Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment

Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent patient medical histories. Embeddings efficiently represent medical concepts learned from diagnostic, procedure, and prescription codes in patients' medical histories. This approach substantially reduces the need for feature engineering. Our results show that models using embeddings had better performance than a commercial risk adjustment model on the task of prospective risk score prediction.
Statistics of the first Galois cohomology group :
A refinement of Malle’s conjecture

Malle proposed a conjecture for counting the number of $G$-extensions $L/K$ with discriminant bounded above by $X$, denoted $N(K,G;X)$, where $G$ is a fixed transitive subgroup $G\subset S_n$ and $X$ tends towards infinity. We introduce a refinement of Malle's conjecture, if $G$ is a group with a nontrivial Galois action then we consider the set of crossed homomorphisms in $Z^1(K,G)$ (or equivalently $1$-coclasses in $H^1(K,G)$) with bounded discriminant. This has a natural interpretation given by counting $G$-extensions $F/L$ for some fixed $L$ and prescribed extension class $F/L/K$. If $T$ is an abelian group with any Galois action, we compute the asymptotic main term of this refined counting function for $Z^1(K,T)$ (and equivalently for $H^1(K,T)$) and show that it is a natural generalization of Malle's conjecture. The proof technique is in essence an application of a theorem of Wiles on generalized Selmer groups, and additionally gives the asymptotic main term when restricted to certain local behaviors. As a consequence, whenever the inverse Galois problem is solved for $G\subset S_n$ over $K$ and $G$ has an abelian normal subgroup $T\trianglelefteq G$ we prove a nontrivial lower bound for $N(K,G;X)$ given by a nonzero power of $X$ times a power of $\log X$. For many groups, including many solvable groups, these are the first known nontrivial lower bounds. These bounds prove Malle's predicted lower bounds for a large family of groups, and for an infinite subfamily they generalize Kluners' counter example to Malle's conjecture and verify the corrected lower bounds predicted by Turkelli.
Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach

Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist in the real-world decision-making process of healthcare issues. We propose a deep learning model based on Bayesian Neural Networks (BNN) to predict uncertainty induced by data noise. The uncertainty is introduced to provide model predictions with an extra level of confidence. Our experiments verify that instances with high uncertainty are harmful to model performance. Moreover, by investigating the distributions of model prediction and uncertainty, we show that it is possible to identify a group of patients for timely intervention, such that decreasing data noise will benefit more on the prediction accuracy for these patients.
Satellite System Graph: Towards the Efficiency Up-Boundary of Graph-Based Approximate Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) in high dimensional space is essential in database and information retrieval. Recently, there has been a surge of interests in exploring efficient graph-based indices for the ANNS problem. Among them, the NSG has resurrected the theory of Monotonic Search Networks (MSNET) and achieved the state-of-the-art performance. However, the performance of the NSG deviates from a potentially optimal position due to the high sparsity of the graph. Specifically, though the average degree of the graph is small, their search algorithm travels a longer way to reach the query. Integrating both factors, the total search complexity (i.e., the number of distance calculations) is not minimized as their wish. In addition, NSG suffers from a high indexing time complexity, which limits the efficiency and the scalability of their method. In this paper, we aim to further mine the potential of the MSNETs. Inspired by the message transfer mechanism of the communication satellite system, we find a new family of MSNETs, namely the Satellite System Graphs (SSG). In particular, while inheriting the superior ANNS properties from the MSNET, we try to ensure the angles between the edges to be no smaller than a given value. Consequently, each node in the graph builds effective connections to its neighborhood omnidirectionally, which ensures an efficient search-routing on the graph like the message transfer among the satellites. We also propose an approximation of the SSG, Navigating SSG, to increase the efficiency of indexing. Both theoretical and extensive experimental analysis are provided to demonstrate the strengths of the proposed approach over the existing state-of-the-art algorithms. Our code has been released on GitHub.
Real-time event detection in social network data streams

Today streaming text mining plays an important role within real-time social media mining. Given the amount and cadence of the data generated by those platforms classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, available algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions about computational complexity and required execution time per document analysis. This work presents a research proposal to a robust real-time social network text stream event detection system that combines text stream mining and network analysis methods. The present proposal present the current state-of-the-art systems, algorithms and methodologies to perform event detection in streaming environments. The research methodology, research questions and formulation of an hypothesis is also included. Finally it is presented the research planning for a 4 year period with a detailed work breakdown structure and list of deliverables.
The integration of variable generation and storage into electricity capacity markets

