Reviewerly/exHarmony huggingface dataset:
Risk prediction in surgery using case-based reasoning and agent-based modelization

Managing the risks arising from the actions and conditions of the various elements that make up an operating room is a major concern during a surgical procedure. One of the main challenges is to define alert thresholds in a non-deterministic context where unpredictable adverse events occur. In response to this problematic, this paper presents an architecture that couples a Multi-Agent System (MAS) with Case-Based Reasoning (CBR). The possibility of emulating a large number of situations thanks to MAS, combined with analytical data management thanks to CBR, is an original and efficient way of determining thresholds that are not defined a priori. We also compared different similarity calculation methods (Retrieve phase of CBR). The results presented in this article show that our model can manage alert thresholds in an environment that manages data as disparate as infectious agents, patient's vitals and human fatigue. In addition, they reveal that the thresholds proposed by the system are more efficient than the predefined ones. These results tend to prove that our simulator is an effective alert generator. Nevertheless, the context remains a simulation mode that we would like to enrich with real data from, for example, monitoring sensors (bracelet for human fatigue, monitoring, etc).
Tracelets and Tracelet Analysis Of Compositional Rewriting Systems

Taking advantage of a recently discovered associativity property of rule compositions, we extend the classical concurrency theory for rewriting systems over adhesive categories. We introduce the notion of tracelets, which are defined as minimal derivation traces that universally encode sequential compositions of rewriting rules. Tracelets are compositional, capture the causality of equivalence classes of traditional derivation traces, and intrinsically suggest a clean mathematical framework for the definition of various notions of abstractions of traces. We illustrate these features by introducing a first prototype for a framework of tracelet analysis, which as a key application permits to formulate a first-of-its-kind algorithm for the static generation of minimal derivation traces with prescribed terminal events.
Latent learning and the formation of a spatiotemporal cognitive map of a road network

This paper discusses two main themes. First, it investigates the formation of a spatiotemporal cognitive map (mental image) of a road network in travelers memory, which entails the travelers global conceptual understanding of congestion or the degree of crowding of the network. Second, it tries to investigate how latent learning of travelers from previous experiences shapes parts of the mental image, even for the parts of the network with which the travelers are unfamiliar. An experiment of route choice experiences was conducted among 90 participants in order to gain insight into the formation of a cognitive map and latent learning. In this experiment, the following independent variables are connected to the formation of a mental image of the network and the quality of the generalization of the unfamiliar parts of the network: (i) dispersion of the links travel time throughout the network, (ii) number of trips the traveler makes, (iii) travelers gender, (iv) travelers driving experiences, (v) travelers natural level of optimism or pessimism, (vi) salient or noticeable features on the network, and (vii) the presence of traffic signals. Several nonparametric (distribution-free) tests are employed to test the hypotheses. The results indicate that apart from the travelers gender and salient or noticeable features on the network, the considered factors significantly affect the degree of the recognizability of the network elements by travelers.
Duality in Quantum Quenches and Classical Approximation Algorithms: Pretty Good or Very Bad

We consider classical and quantum algorithms which have a duality property: roughly, either the algorithm provides some nontrivial improvement over random or there exist many solutions which are significantly worse than random. This enables one to give guarantees that the algorithm will find such a nontrivial improvement: if few solutions exist which are much worse than random, then a nontrivial improvement is guaranteed. The quantum algorithm is based on a sudden <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>h</mml:mi></mml:math> of a Hamiltonian; while the algorithm is general, we analyze it in the specific context of MAX-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math>-LIN<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn></mml:math>, for both even and odd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math>. The classical algorithm is a ``dequantization of this algorithm", obtaining the same guarantee (indeed, some results which are only conjectured in the quantum case can be proven here); however, the quantum point of view helps in analyzing the performance of the classical algorithm and might in some cases perform better.
Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

We present a novel method for publishing differentially private synthetic attributed graphs. Unlike preceding approaches, our method is able to preserve the community structure of the original graph without sacrificing the ability to capture global structural properties. Our proposal relies on C-AGM, a new community-preserving generative model for attributed graphs. We equip C-AGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release community-preserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.
Pyramid Point: A Multi-Level Focusing Network for Revisiting Feature Layers

