Reviewerly/exHarmony huggingface dataset:
MedML: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction

<h2>Summary</h2> The COVID-19 pandemic has caused devastating economic and social disruption. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform the distribution of limited healthcare resources. To address this challenge, we propose a machine learning model, MedML, to conduct the hospitalization and severity prediction for the pediatric population using electronic health records. MedML extracts the most predictive features based on medical knowledge and propensity scores from over 6 million medical concepts and incorporates the inter-feature relationships in medical knowledge graphs via graph neural networks. We evaluate MedML on the National Cohort Collaborative (N3C) dataset. MedML achieves up to a 7% higher AUROC and 14% higher AUPRC compared to the best baseline machine learning models. MedML is a new machine learnig framework to incorporate clinical domain knowledge and is more predictive and explainable than current data-driven methods.
Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

Knowing exactly how much solar radiation reaches a particular area is helpful when planning solar energy installations. In recent years the use of renewable energies, especially those related to photovoltaic systems, has had an impressive up-tendency. Therefore, mechanisms that allow us to predict solar radiation are essential. This work aims to present results for predicting solar radiation using optimization with the Random Forest (RF) algorithm. Moreover, it compares the obtained results with other machine learning models. The conducted analysis is performed in Queretaro, Mexico, which has both direct solar radiation and suitable weather conditions more than three quarters of the year. The results show an effective improvement when optimizing the hyperparameters of the RF and Adaboost models, with an improvement of 95.98% accuracy compared to conventional methods such as linear regression, with 54.19%, or recurrent networks, with 53.96%, without increasing the computational time and performance requirements to obtain the prediction. The analysis was successfully repeated in two different scenarios for periods in 2020 and 2021 in Juriquilla. The developed method provides robust performance with similar results, confirming the validity and effectiveness of our approach.
LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation

While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation~(UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift~w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called ``Label-Efficient Unsupervised Domain Adaptation"~(LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature. Code is available at: https://github.com/jacobzhaoziyuan/LE-UDA.
Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation

We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an auxiliary task in an unsupervised manner. To generate the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of the most widely used and powerful hand-crafted features for detection. Together with the ground truth semantic segmentation masks for the primary task and pseudo-labels for the auxiliary task, we learn the parameters of the deep network to minimize the loss of both the primary task and the auxiliary task jointly. We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup. To validate our hypothesis, we performed experiments on two different medical image segmentation data sets. From the extensive quantitative and qualitative results, we observe that our method consistently improves the performance compared to the counter-part method. Moreover, our method is the winner of FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction with MICCAI 2021. Code and implementation details are available at:https://github.com/thetna/medical_image_segmentation.
Improving metrology with quantum scrambling

Quantum scrambling describes the spreading of information into many degrees of freedom in quantum systems, such that the information is no longer accessible locally but becomes distributed throughout the system. This idea can explain how quantum systems become classical and acquire a finite temperature, or how in black holes the information about the matter falling in is seemingly erased. We probe the exponential scrambling of a multiparticle system near a bistable point in phase space and utilize it for entanglement-enhanced metrology. A time-reversal protocol is used to observe a simultaneous exponential growth of both the metrological gain and the out-of-time-order correlator, thereby experimentally verifying the relation between quantum metrology and quantum information scrambling. Our results show that rapid scrambling dynamics capable of exponentially fast entanglement generation are useful for practical metrology, resulting in a 6.8(4)-decibel gain beyond the standard quantum limit.
Image Preprocessing for Illumination Invariant Face Verification

Performance of the face verification system depend on many conditions. One of the most problematic is varying illumination condition. In this paper 14 normalization algorithms based on histogram normalization, illumination properties and the human perception theory were compared using 3 verification methods. The results obtained from the experiments showed that the illumination preprocessing methods significantly improves the verification rate and it’s a very important step in face verification system.
Improved Particle Filter Resampling Architectures