We show how to value both variable generation and energy storage in such a way as to enable them to be integrated fairly (from the point of view of capacity providers) and optimally (from the point of view of society) into electricity capacity markets, for example that which operates in Great Britain. We develop a theory based on balancing expected energy unserved against costs of capacity procurement, and in which the optimal procurement may be expressed as that necessary to meet an appropriate reliability standard. In the absence of variable generation and storage the entire theory reduces to that already in common use---both in the definition of a standard and in its economic justification. Further the valuation of both variable generation and storage in the proposed approach coincides with the traditional risk-based approach leading to the concept of an equivalent firm capacity. The determination of the equivalent firm capacity of storage requires particular care; this is due both to the flexibility with which storage added to an existing system may be scheduled, and also to the fact that, when \emph{any} resource is added to an existing system, storage already within that system may be flexibly rescheduled.
BKT-paired phase in coupled XY models

Giacomo Bighin,1, ∗ Nicolò Defenu,2, ∗ István Nándori,3, 4, 5 Luca Salasnich,6, 7 and Andrea Trombettoni8, 9 1IST Austria (Institute of Science and Technology Austria), Am Campus 1, 3400 Klosterneuburg, Austria 2Institut für Theoretische Physik, Universität Heidelberg, D-69120 Heidelberg, Germany 3MTA-DE Particle Physics Research Group, P.O. Box 51, H-4001 Debrecen, Hungary 4MTA Atomki, P.O. Box 51, H-4001 Debrecen, Hungary 5University of Debrecen, P.O. Box 105, H-4010 Debrecen, Hungary 6Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova, Italy 7Istituto Nazionale di Ottica (INO) del Consiglio Nazionale delle Ricerche (CNR), Sezione di Sesto Fiorentino, Via Nello Carrara 2, 50019 Sesto Fiorentino, Italy 8CNR-IOM DEMOCRITOS Simulation Center, Via Bonomea 265, I-34136 Trieste, Italy 9SISSA and INFN, Sezione di Trieste, Via Bonomea 265, I-34136 Trieste, Italy (Dated: July 16, 2019)
Anonymous and Confidential File Sharing over Untrusted Clouds

Using public cloud services for storing and sharing confidential data requires end users to cryptographically protect both the data and the access to the data. In some cases, the identity of end users needs to remain confidential against the cloud provider and fellow users accessing the data. As such, the underlying cryptographic access control mechanism needs to ensure the anonymity of both data producers and consumers. We introduce A-SKY, a cryptographic access control extension capable of providing confidentiality and anonymity guarantees, all while efficiently scaling to large organizations. A-SKY leverages trusted execution environments (TEEs) to address the impracticality of anonymous broadcast encryption (ANOBE) schemes, achieving faster execution times and shorter ciphertexts. The innovative design of A-SKY limits the usage of the TEE to the narrow set of data producing operations, and thus optimizes the dominant data consumption actions by not requiring a TEE. Furthermore, we propose a scalable implementation for A-SKY leveraging micro-services that preserves strong security guarantees while being able to efficiently manage realistic large user bases. Results highlight that the A-SKY cryptographic scheme is 3 orders of magnitude better than state of the art ANOBE, and an end-to-end system encapsulating A-SKY can elastically scale to support groups of 10000 users while maintaining processing costs below 1 second.
Within and among-population variation in vital rates and population dynamics in a variable environment

Understanding the causes of within-and among-population differences in vital rates, life histories, and population dynamics is a central topic in ecology. To understand how within-and among-population variation emerges, we need long-term studies that include episodic events and contrasting environmental conditions, data to characterize individual and shared variation, and statistical models that can tease apart population-, shared-, and individual contribution to the observed variation. We used long-term tag-recapture data and novel statistical and modeling techniques to investigate and estimate within-and among-population differences in vital rates, life histories and population dynamics of marble trout Salmo marmoratus, a endemic freshwater salmonid with a narrow range. Only ten populations of pure marble trout persist in headwaters of Alpine rivers in western Slovenia. Marble trout populations are also threatened by floods and landslides, which have caused the extinction of two populations in recent years. We estimated and determined causes of variation in growth, survival, and recruitment both within and among populations, and evaluated trade-offs between them. Specifically, we estimated the responses of these traits to variation in water temperature, density, sex, early life conditions, and the occurrence of extreme climatic events (e.g., flash floods and debris flows). We found that the effects of population density on traits were mostly limited to the early stages of life and that individual growth trajectories were established early in life. We found no clear effects of water temperature on survival and recruitment. Population density varied over time, with flash floods and debris flows causing massive mortalities and threatening population persistence. Apart from flood events, variation in population density within streams was largely determined by variation in recruitment, with survival of older fish being relatively constant over time within populations, but substantially different among populations. Marble trout show a fast-to-slow continuum of life histories, with slow growth associated with higher survival at the population level, possibly determined by food conditions and age at maturity. Our work provides unprecedented insight into the causes of variation in vital rates, life histories, and population dynamics in an endemic species that is teetering on the edge of extinction.
Plataforma de simulação ns-3 para avaliação de um protocolo de ligação de dados fiável sobre canais IEEE 802.11