We present a mcthod to lcarn a diverse group of object categories from an unordcrcd point set. We propose our Pyramid Point network, which uses a dense pyramid structure instead of thc traditional &#x2019;U&#x2019; shape, typically seen in semantic segmentation networks. This pyramid structure gives a second look, allowing thc network to revisit different layers creating from various leveis on the network, allowing for feature propagation. We introduce a Focused Kemel Point convolution (FKP Conv), which expands on the traditional point convolutions by adding an attention mcchanism to the kemel outputs. This FKP Conv increases our feature quality and allows us to weigh thc kemel outputs dynamically. These FKP Convs are the central part of our Recurrent FKP Bottlcneck block, which makes up thc backbonc of our encoder. With this distinct network, we demonstrate competitive performance on threc benchmark data sets.
Aesthetic and technical strategies for networked music performance

Networked music is no longer a future genre: the global quarantine event of 2020 launched the concept of performing together over the Internet into the mainstream. While the demand for performing at a distance may be a new imperative, musicians find themselves faced with technological and performative processes that do not appear to be suitable for performing music together online, due particularly to network latency which disrupts the ability for musicians to synchronize. The research presented in this paper investigates and challenges the reasons why networked music is not readily embraced by musicians and describes how that might change, by way of interviews with practitioners and an in-depth review of the technical constraints. Limitations that might cause frustration are in fact shown to have creative strategies that give rise to aesthetic approaches, distinct to the platform. By exploiting the constraints, in tandem with developing technology designed specifically for remote performance, aesthetic implications arise that not only accommodate the inconveniences of latency and acoustic feedback but can help us adapt and transform how we engage in real-time online, towards a future where we can imagine performing together over even more dramatic distances such as high-latency, low-bandwidth locations outside of urban areas-or even over galactic distances.
Scheme for realizing quantum dense coding via entanglement swapping

Quantum dense coding is a protocol for transmitting two classical bits of information from a sender (Alice) to a remote receiver (Bob) by sending only one quantum bit (qubit). In this article, we propose an experimentally feasible scheme to realize quantum dense coding via entanglement swapping in a cavity array containing a certain number of two-level atoms. Proper choice of system parameters such as atom–cavity and inter-cavity couplings allows perfect transfer of information. A high fidelity transfer of information is shown to be possible by using recently achieved experimental values in the context of photonic crystal cavities and superconducting resonators. To mimic experimental imperfections, disorder in both the coupling strengths and resonance frequencies is considered.
Modular Answer Set Programming as a Formal Specification Language

In this paper, we study the problem of formal verification for Answer Set Programming (ASP), namely, obtaining a formal proof showing that the answer sets of a given (non-ground) logic program P correctly correspond to the solutions to the problem encoded by P, regardless of the problem instance. To this aim, we use a formal specification language based on ASP modules, so that each module can be proved to capture some informal aspect of the problem in an isolated way. This specification language relies on a novel definition of (possibly nested, first order) program modules that may incorporate local hidden atoms at different levels. Then, verifying the logic program P amounts to prove some kind of equivalence between P and its modular specification. Under consideration for acceptance in TPLP.
On the number of limit cycles in asymmetric neural networks

The comprehension of the mechanisms at the basis of the functioning of complexly interconnected networks represents one of the main goals of neuroscience. In this work, we investigate how the structure of recurrent connectivity influences the ability of a network to have storable patterns and in particular limit cycles, by modeling a recurrent neural network with McCulloch–Pitts neurons as a content-addressable memory system.
Efficient Bayesian inversion for shape reconstruction of lithography masks

