Miracle-dz/UniFAR-FTUs huggingface dataset:
Nursing and Clinical Informatics: Socio-technical Approaches

The field of nursing informatics is one of the fastest growing areas of medical informatics. As the industry grows, so does the need for obtaining the most recent, up-to-date research in this significant field of study. Nursing and Clinical Informatics: Socio-Technical Approaches gives a general overview of the current state of nursing informatics paying particular attention to its social, socio-technical, and political aspects to further research and development projects. A unique international comparative work, this book covers the core areas of nursing informatics with a technical and functional respect and portrays them in their proper context.
Collective Learning From Diverse Datasets for Entity Typing in the Wild

Entity typing (ET) is the problem of assigning labels to given entity mentions in a sentence. Existing works for ET require knowledge about the domain and target label set for a given test instance. ET in the absence of such knowledge is a novel problem that we address as ET in the wild. We hypothesize that the solution to this problem is to build supervised models that generalize better on the ET task as a whole, rather than a specific dataset. In this direction, we propose a Collective Learning Framework (CLF), which enables learning from diverse datasets in a unified way. The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets. Then it builds a single neural network classifier using UHLS, label mapping, and a partial loss function. The single classifier predicts the finest possible label across all available domains even though these labels may not be present in any domain-specific dataset. We also propose a set of evaluation schemes and metrics to evaluate the performance of models in this novel problem. Extensive experimentation on seven diverse real-world datasets demonstrates the efficacy of our CLF.
Black Hole Energy in Lovelock Unique Vacuum theories

In this thesis, we explore the possibility to define black hole mass in terms of the Weyl tensor for the entire family of Lovelock-AdS gravity theories. The level of degeneracy $k$ of the corresponding vacuum fixes the number of curvatures that should appear in the energy formula. Therefore, the charge expression which is a polynomial of maximal degree in the curvature, can be consistently truncated to an order $k$ in the Weyl tensor. In particular, for the maximally degenerate case in odd dimensions (Chern-Simons AdS) the expression identically vanishes and the mass must come from the formula that, in the other cases, produces the vacuum energy.
Fuzzy Logic Control for Mixed conventional/braking Actuation Mobile Robots

The use of conventional actuators in robotic systems (electric motors in particular), while often offering advantages in terms of flexibility and controllability, suffer from primary actuator failure,due to unexpected complexities in their environment, which can lead to loss of controllability. Conventional actuators can impose disadvantages on mechanical complexity, weight, and cost. Here,the Mixed conventional/braking Actuation Mobile Robot (MAMR), a new mobile robot platform,is proposed to tackle such drawbacks in actuation and explore the use and control of braking actuation. This platform substitutes the drive motors used in Ackermann steering with brakes that have only two states, ON and OFF. Additionally, the conventional drive wheels are replaced by a single,omni-directional wheel that only supports a driving force in the robots longitudinal direction. The ability of braking actuators in providing controllability under actuator failure is one of the primary motivations of this work. To validate the reliability and accuracy of MAMR approach, this paper studies the design of such robotic systems, the design and synthesis of fuzzy logic controllers along with the experimental assessments of these controllers in real-time. The experimental tests point out the controller performance enhancement using fuzzy logic controllers and MAMR.
$g$-vectors of manifolds with boundary

We extend several $g$-type theorems for connected, orientable homology manifolds without boundary to manifolds with boundary. As applications of these results we obtain Kuhnel-type bounds on the Betti numbers as well as on certain weighted sums of Betti numbers of manifolds with boundary. Our main tool is the completion $\hat\Delta$ of a manifold with boundary $\Delta$; it is obtained from $\Delta$ by coning off the boundary of $\Delta$ with a single new vertex. We show that despite the fact that $\hat{\Delta}$ has a singular vertex, its Stanley--Reisner ring shares a few properties with the Stanley--Reisner rings of homology spheres. We close with a discussion of a connection between three lower bound theorems for manifolds, PL-handle decompositions, and surgery.
MRT-Kriterien zur Differenzierung „pseudotumoröser” Läsionen von Knochensarkomen der Extremitäten