V AGRADECIMENTOS . VII LISTA DE FIGURAS. XIV LISTA DE TABELAS . XVII ABREVIATURAS E SÍMBOLOS . XVIII CAPÍTULO 1 . 1 INTRODUÇÃO . 1 1.1 Contexto e motivação . 1 1.2 Objetivos. 2 1.3 Organização do documento . 3 CAPÍTULO 2 . 4 REVISÃO DA LITERATURA E ESTADO DA ARTE . 4 2.1 Introdução ao standard IEEE 802.11 . 5 2.2 Modos de operação e tipos de redes IEEE 802.11 . 6 2.3 Camada MAC . 7 2.3. 1 CSMA/CA . 8 2.3. 2 RTS/CTS . 8 2.3. 3 Point Coordination Function (PCF) . 9 2.4 Camada PHY . 10 2.5 Evolução do standard IEEE 802.11 . 10 2.5. 1 Standards de Nova Geração . 11 2.5. 1.1 Agregação de tramas . 11 2.5. 1.2 Confirmação de tramas em bloco . 13 2.6 Protocolos do Tipo Polling . 15
Vid2Param: Online system identification from video for robotics applications

Robots performing tasks in dynamic environments would benefit greatly from understanding the underlying environment motion, in order to make future predictions and to synthesize effective control policies that use this inductive bias. Online system identification is therefore a fundamental requirement for robust autonomous agents. When the dynamics involves multiple modes (due to contacts or interactions between objects), and when system identification must proceed directly from a rich sensory stream such as video, then traditional methods for system identification may not be well suited. We propose an approach wherein fast parameter estimation with a model can be seamlessly combined with a recurrent variational autoencoder. Our Physics-based recurrent variational autoencoder model includes an additional loss that enforces conformity with the structure of a physically based dynamics model. This enables the resulting model to encode parameters such as position, velocity, restitution, air drag and other physical properties of the system. The model can be trained entirely in simulation, in an end-to-end manner with domain randomization, to perform online system identification, and probabilistic forward predictions of parameters of interest. We benchmark against existing system identification methods and demonstrate that Vid2Param outperforms the baselines in terms of speed and accuracy of identification, and also provides uncertainty quantification in the form of a distribution over future trajectories. Furthermore, we illustrate the utility of this in physical experiments wherein a PR2 robot with velocity constrained arm must intercept a bouncing ball, by estimating the physical parameters of this ball directly from the video trace after the ball is released.
A Neural Turing~Machine for Conditional Transition Graph Modeling

Graphs are an essential part of many machine learning problems such as analysis of parse trees, social networks, knowledge graphs, transportation systems, and molecular structures. Applying machine learning in these areas typically involves learning the graph structure and the relationship between the nodes of the graph. However, learning the graph structure is often complex, particularly when the graph is cyclic, and the transitions from one node to another are conditioned such as graphs used to represent a finite state machine. To solve this problem, we propose to extend the memory based Neural Turing Machine (NTM) with two novel additions. We allow for transitions between nodes to be influenced by information received from external environments, and we let the NTM learn the context of those transitions. We refer to this extension as the Conditional Neural Turing Machine (CNTM). We show that the CNTM can infer conditional transition graphs by empirically verifiying the model on two data sets: a large set of randomly generated graphs, and a graph modeling the information retrieval process during certain crisis situations. The results show that the CNTM is able to reproduce the paths inside the graph with accuracy ranging from 82,12% for 10 nodes graphs to 65,25% for 100 nodes graphs.
A Conditional Wasserstein Generative Adversarial Network for Pixel-level Crack Detection using Video Extracted Images

Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a novel method on the basis of conditional Wasserstein generative adversarial network (cWGAN) is proposed for road crack detection. A 121-layer densely connected neural network with deconvolution layers for multi-level feature fusion is used as generator, and a 5-layer fully convolutional network is used as discriminator. To overcome the scattered output issue related deconvolution layers, connectivity maps are introduced to represent the crack information within the proposed cWGAN. The proposed method is tested on a dataset collected from a moving vehicle equipped with a commercial grade high speed camera. This dataset is challenging because the images containing cracks also include the disturbance of other objects. The results show that the proposed method achieves state-of-the-art performance compared with other existing methods in terms of precision, recall and F1 score.
Contour dynamics for surface quasi-geostrophic fronts

We derive contour dynamics equations for the motion of infinite planar surface quasi-geostrophic fronts that can be represented as a graph. We give two different derivations with the same result: one is based on a decomposition of the front velocity field into a shear flow and a perturbation that decays away from the front; the other is based on the interpretation of the Riesz transform of the piecewise-constant L∞-function that jumps across the front as an element of BMO. The resulting equation is equivalent to one derived by a regularization procedure introduced previously by Hunter and Shu (2018).
Dynamical Systems as Temporal Feature Spaces

Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we consider the state space a temporal feature space and the readout mapping from the state space a kernel machine operating in that feature space. We show that: (1) The usual ESN strategy of randomly generating input-to-state, as well as state coupling leads to shallow memory time series representations, corresponding to cross-correlation operator with fast exponentially decaying coefficients; (2) Imposing symmetry on dynamic coupling yields a constrained dynamic kernel matching the input time series with straightforward exponentially decaying motifs or exponentially decaying motifs of the highest frequency; (3) Simple cycle high-dimensional reservoir topology specified only through two free parameters can implement deep memory dynamic kernels with a rich variety of matching motifs. We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.
Envelopes in Outer Space

We study the geometry of Outer Space $CV_n$ in regard of the asymmetric Lipschitz metric via envelopes, that is the set of all geodesics between two points. In the simplicial structure of $CV_n$ the envelopes are polytopes. We construct a piecewise unique geodesic between any two points in $CV_n$ by concatenating edges of these polytopes. In fact rigid geodesics can be identified with edges of out- and in-envelopes, that is the set of all geodesics from or to a base point with a given maximally stretched path. We introduce a notion of general position for pairs of points which is a dense and open condition. Using this we will show, that for almost all pairs of points in $CV_n$ their envelopes have dimension $3n-4$. Whenever an envelope passes a face, it might change its dimension. This determines the simplicial structure of reduced Outer Space via the Lipschitz metric which implies $\mathrm{Isom}(CV_n^{red})=\mathrm{Isom}(CV_n)$. As another implication we get that a geodesic ray in $CV_2$ becomes after a given length rigid.
Automatic parallelization of fine-grained metafunctions on a chip multiprocessor

Due to the importance of reliability and security, prior studies have proposed inlining metafunctions into applications for detecting bugs and security vulnerabilities. However, because these software techniques add frequent, fine-grained instrumentation to programs, they often incur large runtime overheads. In this work, we consider an automatic thread extraction technique for removing these fine-grained checks from a main application and scheduling them on helper threads. In this way, we can leverage the resources available on a CMP to reduce the latency and overhead of fine-grained checking codes. Our parallelization strategy extracts metafunctions from a single threaded application and executes them in customized helper threads—threads constructed to mirror relevant fragments of the main program’s behavior in order to keep communication and overhead low. To get good performance, we consider optimizations that reduce communication and balance work among many threads. We evaluate our parallelization strategy on Mudflap, a pointer-use checking tool in GCC. To show the benefits of our technique, we compare it to a manually parallelized version of Mudflap. We run our experiments on an architectural simulator with support for fast queueing operations. On a subset of SPECint 2000, our automatically parallelized code using static load balance is only 19% slower, on average, than the manually parallelized version on a simulated eight-core system. In addition, our automatically parallelized code using dynamic load balance is competitive, on average, to the manually parallelized version on a simulated eight-core system. Furthermore, all the applications except parser achieve better speedups with our automatic algorithms than with the manual approach. Also, our approach introduces very little overhead in the main program—it is kept under 100%, which is more than a 5.3× reduction compared to serial Mudflap.