Background: Scatterometry is a fast, indirect, and nondestructive optical method for quality control in the production of lithography masks. To solve the inverse problem in compliance with the upcoming need for improved accuracy, a computationally expensive forward model that maps geometry parameters to diffracted light intensities has to be defined. Aim: To quantify the uncertainties in the reconstruction of the geometry parameters, a fast-to-evaluate surrogate for the forward model has to be introduced. Approach: We use a nonintrusive polynomial chaos-based approximation of the forward model, which increases speed and thus enables the exploration of the posterior through direct Bayesian inference. In addition, this surrogate allows for a global sensitivity analysis at no additional computational overhead. Results: This approach yields information about the complete distribution of the geometry parameters of a silicon line grating, which in return allows for quantifying the reconstruction uncertainties in the form of means, variances, and higher order moments of the parameters. Conclusions: The use of a polynomial chaos surrogate allows for quantifying both parameter influences and reconstruction uncertainties. This approach is easy to use since no adaptation of the expensive forward model is required.
Axiomatising infinitary probabilistic weak bisimilarity of finite-state behaviours

In concurrency theory, weak bisimilarity is often used to relate processes exhibiting the same observable behaviour. The probabilistic environment gives rise to several generalisations; we study the infinitary semantics, which abstracts from a potentially unbounded number of internal actions being performed when something observable happens. Arguing that this notion yields the most desirable properties, we provide a sound and complete axiomatisation capturing its essence. Previous research has failed to achieve completeness in the presence of unguarded recursion, as only the finitary variant has been axiomatised, yet.
Bottleneck Problems: An Information and Estimation-Theoretic View

Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, capacity problems (e.g., Mrs. Gerber's Lemma), and strong data processing inequalities, among others. In this work, we first investigate the functional properties of IB and PF through a unified theoretical framework. We then connect them to three information-theoretic coding problems, namely hypothesis testing against independence, noisy source coding, and dependence dilution. Leveraging these connections, we prove a new cardinality bound on the auxiliary variable in IB, making its computation more tractable for discrete random variables. In the second part, we introduce a general family of optimization problems, termed "bottleneck problems", by replacing mutual information in IB and PF with other notions of mutual information, namely f-information and Arimoto's mutual information. We then argue that, unlike IB and PF, these problems lead to easily interpretable guarantees in a variety of inference tasks with statistical constraints on accuracy and privacy. While the underlying optimization problems are non-convex, we develop a technique to evaluate bottleneck problems in closed form by equivalently expressing them in terms of lower convex or upper concave envelope of certain functions. By applying this technique to a binary case, we derive closed form expressions for several bottleneck problems.
Quantum Hamiltonian Computing protocols for molecular electronics Boolean logic gates

Quantum Hamiltonian Computing is a recent approach that uses quantum systems, in particular a single molecule, to perform computational tasks. Within this approach, we present explicit methods to construct logic gates using two different designs, where the logical outputs are encoded either at fixed energy and spatial positioning of the quantum states, or at different energies. We use these results to construct quantum Boolean adders involving a minimal number of quantum states with the two designs. We also establish a matrix algebra giving an analogy between classical Boolean logic gates and quantum ones, and assess the possibilities of both designs for more complex gates.
Signatures of avoided energy-level crossings in entanglement indicators obtained from quantum tomograms

Extensive theoretical and experimental investigations on multipartite systems close to an avoided energy-level crossing reveal interesting features such as the extremisation of entanglement. Conventionally, the estimation of entanglement directly from experimental observation involves either one of two approaches: Uncertainty-relation-based estimation that captures the linear correlation between relevant observables, or rigorous but error-prone quantum state reconstruction on tomograms obtained from homodyne measurements. We investigate the behaviour, close to avoided crossings, of entanglement indicators that can be calculated directly from a numerically-generated tomogram. The systems we study are two generic bipartite continuous-variable systems: a Bose-Einstein condensate trapped in a double-well potential, and a multi-level atom interacting with a radiation field. We also consider a multipartite hybrid quantum system of superconducting qubits interacting with microwave photons. We carry out a quantitative comparison of the indicators with a standard measure of entanglement, the subsystem von Neumann entropy (SVNE). It is shown that the indicators that capture the nonlinear correlation between relevant subsystem observables are in excellent agreement with the SVNE.
State leakage during fast decay and control of a superconducting transmon qubit