Bei 57 Patienten mit Ewingoder Osteosarkom und 34 Patienten mit hämatogener Osteomyelitis oder Periostitis/Streßfraktur wurden vorliegende MRT auf' morphologische Merkmale untersucht, die evtl. einc Dignit2tsbestimmurig der jeweiligen Veränderung ermöglichen könnten. Die 4 Kriterien waren: Begrenzung der Markraumläsion; Intensität und Homogenität des T I gewichteten Signales im betroffenen Markraum; Vorhandensein einerextraossären, strukturierten Weichteilmasse und/oder eines Weichteilödemes. Das Ergebnis ist, daß der MarkbefaU beim zentralen Osteosarkom und Ewingsarkom eine Signalminderung des Markraurnes auf ungefähr muskeiisointense Werte mit einer gut definierten Grenze zum intakten Fettmark bewirkte. Bei der akuten hämatogenen Osteomyelitis und Periostitis/ Streßfraktur war die Markraumläsion hingegen nicht scharf begrenzt. Hier fanden wir im Gegensatz zu den Knochensarkomen nur bei einem Patienten mit Osteomyelitis eine extraossäre strukturierte Gewebsmasse. Aufgrund dieser Merkmale scheint uns die Unterscheidung vor allem der akuten hiirnatogenen Osteoinyelitis vom Ewingsarkom/zentralen Osteosarkom mit hoher Wahrscheinlichkeit möglich zu sein.
Identifying Research Challenges in Post Quantum Cryptography Migration and Cryptographic Agility

The implications of sufficiently large quantum computers for widely used public-key cryptography is well-documented and increasingly discussed by the security community. An April 2016 report by the National Institute of Standards and Technology (NIST), notably, calls out the need for new standards to replace cryptosystems based on integer factorization and discrete logarithm problems, which have been shown to be vulnerable to Shor's quantum algorithm for prime factorization. Specifically, widely used RSA, ECDSA, ECDH, and DSA cryptosystems will need to be replaced by post-quantum cryptography (PQC) alternatives (also known as quantum-resistant or quantum-safe cryptography). Failure to transition before sufficiently powerful quantum computers are realized will jeopardize the security of public key cryptosystems which are widely deployed within communication protocols, digital signing mechanisms, authentication frameworks, and more. To avoid this, NIST has actively led a PQC standardization effort since 2016, leveraging a large and international research community. On January 31-February 1, 2019, the Computing Community Consortium (CCC) held a workshop in Washington, D.C. to discuss research challenges associated with PQC migration. Entitled, "Identifying Research Challenges in Post Quantum Cryptography Migration and Cryptographic Agility", participants came from three distinct yet related communities: cryptographers contributing to the NIST PQC standards effort, applied cryptographers with expertise in creating cryptographic solutions and implementing cryptography in real-world settings, and industry practitioners with expertise in deploying cryptographic standards within products and compute infrastructures. Discussion centered around two key themes: identifying constituent challenges in PQC migration and imagining a new science of "cryptographic agility".
Bayesian Optimization under Heavy-tailed Payoffs

We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is in contrast to existing literature that typically assumes sub-Gaussian noise distributions for queries. Under the assumption that the unknown function belongs to the Reproducing Kernel Hilbert Space (RKHS) induced by a kernel, we first show that an adaptation of the well-known GP-UCB algorithm with reward truncation enjoys sublinear $\tilde{O}(T^{\frac{2 + \alpha}{2(1+\alpha)}})$ regret even with only the $(1+\alpha)$-th moments, $\alpha \in (0,1]$, of the reward distribution being bounded ($\tilde{O}$ hides logarithmic factors). However, for the common squared exponential (SE) and Mat\'{e}rn kernels, this is seen to be significantly larger than a fundamental $\Omega(T^{\frac{1}{1+\alpha}})$ lower bound on regret. We resolve this gap by developing novel Bayesian optimization algorithms, based on kernel approximation techniques, with regret bounds matching the lower bound in order for the SE kernel. We numerically benchmark the algorithms on environments based on both synthetic models and real-world data sets.
Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds

We propose a neural architecture with the main characteristics of the most successful neural models of the last years : bidirectional RNNs, encoder-decoder, and the Transformer model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for some of them, showing the pertinence of this hybrid architecture for this kind of tasks.
Network Effects on Robustness of Dynamic Systems