Abstract The most challenging aspect of particle filtering hardware implementation is the resampling step. This is because of high latency as it can be only partially executed in parallel with the other steps of particle filtering and has no inherent parallelism inside it. To reduce the latency, an improved resampling architecture is proposed which involves pre-fetching from the weight memory in parallel to the fetching of a value from a random function generator along with architectures for realizing the pre-fetch technique. This enables a particle filter using M particles with otherwise streaming operation to get new inputs more often than 2 M cycles as the previously best approach gives. Results show that a pre-fetch buffer of five values achieves the best area-latency reduction trade-off while on average achieving an 85 % reduction in latency for the resampling step leading to a sample time reduction of more than 40%. We also propose a generic division-free architecture for the resampling steps. It also removes the need of explicitly ordering the random values for efficient multinomial resampling implementation. In addition, on-the-fly computation of the cumulative sum of weights is proposed which helps reduce the word length of the particle weight memory. FPGA implementation results show that the memory size is reduced by up to 50%.
Facilitating Interaction Between Virtual Agents Through Negotiation Over Ontological Representation

Fluent, effortless, and diverse e-business transactions depend on the ability of automated agents to interact. The difficulties of tailoring representation and information to be consistent and therefore interoperable needs to fall not on human users but on these automated agents. In this chapter, the authors present their system, ORS (ontology repair system), which is designed to be a tool for automated agents, acting on behalf of people or systems which need to interact, to enable them to understand one another, despite the fact that they are not centrally or consistently designed.
Interactive graphics for visually diagnosing forest classifiers in R

This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble since it is produced by bagging multiple trees. The process of bagging and combining results from multiple trees produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects of models are explored in this article, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm and projection pursuit forest but could be more broadly applied to other bagged ensembles helping in the interpretability deficit of these methods. Interactive graphics are built in R using the ggplot2, plotly, and shiny packages.
Learning residual alternating automata

Residuality plays an essential role for learning finite automata. While residual deterministic and nondeterministic automata have been understood quite well, fundamental questions concerning alternating automata (AFA) remain open. Recently, Angluin, Eisenstat, and Fisman (2015) have initiated a systematic study of residual AFAs and proposed an algorithm called AL⋆ – an extension of the popular L⋆ algorithm – to learn AFAs. Based on computer experiments they conjectured that AL⋆ produces residual AFAs, but have not been able to give a proof. In this paper we disprove this conjecture by constructing a counterexample. As our main positive result we design an efficient learning algorithm, named AL⋆⋆, and give a proof that it outputs residual AFAs only. In addition, we investigate the succinctness of these different finite automata (FA) types in more detail.
Random ubiquitous transformation semigroups

A smallest generating set of a semigroup is a generating set of the smallest cardinality. Similarly, an irredundant generating setX is a generating set such that no proper subset of X is also a generating set. A semigroup S is ubiquitous if every irredundant generating set of S is of the same cardinality. We are motivated by a naïve algorithm to find a small generating set for a semigroup, which in practice often outputs a smallest generating set. We give a sufficient condition for a transformation semigroup to be ubiquitous and show that a transformation semigroup generated by k randomly chosen transformations asymptotically satisfies the sufficient condition. Finally, we show that under this condition the output of the previously mentioned naïve algorithm is irredundant.
Tree sums of maximal connected spaces

A topology $\tau$ on a set $X$ is called maximal connected if it is connected, but no strictly finer topology $\tau^* > \tau$ is connected. We consider a construction of so-called tree sums of topological spaces, and we show how this construction preserves maximal connectedness and also related properties of strong connectedness and essential connectedness. We also recall the characterization of finitely generated maximal connected spaces and reformulate it in the language of specialization preorder and graphs, from which it is clear that finitely generated maximal connected spaces are precisely $T_\frac{1}{2}$ tree sums of copies of the Sierpi\'nski space.
Fast and Almost Optimal Any-Angle Pathfinding Using the 2k Neighborhoods

Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to the 2k neighborhoods, a family of neighborhoods that allow an increasing number of movements (and angles) as k is increased. This observation yields a pathfinder that computes 2k-optimal paths very quickly. Compared to ANYA, an optimal true any-angle planner, over a variety of benchmarks, our planner is one order of magnitude faster while being less than 0.0005% suboptimal. Important to our planner's performance was the development of an iterative 2k heuristic, linear in k, which is also a contribution of this paper.
Using Parallel Computing to Improve the Scalability of Models with BDI Agents

These last years have seen the development of several extensions of modeling platforms to include BDI agents. These extensions have allowed modelers with little knowledge in programming and artificial intelligence to develop their own cognitive agents. However, especially in large-scale simulations, the problem of the computational time required by such complex agents is still an open issue. In order to address this difficulty, we propose a parallel version of the BDI architecture integrated into the GAMA platform. We show through several case studies that this new parallel architecture is much more efficient in terms of execution time, while remaining easy to use even by non-computer scientists.
Fields with automorphism and valuation

Abraham Robinson's method for finding model completions is refined and and generalized for model companions and is applied to the theory of fields equipped with both a valuation and an automorphism.
Modernizing quantum annealing II: genetic algorithms with the inference primitive formalism

Quantum annealing allows for quantum fluctuations to be used used to assist in finding the solution to some of the worlds most challenging computational problems. Recently, this field has attracted much interest because of the construction of large-scale flux-qubit based quantum annealing devices. There has been recent work on [Chancellor NJP 19(2):023024, 2017] how the control protocols of these devices can be modified so that individual annealer calls on real devices can take initial conditions. Development is being undertaken to implement such protocols in the quantum annealing devices designed by D-Wave Systems Inc. and these features will be available to customers soon. In this paper, I develop a formalism for algorithmic design in quantum annealers, which I call the `inference primitive' formalism. This formalism allows for a natural description of calls to quantum annealers with a general control structure. This more generalized control structure includes not only the ability to include initial conditions in an annealer run, but also to control the annealing schedules of qubits or clusters of qubits independently, thereby representing relative certainty values of different parts of a candidate solution. I discuss the compatability of such controls with a wide variety of other current efforts to improve the performance of annealers, such as non-stoquatic drivers, synchronizing freeze times for the qubits, and belief propagation techniques. To demonstrate the power of the formalism I present here, I discuss how this new formalism can be used to represent annealer implementations of genetic algorithms, and can represent the addition of genetic components to currently used algorithms. The new tools I develop will allow a more complete understanding of the algorithmic space available to quantum annealers, and thereby make the field more competitive.
To the memory of Prof. Yuriy Reznikov

• Main attention is paid to the character traits of Prof. Yuriy Reznikov. • Caring for people and ability to gather them in non-formal events are described. • The Big Pre-tilt day is shortly mentioned.
Relative JSON Pointers

JSON Pointer is a syntax for specifying locations in a JSON document,starting from the document root. This document defines an extension tothe JSON Pointer syntax, allowing relative locations from within thedocument.
RESTRICTED INTERPOLATION AND LACK THEREOF IN STIT LOGIC

Abstract We consider the propositional logic equipped with Chellas stit operators for a finite set of individual agents plus the historical necessity modality. We settle the question of whether such a logic enjoys restricted interpolation property, which requires the existence of an interpolant only in cases where the consequence contains no Chellas stit operators occurring in the premise. We show that if action operators count as logical symbols, then such a logic has restricted interpolation property iff the number of agents does not exceed three. On the other hand, if action operators are considered to be nonlogical symbols, then the restricted interpolation fails for any number of agents exceeding one. It follows that unrestricted Craig interpolation also fails for almost all versions of stit logic.
On Basing One-way Permutations on NP-hard Problems under Quantum Reductions