Superconducting Josephson junction qubits constitute the main current technology for many applications, including scalable quantum computers and thermal devices. Theoretical modeling of such systems is usually done within the two-level approximation. However, accurate theoretical modeling requires taking into account the influence of the higher excited states without limiting the system to the two-level qubit subspace. Here, we study the dynamics and control of a superconducting transmon using the numerically exact stochastic Liouville-von Neumann equation approach. We focus on the role of state leakage from the ideal two-level subspace for bath induced decay and single-qubit gate operations. We find significant short-time state leakage due to the strong coupling to the bath. We quantify the leakage errors in single-qubit gates and demonstrate their suppression with DRAG control for a five-level transmon in the presence of decoherence. Our results predict the limits of accuracy of the two-level approximation and possible intrinsic constraints in qubit dynamics and control for an experimentally relevant parameter set.
Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach

Background Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the “gold standard” reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. Objective This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. Methods We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. Results When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve–receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. Conclusions This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.
RESEARCH OF DESCRIPTOR BASED IMAGE NORMALIZATION AND COMPARATIVE ANALYSIS OF SURF, SIFT, BRISK, ORB, KAZE, AKAZE DESCRIPTORS

The subject of research is image normalization based on key points analysis. The purpose is development of mathematical models and their software implementation for normalization of image geometric transformations based on the analysis of SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors; the model application for comparative analysis of descriptors based on expert assessments of normalization quality, time costs and other indicators; construction and usage in experiments the own dataset with 100 real image pairs which contains scenes of five types: buildings, plane images outside, plane images inside, natural and artificial textures; making conclusions about the performance of the considered descriptors to solve the normalization problem. Such methods are applied: SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors for describing key points, the Nearest Neighbor Distance Ratio method or symmetric method for search of corresponding pairs of key points from different images, the RANSAC method for rejecting false correspondences and obtaining a homography matrix, similarity measures, software modeling. The results obtained: experimental normalization results by SIFT, SURF, ORB, BRISK, KAZE, AKAZE descriptors for 100 real pairs of own dataset (normalized images, their overlaps, quantitative descriptor evaluation, precision and recall estimation, time costs estimation, expert quality assessment, conversion of all indicator values to an 8-point rating scale); summary diagrams and conclusions about advantages and weaknesses of the compared descriptors; recommendations about the most-suitable-algorithm selection for solving normalization problem in specific cases.
Industrial Augmented Reality: 3D-Content Editor for Augmented Reality Maintenance Worker Support System

Supporting maintenance with 3D object enhanced instruction is one of the key applications of Augmented Reality (AR) in industry. For the breakthrough of AR in maintenance, it is important that the technicians themselves can create AR-instructions and perform the challenging task of placing 3D objects as they know best how to perform a task and what necessary information needs to be displayed. For this challenge, a 3D-content editor is being presented wherein a first step the 3D objects can roughly be placed using a 2D image of the machine, therefore, limiting the time required to access the machine. In a second step, the positions of the 3D objects can be fine-tuned at the machine site using live footage. The key challenges were to develop an easily accessible UI that requires no prior knowledge of AR content creation in a tool that works both with live footage and images and is usable with a touch screen and keyboard/mouse. The 3D-content editor was qualitatively assessed by technicians revealing its general applicability, but also the requirement for a lot of time to gain the necessary experience for positioning 3D objects.
A random-walk benchmark for single-electron circuits

Mesoscopic integrated circuits achieving high-fidelity control of elementary quantum systems require new methodology for benchmarking. We offer circuit-level statistical description of rare-error accumulation in terms of a universal random-walk model for on-demand electron transfer. For a high-fidelity single-electron circuit, realized in the experiment as a chain of quantum dots in a GaAs/AlGaAs heterostructure, the error of the transfer operation is probed by charge counting. Error rates for extra ($P_+$) or missing ($P_-$) electrons of the electron shuttle are measured to $P_{-}=(6.92 \pm 0.14) \times 10^{-5}$ and $P_{+}=(2.13 \pm 0.08)\times 10^{-5}$ with uncertainty due to correlated noise in the environment. Furthermore, precise control over the timing of the random walk allows to explore the role of memory as the clock frequency is increased.