We review selected results related to robustness of networked systems in finite and asymptotically large size regimes, under static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small gain type analysis provides tight robustness guarantees for linear dynamics over finite networks towards worst-case and stochastic disturbances. We also discuss network flow dynamic settings where nonlinear techniques facilitate in understanding the effect on robustness of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast the latter with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through the review of these results, the paper aims to achieve two objectives. First, to highlight selected settings in which the role of interconnectivity structure of a network on its robustness is well-understood. Second, to highlight a few additional settings in which existing system theoretic tools give tight robustness guarantees, and which are also appropriate avenues for future network-theoretic investigations.
Graph-Guided Architecture Search for Real-Time Semantic Segmentation

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with a new search space where a lightweight model can be effectively explored through the cell-level diversity and latency oriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with the speed of 108.4 FPS on Titan Xp.
A look at the way we look at Complex Networks Robustness and Resilience

This article offers a brief overview of the current research topics concerning strategies to mitigate the adverse effects of perturbations in complex networks. It addresses the issue of an unclear use of Robustness and Resilience terminology and proposes a common interdisciplinary framework for comparing strategies across different fields. It concludes with a high-level discussion of existing challenges and suggestions for future research.
Sinkhorn Algorithm as a Special Case of Stochastic Mirror Descent

We present a new perspective on the celebrated Sinkhorn algorithm by showing that is a special case of incremental/stochastic mirror descent. In order to see this, one should simply plug Kullback-Leibler divergence in both mirror map and the objective function. Since the problem has unbounded domain, the objective function is neither smooth nor it has bounded gradients. However, one can still approach the problem using the notion of relative smoothness, obtaining that the stochastic objective is 1-relative smooth. The discovered equivalence allows us to propose 1) new methods for optimal transport, 2) an extension of Sinkhorn algorithm beyond two constraints.
Joint Beamforming Design in Multi-Cluster MISO NOMA Intelligent Reflecting Surface-Aided Downlink Communication Networks

Considering intelligent reflecting surface (IRS), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, an IRS assists the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the transmit beamforming vectors at the BS and the reflection coefficient vector at the IRS. Because of the restrictions on the IRS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing (ZF) based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the number of passive reflection elements is relatively high, our proposed ZF-based suboptimal algorithm also outperforms the SDP based algorithm.
Distributed representation of patients and its use for medical cost prediction

Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction. Many existing methods, such as diagnostic Cost Groups (DCG), rely on expert knowledge to build patient representation from medical data, which is resource consuming and non-scalable. Unsupervised machine learning algorithms are a good choice for automating the representation learning process. However, there is very little research focusing on onpatient-level representation learning directly from medical claims. In this paper, weproposed a novel patient vector learning architecture that learns high quality,fixed-length patient representation from claims data. We conducted several experiments to test the quality of our learned representation, and the empirical results show that our learned patient vectors are superior to vectors learned through other methods including a popular commercial model. Lastly, we provide potential clinical interpretation for using our representation on predictive tasks, as interpretability is vital in the healthcare domain
TiM-DNN: Ternary In-Memory Accelerator for Deep Neural Networks

The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex deep neural networks (DNNs). In the quest for lower precision, recent studies have shown that ternary DNNs (which represent weights and activations by signed ternary values) represent a promising sweet spot, achieving accuracy close to full-precision networks on complex tasks. We propose TiM-DNN, a programmable in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted {−1, 0, 1}, symmetric weighted <inline-formula> <tex-math notation="LaTeX">$\{-a,0,a\}$ </tex-math></inline-formula>, and asymmetric weighted <inline-formula> <tex-math notation="LaTeX">$\{-a,0,b\}$ </tex-math></inline-formula> ternary systems. The building blocks of TiM-DNN are TiM tiles—specialized memory arrays that perform massively parallel signed ternary vector–matrix multiplications with a single access. TiM tiles are in turn composed of ternary processing cells (TPCs), bit-cells that function as both ternary storage units and signed ternary multiplication units. We evaluate an implementation of TiM-DNN in 32-nm technology using an architectural simulator calibrated with SPICE simulations and RTL synthesis. We evaluate TiM-DNN across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks. A 32-tile instance of TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9-W power, and occupies 1.96 mm<sup>2</sup> chip area, representing a <inline-formula> <tex-math notation="LaTeX">$300\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$388\times $ </tex-math></inline-formula> improvement in TOPS/W and TOPS/mm<sup>2</sup>, respectively, compared to an NVIDIA Tesla V100 GPU. In comparison to specialized DNN accelerators, TiM-DNN achieves <inline-formula> <tex-math notation="LaTeX">$55\times $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$240\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$160\times $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$291\times $ </tex-math></inline-formula> improvement in TOPS/W and TOPS/mm<sup>2</sup>, respectively. Finally, when compared to a well-optimized near-memory accelerator for ternary DNNs, TiM-DNN demonstrates <inline-formula> <tex-math notation="LaTeX">$3.9\times $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$4.7\times $ </tex-math></inline-formula> improvement in system-level energy and <inline-formula> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$4.2\times $ </tex-math></inline-formula> speedup, underscoring the potential of in-memory computing for ternary DNNs.
Physical Layer Security in UAV Systems: Challenges and Opportunities

