergence estimates of empirical measures under aforementioned discrepancies and semi-discrete optimal transport.
Entanglement production by statistical operators

In the problem of entanglement there exist two different notions. One is the entanglement of a quantum state, characterizing the state structure. The other is entanglement production by quantum operators, describing the action of operators in the given Hilbert space. Entanglement production by statistical operators, or density operators, is an important notion arising in quantum measurements and quantum information processing. The operational meaning of the entangling power of any operator, including statistical operators, is the property of the operators to entangle wave functions of the Hilbert space they are defined on. The measure of entanglement production by statistical operators is described and illustrated by entangled quantum states, equilibrium Gibbs states, as well as by the state of a complex multiparticle spinor system. It is shown that this measure is in intimate relation to other notions of quantum information theory, such as the purity of quantum states, linear entropy, or impurity, inverse participation ratio, quadratic R\'{e}nyi entropy, the correlation function of composite measurements, and decoherence phenomenon. This measure can be introduced for a set of statistical operators characterizing a system after quantum measurements. The explicit value of the measure depends on the type of the Hilbert space partitioning. For a general multiparticle spinor system, it is possible to accomplish the particle-particle partitioning or spin-spatial partitioning. Conditions are defined showing when entanglement production is maximal and when it is zero. The study on entanglement production by statistical operators is important because, depending on whether such an operator is entangling or not, it generates qualitatively different probability measures, which is principal for quantum measurements and quantum information processing.
Sequential neural networks for noetic end-to-end response selection

The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper presents our systems that are ranked top 1 on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among different turns’ utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking top 1 in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, on two large-scale public multi-turn response selection benchmark datasets.
Enhancing quantum annealing performance by a degenerate two-level system

Abstract Quantum annealing is an innovative idea and method for avoiding the increase of the calculation cost of the combinatorial optimization problem. Since the combinatorial optimization problems are ubiquitous, quantum annealing machine with high efficiency and scalability will give an immeasurable impact on many fields. However, the conventional quantum annealing machine may not have a high success probability for finding the solution because the energy gap closes exponentially as a function of the system size. To propose an idea for finding high success probability is one of the most important issues. Here we show that a degenerate two-level system provides the higher success probability than the conventional spin-1/2 model in a weak longitudinal magnetic field region. The physics behind this is that the quantum annealing in this model can be reduced into that in the spin-1/2 model, where the effective longitudinal magnetic field may open the energy gap, which suppresses the Landau–Zener tunneling providing leakage of the ground state. We also present the success probability of the Λ-type system, which may show the higher success probability than the conventional spin-1/2 model.
Hamiltonian assignment for open quantum systems

This paper proposes methods to inferentially assign a Hamiltonian to describe the unknown interactions between constituent particles of select open quantum systems. The authors' inferential protocol constructs the model Hamiltonians through a minimization procedure which involves static and dynamic correlation matrices, which are themselves obtained from a polynomial number of measurements
The Existence of Perfect Codes in Doob Graphs

We solve the problem of existence of perfect codes in the Doob graph. It is shown that 1-perfect codes in the Doob graph D(m, n) exist if and only if 6m + 3n + 1 is a power of 2; that is, if the size of a 1-ball divides the number of vertices.
Parareal with a learned coarse model for robotic manipulation

Abstract A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Code ( https://doi.org/10.5281/zenodo.3779085 ) and videos ( https://youtu.be/wCh2o1rf-gA ) are publicly available.
Exact dynamics of concurrence-based entanglement in a system of four spin-1/2 particles on a triangular ladder structure

Motivated by the ability of triangular spin ladders to implement quantum information processing, we propose a type of such systems whose Hamiltonian includes the XX Heisenberg interaction on the rungs and DzyaloshinskiiMoriya (DM) coupling over the legs. In this work, we discuss how tuning the magnetic interactions between elements of a nanomagnetic cell of a triangular ladder which contains four qubits influences on the dynamical behavior of entanglement shared between any pairs of the system. In this work, we make use of concurrence for monitoring entanglement. It is realized that the generation of quantum W states is an important feature of the present model when the system evolves unitarily with time. In general, coincidence with the emergence of W states, the concurrences of all pairs are equal to $N/2$, where N is the number of system's qubits. We also obtain the precise relationship between the incidence of such states and the value of DM interaction as well as the time of entanglement transfer. Finally, by studying the two-point quantum correlations and expectation values of different spin variables, we find that xx and yy correlations bring the entanglement to a maximum value for W states, whereas for these states, zz correlation between any pairs completely quenches. Our results reveal that although $S^{tot}_{z}$ does not commute with the system's Hamiltonian, its expectation value remains constant during time evolution which is a generic property of quantum W states.
Quantum jump statistics with a shifted jump operator in a chiral waveguide

Resonance fluorescence, consisting of light emission from an atom driven by a classical oscillating field, is well-known to yield a sub-Poissonian photon counting statistics.This occurs when only emitted light is detected, which corresponds to a master equation (ME) unraveling in terms of the canonical jump operator describing spontaneous decay.Formally, an alternative ME unraveling is possible in terms of a shifted jump operator.We show that this shift can result in sub-Poissonian, Poissonian or super-Poissonian quantum jump statistics.This is shown in terms of the Mandel Q parameter in the limit of long counting times, which is computed through large deviation theory.We present a waveguide-QED setup, comprising a chiral waveguide coupled to a driven atom, where photon counting is described by the considered class of shifted jump operators.