A fundamental pursuit in complexity theory concerns reducing worst-case problems to average-case problems. There exist complexity classes such as PSPACE that admit worst-case to average-case reductions. However, for many other classes such as NP, the evidence so far is typically negative, in the sense that the existence of such reductions would cause collapses of the polynomial hierarchy(PH). Basing cryptographic primitives, e.g., the average-case hardness of inverting one-way permutations, on NP-completeness is a particularly intriguing instance. As there is evidence showing that classical reductions from NP-hard problems to breaking these primitives result in PH collapses, it seems unlikely to base cryptographic primitives on NP-hard problems. Nevertheless, these results do not rule out the possibilities of the existence of quantum reductions. In this work, we initiate a study of the quantum analogues of these questions. Aside from formalizing basic notions of quantum reductions and demonstrating powers of quantum reductions by examples of separations, our main result shows that if NP-complete problems reduce to inverting one-way permutations using certain types of quantum reductions, then<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">coNP</mml:mtext></mml:mrow></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>⊆</mml:mo></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="sans-serif">QIP</mml:mtext></mml:mrow><mml:mo stretchy="false">(</mml:mo></mml:math>2<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo stretchy="false">)</mml:mo></mml:math>.
Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

A new and an enriched JPEG algorithm is provided for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function. Our main contribution is by formulating analytically (instead of approximating) the inverse of the transpose of JPEG-wavelet transform without involving matrices which are computationally cumbersome. The algorithm is suitable for the widely-spread situations where the original data distribution is unobservable such as in cases where there is deficient representation of the entire population in the training data (in machine learning) and thus the covariate shift assumption is violated. The proposed estimator corrects for both biases, the one generated by endogenous truncation and the one generated by endogenous covariates. Results from utilizing 2 000 000 different distribution functions verify the applicability and high accuracy of our procedure to cases in which the disturbances are neither jointly nor marginally normally distributed.
Generalized Network Control Automation YANG Model

This document describes a YANG data model for the Generalized NetworkControl Automation (GNCA), aimed to define an abstract and uniformsemantics for NETCONF/YANG scripts in the form of Event-Condition-Action (ECA) containers.
Dynamical quantum phase transitions in interacting atomic interferometers

Particle-wave duality has allowed physicists to establish atomic interferometers as celebrated complements to their optical counterparts in a broad range of quantum devices. However, interactions naturally lead to decoherence and have been considered as a longstanding obstacle in implementing atomic interferometers in precision measurements. Here, we show that interactions lead to dynamical quantum phase transitions between Schr\"{o}dinger's cats in an atomic interferometer. These transition points result from zeros of Loschmidt echo, which approach the real axis of the complex time plane in the large particle number limit, and signify pair condensates, another type of exotic quantum states featured with prevailing two-body correlations. Our work suggests interacting atomic interferometers as a new tool for exploring dynamical quantum phase transitions and creating highly entangled states to beat the standard quantum limit.
Foundation School Program System Online Training

Click any of the following links to begin training for the Foundation School Program (FSP) modules. You can view as many as you like in any sequence. However, we recommend that you begin with the FSP Overview. Most modules contain multiple parts, and each module concludes with a quiz. The module
Applications of the Bounded Total Variation Denoising Method to Urban Traffic Analysis

While it is believed that denoising is not always necessary in many big data applications, we show in this paper that denoising is helpful in urban traffic analysis by applying the method of bounded total variation denoising to the urban road traffic prediction and clustering problem.We propose two easy-to-implement methods to estimate the parameter noise strength in the denoising algorithm, and apply the denoising algorithm to GPS-based traffic data from Beijing taxi system.For the traffic prediction problem, numerical experiments show that the predicting accuracy is improved by applying the proposed bounded total variation denoising algorithm.For the clustering problem, we apply a recently developed parameter-free clustering analysis method for more than one hundred urban road segments in Beijing based on their velocity profiles.Much better clustering results are obtained after denoising.