Reviewerly/exHarmony huggingface dataset:
Latent Nested Nonparametric Priors (with Discussion)

Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and prediction, among others. In this paper, we consider nested processes and study the dependence structures they induce. Dependence ranges between homogeneity, corresponding to full exchangeability, and maximum heterogeneity, corresponding to (unconditional) independence across samples. The popular nested Dirichlet process is shown to degenerate to the fully exchangeable case when there are ties across samples at the observed or latent level. To overcome this drawback, inherent to nesting general discrete random measures, we introduce a novel class of latent nested processes. These are obtained by adding common and group-specific completely random measures and, then, normalizing to yield dependent random probability measures. We provide results on the partition distributions induced by latent nested processes, and develop a Markov Chain Monte Carlo sampler for Bayesian inferences. A test for distributional homogeneity across groups is obtained as a by-product. The results and their inferential implications are showcased on synthetic and real data.
Conjugate gradient-based Takagi-Sugeno fuzzy neural network parameter identification and its convergence analysis

Model identification is divided into two parts: structure identification and parameter identification, and the parameter identification is actually an optimization process. For improving the optimization performance, in this paper, we firstly present a novel conjugate gradient descent method with a modified Armijo-type line search technique to train a Takagi-Sugeno fuzzy neural network model. Numerical simulations are implemented to demonstrate the efficiency of the proposed algorithm. According to the experimental comparisons that are evaluated over 15 classification and 3 regression problems, the advantages of the given method are superior to its another two counterparts. To complement the simulation results and help in establishing a robust fuzzy neural network model, we strictly prove two deterministic convergent behaviors of the presented algorithm, i.e., weak and strong convergence results. They indicate the gradient of the target function with respect to network weights converges to zero and the parameter sequence approaches a fixed optimal point, respectively.
Preparing the HoloLens for user Studies: an Augmented Reality Interface for the Spatial Adjustment of Holographic Objects in 3D Indoor Environments

Augmented reality (AR), the extension of the real physical world with holographic objects provides numerous ways to influence how people perceive and interact with geographic space. Such holographic elements may for example improve orientation, navigation, and the mental representations of space generated through interaction with the environment. As AR hardware is still in an early development stage, scientific investigations of the effects of holographic elements on spatial knowledge and perception are fundamental for the development of user-oriented AR applications. However, accurate and replicable positioning of holograms in real world space, a highly relevant precondition for standardized scientific experiments on spatial cognition, is still an issue to be resolved. In this paper, we specify technical causes for this limitation. Subsequently, we describe the development of a Unity-based AR interface capable of adding, selecting, placing and removing holograms. The capability to quickly reposition holograms compensates for the lack of hologram stability and enables the implementation of AR-based geospatial experiments and applications. To facilitate the implementation of other task-oriented AR interfaces, code examples are provided and commented.
Automated distribution of quantum circuits via hypergraph partitioning

Quantum algorithms are usually described as monolithic circuits, becoming large at modest input size. Near-term quantum architectures can only manage a small number of qubits. We develop an automated method to distribute quantum circuits over multiple agents, minimising quantum communication between them. We reduce the problem to hypergraph partitioning and then solve it with state-of-the-art optimisers. This makes our approach useful in practice, unlike previous methods. Our implementation is evaluated on five quantum circuits of practical relevance.
The resurgence of the linear optics quantum interferometer — recent advances &amp; applications

Linear optics has seen a resurgence for applications in quantum information processing owing to its miniaturisation on-chip, and increase in production efficiency and quality of single photons. Time-bin encodings have also become feasible owing to architectural breakthroughs, and new processing capabilities. Theoretical efforts have found new ways to implement universal quantum computations with linear optics requiring less resources, and to demonstrate the capabilities of linear optics without requiring a universal optical quantum computer.
Fast and Robust Watermarking Method Based on DCT Specific Location

In many studies related to watermarking, spatial-domain methods have a relatively low information-hiding capacity and limited robustness, and transform-domain methods are not applicable in real-time processes because of their considerably high computational time. In this paper, we propose a novel watermarking method based on a discrete cosine transform (DCT), which guarantees robustness and low computational complexity. First, we calculated the DCT coefficient of a specific location. Then, a variation value was calculated according to the embedding bits and quantization steps to modify the coefficient. Last, we embedded watermark bits by directly modifying the pixel values without full-frame DCT. Tests comparing invisibility, robustness, and computational time were conducted for determining the feasibility of the proposed method. The results showed that the proposed method had the faster and the more robust performance than previous studies.
Practical Phase-Modulation Stabilization in Quantum Key Distribution via Machine Learning

