Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE (Digital Academic Repository) Clinical review: Practical recommendations on the management of perioperative heart failure in cardiac surgery Mebazaa,

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Egocentric Visitors Localization in Cultural Sites

We consider the problem of localizing visitors in a cultural site from egocentric (first-person) images. Localization information can be useful both to assist the user during his visit (e.g., by suggesting where to go and what to see next) and to provide behavioral information to the manager of the cultural site (e.g., how much time has been spent by visitors at a given location? What has been liked most? ). To tackle the problem, we collected a large dataset of egocentric videos using two cameras: a head-mounted HoloLens device and a chest-mounted GoPro. Each frame has been labeled according to the location of the visitor and to what he was looking at. The dataset is freely available in order to encourage research in this domain. The dataset is complemented with baseline experiments performed considering a state-of-the-art method for location-based temporal segmentation of egocentric videos. Experiments show that compelling results can be achieved to extract useful information for both the visitor and the site-manager.
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community labels, a stochastic block model (SBM) is assumed for each layer. We develop an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities. In doing so, we address some unique challenges posed by coupling the complex likelihood of SBM with the hierarchical nature of the prior on the labels. An extensive empirical validation is performed on simulated and real data, demonstrating the superior performance of the model over single-layer alternatives, as well as the ability to uncover interesting structures in real networks.
NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier

The paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI’s GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.
On Zero-Shot Recognition of Generic Objects

Many recent advances in computer vision are the results of a healthy competition among researchers on high quality, task-specific, benchmarks. After a decade of active research, zero-shot learning (ZSL) models accuracy on the Imagenet benchmark remains far too low to be considered for practical object recognition applications. In this paper, we argue that the main reason behind this apparent lack of progress is the poor quality of this benchmark. We highlight major structural flaws of the current benchmark and analyze different factors impacting the accuracy of ZSL models. We show that the actual classification accuracy of existing ZSL models is significantly higher than was previously thought as we account for these flaws. We then introduce the notion of structural bias specific to ZSL datasets. We discuss how the presence of this new form of bias allows for a trivial solution to the standard benchmark and conclude on the need for a new benchmark. We then detail the semi-automated construction of a new benchmark to address these flaws.
Few-shot Learning: A Survey

The quest of `can machines think' and `can machines do what human do' are quests that drive the development of artificial intelligence. Although recent artificial intelligence succeeds in many data intensive applications, it still lacks the ability of learning from limited exemplars and fast generalizing to new tasks. To tackle this problem, one has to turn to machine learning, which supports the scientific study of artificial intelligence. Particularly, a machine learning problem called Few-Shot Learning (FSL) targets at this case. It can rapidly generalize to new tasks of limited supervised experience by turning to prior knowledge, which mimics human's ability to acquire knowledge from few examples through generalization and analogy. It has been seen as a test-bed for real artificial intelligence, a way to reduce laborious data gathering and computationally costly training, and antidote for rare cases learning. With extensive works on FSL emerging, we give a comprehensive survey for it. We first give the formal definition for FSL. Then we point out the core issues of FSL, which turns the problem from "how to solve FSL" to "how to deal with the core issues". Accordingly, existing works from the birth of FSL to the most recent published ones are categorized in a unified taxonomy, with thorough discussion of the pros and cons for different categories. Finally, we envision possible future directions for FSL in terms of problem setup, techniques, applications and theory, hoping to provide insights to both beginners and experienced researchers.
Deep neural networks ensemble for detecting medication mentions in tweets

Abstract Objective Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names suffer from low recall due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Materials and Methods We present Kusuri, an Ensemble Learning classifier able to identify tweets mentioning drug products and dietary supplements. Kusuri (薬, “medication” in Japanese) is composed of 2 modules: first, 4 different classifiers (lexicon based, spelling variant based, pattern based, and a weakly trained neural network) are applied in parallel to discover tweets potentially containing medication names; second, an ensemble of deep neural networks encoding morphological, semantic, and long-range dependencies of important words in the tweets makes the final decision. Results On a class-balanced (50-50) corpus of 15 005 tweets, Kusuri demonstrated performances close to human annotators with an F1 score of 93.7%, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 112 Twitter users (98 959 tweets, with only 0.26% mentioning medications), Kusuri obtained an F1 score of 78.8%. To the best of our knowledge, Kusuri is the first system to achieve this score on such an extremely imbalanced dataset. Conclusions The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness, and is ready to be integrated in pharmacovigilance, toxicovigilance, or more generally, public health pipelines that depend on medication name mentions.
Opportunistic content dissemination in mobile social networks via adjustment of user selfishness