Unmanned aerial vehicle (UAV) wireless communications has experienced an upsurge of interest in both military and civilian applications, due to its high mobility, low cost, on-demand deployment, and inherent line-of-sight air-to-ground channels. However, these benefits also make UAV wireless communication systems vulnerable to malicious eavesdropping attacks. In this article, we aim to examine the physical layer security issues in UAV systems. In particular, passive and active eavesdropping are two primary types of attack in UAV systems. We provide an overview on emerging techniques, such as trajectory design, resource allocation, and cooperative UAVs, to fight against both types of eavesdropping in UAV wireless communication systems. Moreover, the applications of non-orthogonal multiple access, multiple-input multiple-output, and millimeter-wave in UAV systems are also proposed to improve the system spectral efficiency and to guarantee security simultaneously. Finally, we discuss some potential research directions and challenges in terms of physical layer security in UAV systems.
Diagnostic tests for acute pancreatitis.

: Serum amylase continues to be the most widely used test to diagnose acute pancreatitis; however, its popularity does not appear to be justified. The serum amylase test has a poor sensitivity and specificity. Furthermore, it has an extremely low sensitivity in detecting acute alcoholic pancreatitis, which is the most common cause of acute pancreatitis in city hospitals. Older assay techniques for serum lipase were cumbersome and time-consuming. The newer methods seem to have overcome the disadvantages of the previous techniques. They are quick, reliable, and inexpensive. Recent studies indicate that serum lipase may be a better test to diagnose acute pancreatitis. Therefore, serum lipase should be used more frequently in the diagnosis of acute pancreatitis. Serum trypsin, although sensitive, is difficult to estimate and is not routinely available. Serum elastase offers no additional benefit over the serum amylase or lipase tests. Markers such as alpha 2-macroglobulin, RNase, phospholipase, and polymorphonuclear elastase predict severity of disease, but assay techniques for these agents are still experimental and confined to specialized centers. C-reactive protein is a reasonably reliable indicator of severity and, because it is universally available, should be used more frequently. Of the imaging techniques, computerized tomography scanning is the best method to delineate the pancreas; however, ultrasound is more cost-effective in clinical practice.
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer. Our tool is characterized by two features, i) recording of word-level revision histories and ii) flexible auxiliary edit support and feedback to annotators. The text rewriting assist and traceable rewriting history are potentially beneficial to the future research of natural language generation.
Degeneration of 3-dimensional hyperbolic cone structures with decreasing cone angles

For 3-dimensional hyperbolic cone structures with cone angles θ \theta , local rigidity is known for 0 ≤ θ ≤ 2 π 0 \leq \theta \leq 2\pi , but global rigidity is known only for 0 ≤ θ ≤ π 0 \leq \theta \leq \pi . The proof of the global rigidity by Kojima is based on the fact that hyperbolic cone structures with cone angles at most π \pi do not degenerate in deformations decreasing cone angles to zero. In this paper, we give an example of a degeneration of hyperbolic cone structures with decreasing cone angles less than 2 π 2\pi . These cone structures are constructed on a certain alternating link in the thickened torus by gluing four copies of a certain polyhedron. For this construction, we explicitly describe the isometry types on such a hyperbolic polyhedron.