In practical implementation of quantum key distributions (QKD), it requires efficient, real-time feedback control to maintain system stability when facing disturbance from either external environment or imperfect internal components. Usually, a "scanning-and-transmitting" program is adopted to compensate physical parameter variations of devices, which can provide accurate compensation but may cost plenty of time in stopping and calibrating processes, resulting in reduced efficiency in key transmission. Here we for the first propose to employ a well known machine learning model, i.e., the Long Short-Term Memory Network (LSTM), to predict those physical parameter variations in advance and actively perform real-time control on corresponding QKD devices. Experimentally, we take the phase-coding scheme as an example and run the LSTM model based QKD system for more than 10 days. Experimental results show that we can keep the same level of quantum-bit error rate as the traditional "scanning-and-transmitting" program by employing our new machine learning method, but dramatically reducing the scanning time and resulting in significantly enhanced key transmission efficiency. Furthermore, our present machine learning model should also be applicable to any other QKD systems using any coding scheme or QKD protocols, and thus seems a very promising candidate in large-scale application of quantum communication network in the near future.
A personality-based emotional model for embodied conversational agents: Effects on perceived social presence and game experience of users

This paper reports on an experiment that investigates the effect of interacting with a personality-driven embodied conversational agent (ECA) on the perceived social presence and game experience of people in a VR social simulator. Furthermore, the dynamics between the different metrics of game experience and social presence of people are explored to determine which game experience metrics are the strongest predictors of perceived social presence in this context. A personality-based emotional model is used for personifying the employed ECA, which governs the manifestation of its non-verbal behaviors. Three experimental conditions manipulating the existence and intensity of non-verbal behaviors exhibited by the ECA were used to investigate the effect of this proposed approach. The results of the experiment with 41 participants indicate that people who were exposed to an extrovert ECA experienced significantly higher levels of behavioral involvement as part of their social presence compared to the other conditions. These results suggest that incorporating personality by means of non-verbal behavior in the emotional model of an ECA influences users perceived feeling of social presence. Furthermore, our results reveal that there is a bidirectional relationship between game experience metrics and perceived social presence of people as each predict the other.
Empirical evidence that concept mapping reduces neurocognitive effort during concept generation for sustainability

Sustainable development is a systems problem that requires a shift in thinking from individual parts to the relationships between them. Enabling those involved in the development process to more quickly think in systems is therefore vital to create more sustainable solutions. However, measuring the ability to think in systems is a challenge because of the limited methods to observe behavior or measure outcomes. Recent advances in neuroimaging offer a radically new approach to measure one's ability to think in systems. The specific technique, called functional near-infrared spectroscopy (fNIRS), captures the change in oxygenated blood in the cortical region of the brain, which is a proxy for elevated cognitive activation. In this study, engineering students (n = 28) performed both concept mapping (i.e., labeling concepts and drawing directional connections between them) and concept listing (i.e., generating a list of concepts without indicating relationships) related to grand challenges for sustainability. fNIRS captured the increase in oxygenated blood along the prefrontal and parietal cortex during both concept mapping and listing tasks. Self-evaluation and concept mapping scores were also captured and compared. Concept mapping led to significantly more concepts compared to concept listing. Concept mapping also required less cognitive resources (i.e., oxygenated blood) to generate more concepts. The region of the brain that received more cognitive resources is also significantly different when participants were using concept listing and mapping. Concept mapping directed more cognitive resources to the regions of the brain associated with cognitive flexibility and decreased cognitive resources in regions associated with visuospatial processing. Self-evaluation of systems thinking was negatively correlated with concept mapping scores, meaning students with high perceived ability produce worse concept maps. Global efficiency of brain networks was positively correlated to concept map scores and more accurately measure performance compared to self-evaluation. This study provides neurocognitive evidence to support the benefits of concept mapping and also demonstrates the added advantages of neuroimaging to study systems thinking.
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture

An energy efficient deep neural network (DNN) learning processor is proposed using direct feedback alignment (DFA). The proposed processor achieves 2.2 × faster learning speed compared with the previous learning processors by the pipelined DFA (PDFA). In order to enhance the energy efficiency by 38.7%, the heterogeneous learning core (LC) architecture is optimized with the 11-stage pipeline data-path. Furthermore, direct error propagation core (DEPC) utilizes random number generators (RNG) to remove external memory access (EMA) caused by error propagation (EP) and improve the energy efficiency by 19.9%. The proposed PDFA based learning processor is evaluated on the object tracking (OT) application, and as a result, it shows 34.4 frames-per-second (FPS) throughput with 1.32 TOPS/W energy efficiency.
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network

We present a deformable generator model to disentangle the appearance and geometric information in purely unsupervised manner. The appearance generator models the appearance related information, including color, illumination, identity or category, of an image, while the geometric generator performs geometric related warping, such as rotation and stretching, through generating displacement of the coordinates of each pixel to obtain the final image. Two generators act upon independent latent factors to extract disentangled appearance and geometric information from image. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments show that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to the other image datasets to facilitate knowledge transfer tasks.
ONNC: A Compilation Framework Connecting ONNX to Proprietary Deep Learning Accelerators

This paper presents ONNC (Open Neural Network Compiler), a retargetable compilation framework designed to connect ONNX (Open Neural Network Exchange) models to proprietary deep learning accelerators (DLAs). The intermediate representations (IRs) of ONNC have one-to-one mapping to ONNX IRs, thus making porting ONNC to proprietary DLAs much simpler than other compilation frameworks such as TVM and Glow especially for hardware with coarse-grained operators that are not part of the generic IRs in the LLVM backend. ONNC also has a flexible pass manager designed to support compiler optimizations at all levels. A docker image of ONNC bundled with a Vanilla backend is released with this paper to enable fast porting to new hardware targets. To illustrate how an ONNC-based toolkit guides our research and development in DLA design, we present a case study on compiler optimizations for activation memory consumption. The study shows that the Best-Fit algorithm with a proposed heuristic and a reordering scheme may act as a near-optimal strategy, getting the memory consumption close to the ideal lower bound in 11 of 12 models from the ONNX model zoo. To our best knowledge, ONNC is the first open source compilation framework that is specially designed to support the ONNX-based models for both commercial and research projects for deep learning applications.
Probabilistic matrix factorization with personalized differential privacy

Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is common that different individuals have different levels of privacy requirements on items. However, traditional differential privacy can only provide a uniform level of privacy protection for all users. In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF). It aims to meet users’ privacy requirements specified at the item-level instead of giving the same level of privacy guarantees for all. We then develop a modified sampling mechanism (with bounded differential privacy) for achieving PDP. We also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the privacy of the PDP-PMF scheme. In addition, we implement our proposed probabilistic matrix factorization schemes both with traditional and with personalized differential privacy (DP-PMF, PDP-PMF). A series of experiments are performed on real datasets to demonstrate the superior performance of PDP-PMF in recommendation quality.
PheValuator: Development and evaluation of a phenotype algorithm evaluator

The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e., determining sensitivity, specificity, and positive predictive value (PPV), is rarely performed. In this study, we propose a tool (PheValuator) to efficiently estimate a complete PA evaluation. We used 4 administrative claims datasets: OptumInsight’s de-identified Clinformatics™ Datamart (Eden Prairie,MN); IBM MarketScan Multi-State Medicaid); IBM MarketScan Medicare Supplemental Beneficiaries; and IBM MarketScan Commercial Claims and Encounters from 2000 to 2017. Using PheValuator involves (1) creating a diagnostic predictive model for the phenotype, (2) applying the model to a large set of randomly selected subjects, and (3) comparing each subject’s predicted probability for the phenotype to inclusion/exclusion in PAs. We used the predictions as a ‘probabilistic gold standard’ measure to classify positive/negative cases. We examined 4 phenotypes: myocardial infarction, cerebral infarction, chronic kidney disease, and atrial fibrillation. We examined several PAs for each phenotype including 1-time (1X) occurrence of the diagnosis code in the subject’s record and 1-time occurrence of the diagnosis in an inpatient setting with the diagnosis code as the primary reason for admission (1X-IP-1stPos). Across phenotypes, the 1X PA showed the highest sensitivity/lowest PPV among all PAs. 1X-IP-1stPos yielded the highest PPV/lowest sensitivity. Specificity was very high across algorithms. We found similar results between algorithms across datasets. PheValuator appears to show promise as a tool to estimate PA performance characteristics.
Robust AdaBoost based ensemble of one-class support vector machines