By increase in smart phone penetration rate, mobile social networks (MSNs) become more popular. In such networks, users can exchange and share information via peer-to-peer opportunistic wireless connections. Wireless connections are prone to failures, devices are battery-powered, and the buffer space is limited. These lead to uncertainty in connections and selfish behaviours in dissemination processes. Hence, information dissemination in MSNs becomes a challenge. In this study, the authors analyse the information dissemination in MSNs with selfish users from different communities. They develop an analytical model through ordinary differential equations to analyse the dissemination process in MSNs. Then, they propose an optimisation problem to find the optimal forwarding probabilities of users. They employ the branch and bound-outer approximation algorithm to analytically solve the optimisation problem. The analytical results represent that the optimal forwarding probability of users diminished by increasing the number of relay users, which accelerate the dissemination in the network. Also, these results represent that the proposed algorithm to find the optimal selfishness vector can improve the network performance by decreasing the dissemination delay.
A review on Neural Turing Machine

One of the major objectives of Artificial Intelligence is to design learning algorithms that are executed on a general purposes computational machines such as human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. The attempt is made here to run a systematic review on Neural Turing Machine. First, the mind-map and taxonomy of machine learning, neural networks, and Turing machine are introduced. Next, NTM is inspected in terms of concepts, structure, variety of versions, implemented tasks, comparisons, etc. Finally, the paper discusses on issues and ends up with several future works.
Testing unateness nearly optimally

We present an Õ(n2/3/є2)-query algorithm that tests whether an unknown Boolean function f∶{0,1}n→ {0,1} is unate (i.e., every variable is either non-decreasing or non-increasing) or є-far from unate. The upper bound is nearly optimal given the Ω(n2/3) lower bound of Chen, Waingarten and Xie (2017). The algorithm builds on a novel use of the binary search procedure and its analysis over long random paths.
Autoencoder-Based Articulatory-to-Acoustic Mapping for Ultrasound Silent Speech Interfaces

When using ultrasound video as input, Deep Neural Network-based Silent Speech Interfaces usually rely on the whole image to estimate the spectral parameters required for the speech synthesis step. Although this approach is quite straightforward, and it permits the synthesis of understandable speech, it has several disadvantages as well. Besides the inability to capture the relations between close regions (i.e. pixels) of the image, this pixelby-pixel representation of the image is also quite uneconomical. It is easy to see that a significant part of the image is irrelevant for the spectral parameter estimation task as the information stored by the neighbouring pixels is redundant, and the neural network is quite large due to the large number of input features. To resolve these issues, in this study we train an autoencoder neural network on the ultrasound image; the estimation of the spectral speech parameters is done by a second DNN, using the activations of the bottleneck layer of the autoencoder network as features. In our experiments, the proposed method proved to be more efficient than the standard approach: the measured normalized mean squared error scores were lower, while the correlation values were higher in each case. Based on the result of a listening test, the synthesized utterances also sounded more natural to native speakers. A further advantage of our proposed approach is that, due to the (relatively) small size of the bottleneck layer, we can utilize several consecutive ultrasound images during estimation without a significant increase in the network size, while significantly increasing the accuracy of parameter estimation.
Contextual Attention for Hand Detection in the Wild

We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: https://www3.cs. stonybrook.edu/~cvl/projects/hand_det_attention/
Predicative proof theory of PDL and basic applications

Propositional dynamic logic (PDL) is presented in Schutte-style mode as one-sided semiformal tree-like sequent calculus Seq pdl ω with standard cut rule and the omega-rule with principal formulas [P * ]A. The omega-rule-free derivations in Seq pdl ω are finite (trees) and sequents deducible by these finite derivations are valid in PDL. Moreover the cut-elimination theorem for Seq pdl ω is provable in Peano Arithmetic (PA) extended by transfinite induction up to Veblen's ordinal ϕ ω (0). Hence (by the cutfree subformula property) such predicative extension of PA proves that any given [P * ]-free sequent is valid in PDL iff it is deducible in Seq pdl ω by a finite cut-and omega-rule-free derivation, while PDL-validity of arbitrary star-free sequents is decidable in polynomial space. The former also implies a Herbrand-style conclusion that e.g. a given formula S = P * A ∨ Z for star-free A and Z is valid in PDL iff there is a k ≥ 0 and a cut-and omega-rule-free derivation of sequent A, P 1 A, · · · , P k A, B where P i A is an abbreviation for P · · · P i times A. This eventually leads to PSPACE-decidability of PDL-validity of S, provided that P is atomic and A is in a suitable basic conjunctive normal form. Furthermore we consider star-free formulas A in dual basic disjunctive normal form, and corresponding expansions S = P * A ∨ Z whose PDL-validity problem is known to be EXPTIME-complete. We show that cutfree-derivability in Seq pdl ω (hence PDL-validity) of such S is equivalent to plain validity of a suitable "transparent" quantified boolean formula S. Hence EXPTIME = PSPACE holds true iff the validity problem for any S involved is solvable by a polynomial-space deterministic TM. This may reduce the former problem to a more transparent complexity problem in quantified boolean logic. The whole proof can be formalized in PA extended by transfinite induction along ϕ ω (0)-actually in the corresponding primitive recursive weakening, PRA ϕ ω (0).
Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover predictive AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03$ \pm 17.25 $ years, and is characterized by mild loss of kidney excretory function (Serum Creatinne (SCr) $1.55\pm 0.34$ mg/dL, estimated Glomerular Filtration Rate Test (eGFR) $107.65\pm 54.98$ mL/min/1.73$m^2$). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81$ \pm 10.43 $ years, and was characterized by severe loss of kidney excretory function (SCr $1.96\pm 0.49$ mg/dL, eGFR $82.19\pm 55.92$ mL/min/1.73$m^2$). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm 11.32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1.69\pm 0.32$ mg/dL, eGFR $93.97\pm 56.53$ mL/min/1.73$m^2$). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.
Novel Quadrotor Manipulation System