One-class support vector machine (OCSVM) is a commonly used one-class classification method for tackling novelty detection problems. Unfortunately, employing the traditional AdaBoost on it may not produce satisfying performance when there are outliers within training samples. In this paper, the conventional loss function of AdaBoost is redesigned, i.e., substituting the exponential loss function by a more robust one to enhance the robustness of the traditional AdaBoost based ensemble of OCSVMs. The proposed loss function is defined as the weighted combination of the modified exponential loss function and the squared loss function. The robust AdaBoost based on the proposed loss function is introduced by redesigning the update formulae for the weights of base classifiers and the probability distribution of training samples. The upper bounds of empirical error and generalization error for the robust AdaBoost based ensemble of OCSVMs are derived. Experimental results on the synthetic and benchmark data sets demonstrate that the proposed ensemble method is superior to its related approaches.
A multi-information fusion “triple variables with iteration” inertia weight PSO algorithm and its application

Particle swarm optimization (PSO) has many advantages such as fewer parameters, faster convergence and easy implementation; however, it is also prone to fall into local optimum. Because inertia weight parameters can increase the diversity of particles and effectively overcome this problem, a large number of studies have done on inertia weight strategy since it was put forward, and many different improvement strategies have been put forward, but these improvement strategies still do not make full use of the multi-dimensional information of particles, resulting in limited improvement of PSO performance. In this study, on the one hand, based on the summary of current inertial weight strategies, aiming at the problems existing in the classification of inertial weight strategies, the inertial weight strategies are reclassified. According to the new classification methods, the inertial weight strategies are divided into four categories, including "no variable with iteration", "single variable with iteration", "double variables with iteration" and "triple variables with iteration" inertia weight. On the other hand, in view of the shortcomings of the existing inertial weight improvement strategies, this study proposes a multi-information fusion "triple variables with iteration" inertia weight PSO algorithm (MFTIWPSO), which combines the multi-dimensional information of particle's time (or iteration), particle and dimension (or space). In order to test the optimization performance of our proposed MFTIWPSO, the benchmark functions are used to test the optimization performance, and then the algorithm is used to optimize the parameters of machine learning classifier and classify the biological datasets. The results of two tests show that the proposed MFTIWPSO algorithm has better optimization performance than other optimization algorithms.
Variable neighborhood algebraic Differential Evolution: An application to the Linear Ordering Problem with Cumulative Costs

Algebraic variants of the Differential Evolution (DE) algorithm have been recently proposed to tackle permutation-based optimization problems by means of an algebraic framework, which allows to directly encode the solutions as permutations. The algebraic DE in the permutation space can be characterized by considering different neighborhood definitions such as swapping two adjacent items, swapping any two items, shifting an item to a given position. Here we propose the Variable Neighborhood Differential Evolution for Permutations (VNDEP), which adaptively searches the three neighborhoods together based on a method of dynamic reward. We provide an extensive and systematic analysis of the theoretical tools required in VNDEP, by studying the complexity of the proposed algorithmic components and by introducing the possibility to use a scale factor parameter larger than one. Experiments have been held on a widely used benchmark suite for the Linear Ordering Problem with Cumulative Costs, where VNDEP has been compared with four known permutation-based DE schemes and with respect to the state-of-the-art results for the considered instances. The experiments clearly show that VNDEP systematically outperforms the competitor algorithms and, most impressively, 32 new best known solutions, of the 50 most challenging instances, have been obtained.
Blockchain and catching-up in developing countries: The case of financial inclusion in Africa

Blockchain technology, born as a tool to support Bitcoins (the most popular and controversial cryptocurrency in the world) has set itself in a very short time as a disruptive technology able not only to revolutionize existing businesses but also to create new ones. This work illustrates the main characteristics of the blockchain and its functioning, and then focuses on the potential applications that can be implemented for developing countries. More specifically, the work, considers the case of financial inclusion in Africa, on the basis of the Global Findex 2017 data, the World Bank Survey on financial inclusion. The empirical analysis identifies the weaknesses in the current financial system in Africa and constitues the basis to discuss potential blockchain solutions to reduce the current level of financial exclusion and pursue sustainable development for African countries.