This thesis introduces a novel quadrotor manipulation system that consists of 2-link manipulator attached to the bottom of a quadrotor. This new system presents a solution for the drawbacks found in the current quadrotor manipulation system which uses a gripper fixed to a quadrotor. Unlike the current system, the proposed system has a 6-DOF, and it provides enough distance between the quadrotor and the object. System kinematics and dynamics are derived. To study the feasibility of the proposed system, a quadrotor with high enough payload to add the 2-link manipulator is constructed. Its parameters are identified to be used in the simulation and controller design. A CAD model is developed to calculate the mass and moments of inertia in an accurate way. Direct relationships between Pulse Width Modulation and each of the angular speeds, thrust forces, and drag moments of the rotors are identified. A Direction Cosine Matrix complementary filter is used to estimate the attitude of the quadrotor using the IMU measurements. Attitude stabilization controller is designed based on feedback linearization technique to test the identified parameters and the attitude estimation. The results of the experiments show satisfactory accuracy of the identified structure parameters, the identified rotor assembly parameters, and the attitude estimation algorithm. A controller for the proposed system is designed based on three control techniques: feedback linearization based PID control, direct fuzzy logic control, and fuzzy model reference learning control. These controllers are tested to provide system stability and trajectory tracking under the effect of picking and placing a payload and the effect of changing the operating region. Simulation results show that the fuzzy model reference learning control technique has superior performance. The results indicate the feasibility of the proposed system.
Quality Improvement : A Systematic Review and Future Directions for Research

The federally qualified community CHC program is a growing federal program that provides primary health care to more than 15 million vulnerable people nationwide and has an annual budget of approxi­ mately $1.5 billion dollars.1 Currently, the CHC program faces the challenge of continually improving its quality of care despite rising numbers of uninsured and underinsured people and a presidential mandate to increase CHC capacity and number. The program’s ability to meet this challenge is considered critically important to our nation’s ability to Abstract Background: Leaders and policymakers need an accurate appraisal of the federally qualified community health center (CHC) quality improvement (QI) literature to make in­ formed decisions for the CHC program.
A 3-D TCAD Framework for NBTI—Part I: Implementation Details and FinFET Channel Material Impact

The time kinetics of interface trap generation and passivation (<inline-formula> <tex-math notation="LaTeX">$\Delta {N} _{\textsf {IT}}$ </tex-math></inline-formula>) and its contribution (<inline-formula> <tex-math notation="LaTeX">$\Delta {V} _{\textsf {IT}}$ </tex-math></inline-formula>) during and after negative bias temperature instability (NBTI) stress is calculated by using Sentaurus TCAD. The framework consists of Sentaurus process for the formation of realistic device structures and to obtain material and strain information, while Sentaurus device is used to implement the double-interface reaction–diffusion (RD) model for trap kinetics. Capture–emission depassivation (CED) model is used for the inversion layer hole and oxide electric field (<inline-formula> <tex-math notation="LaTeX">${E} _{\textsf {OX}}$ </tex-math></inline-formula>)-assisted breaking of hydrogen (H) passivated defects at channel/gate insulator interface. Multistate configuration (MSC) model is used for diffusion of atomic and molecular hydrogen (H and H<sub>2</sub>) and further reactions with other H passivated defects in gate insulator bulk. Quantum effects are considered for hole density, and band structure calculation is used including mechanical strain for CED model parameters. Electron capture in the generated traps during recovery is calculated using transient trap occupancy model (TTOM) and implemented as a postprocessor. Measured data from silicon (Si) and silicon germanium (SiGe) p-channel FinFETs are used for validating the TCAD framework.
Cytotoxic Withanolides from the Whole Herb of Physalis angulata L.

Physalis angulata L. is a medicinal plant of the Solanaceae family, which is used to produce a variety of steroids. The present study reports on the cytotoxic withanolides of this plant. The species of Physalis angulata L. was identified by DNA barcoding techniques. Two new withanolides (1–2), together with six known analogues (3–8), were isolated from the whole plant of Physalis angulata L. The structures of these new compounds were determined on the basis of extensive spectroscopic data analyses and electronic circular dichroism (ECD) calculations. The withanolides exhibited strong cytotoxic activities against A549, Hela and p388 cell lines. Furthermore, compounds 1 and 2 induced typical apoptotic cell death in A549 cell line according to the evaluation of the apoptosis-inducing activity by flow cytometric analysis.