Miracle-dz/UniFAR-FTUs huggingface dataset:
Unbiased weighted variance and skewness estimators for overlapping returns

This article develops unbiased weighted variance and skewness estimators for overlapping return distributions. These estimators extend the variance estimation methods constructed in Bod et. al. (Applied Financial Economics 12:155-158, 2002) and Lo and MacKinlay (Review of Financial Studies 1:41-66, 1988). In addition, they may be used in overlapping return variance or skewness ratio tests as in Charles and Darné (Journal of Economic Surveys 3:503-527, 2009) and Wong (Cardiff Economics Working Papers, 2016). An example using synthetic overlapping returns from a model fit to data from the SPY S&P 500 exchange traded fund is given in order to demonstrate under which circumstances the unbiased correction becomes significant in skewness estimation. Finally, we compare the effect of the HAC weighting schemes of Andrews (Econometrica 53:817-858, 1991) as a function of sample size and overlapping return window length.
Clinical trial for the control of water intake of patients undergoing hemodialysis treatment*

ABSTRACT Objective: to analyze the impact of an educational and motivational intervention for patients with a chronic kidney disease, undergoing hemodialysis treatment, on the control of fluid intake during interdialytic periods. Method: a quasi-experimental, non-randomized clinical trial with patients from a Nephrological Unit of the State of São Paulo. Participants were included in two groups: Control Group with 106 patients and Intervention Group with 86 patients, totaling 192 participants. The used intervention was an educational and motivational video to control liquid intake, based on the Bandura’s Theory. The measure of control of water intake was the percentage of lost weight, also considered the variable outcome of the research. For the data analysis, descriptive analyses and regression analysis of the Inflated Beta Model were used. Results: patients who participated in the intervention had a decrease in the pattern of weight gain in interdialytic periods, with a 3.54 times more chance of reaching the goal of 100% of weight loss when compared to participants from the control group. Conclusion: the educational and motivational intervention was effective in reducing the percentage of weight loss in patients undergoing hemodialysis. Brazilian Clinical Trials Registry (ReBEC) under the opinion RBR-4XYTP6.
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph. The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question. In this work, we propose a novel collective classification framework to address this long-standing challenge. We first formulate learning edge weights as an optimization problem, which quantifies the goals about the final reputation scores that we aim to achieve. However, it is computationally hard to solve the optimization problem because the final reputation scores depend on the edge weights in a very complex way. To address the computational challenge, we propose to jointly learn the edge weights and propagate the reputation scores, which is essentially an approximate solution to the optimization problem. We compare our framework with state-of-the-art methods for graph-based security and privacy analytics using four large-scale real-world datasets from various application scenarios such as Sybil detection in social networks, fake review detection in Yelp, and attribute inference attacks. Our results demonstrate that our framework achieves higher accuracies than state-of-the-art methods with an acceptable computational overhead.
Reduction of Parasitic Capacitance of A PDMS Capacitive Force Sensor

Polymer-based flexible micro electro mechanical systems (MEMS) tactile sensors have been widely studied for a variety of applications, such as medical and robot fields. The small size and flexibility are of great advantage in terms of accurate measurement and safety. Polydimethylsiloxane (PDMS) is often used as the flexible structural material. However, the sensors are likely subject to large parasitic capacitance noise. The smaller dielectric constant leads to smaller influences of parasitic capacitance and a larger signal-to-noise ratio. In this study, the sensor underwent ultraviolet (UV) exposure, which changes Si–CH3 bonds in PDMS to Si–O, makes PDMS nanoporous, and leads to a low dielectric constant. In addition, we achieved further reduction of the dielectric constant of PDMS by washing it with an ethanol–toluene buffer solution after UV exposure. This simple but effective method can be readily applicable to improve the signal-to-noise ratio of PDMS-based flexible capacitive sensors. In this study, we propose reduction techniques for the dielectric constant of PDMS and applications for flexible capacitive force sensors.
Prior Networks for Detection of Adversarial Attacks

Adversarial examples are considered a serious issue for safety critical applications of AI, such as finance, autonomous vehicle control and medicinal applications. Though significant work has resulted in increased robustness of systems to these attacks, systems are still vulnerable to well-crafted attacks. To address this problem, several adversarial attack detection methods have been proposed. However, a system can still be vulnerable to adversarial samples that are designed to specifically evade these detection methods. One recent detection scheme that has shown good performance is based on uncertainty estimates derived from Monte-Carlo dropout ensembles. Prior Networks, a new method of estimating predictive uncertainty, has been shown to outperform Monte-Carlo dropout on a range of tasks. One of the advantages of this approach is that the behaviour of a Prior Network can be explicitly tuned to, for example, predict high uncertainty in regions where there are no training data samples. In this work, Prior Networks are applied to adversarial attack detection using measures of uncertainty in a similar fashion to Monte-Carlo Dropout. Detection based on measures of uncertainty derived from DNNs and Monte-Carlo dropout ensembles are used as a baseline. Prior Networks are shown to significantly out-perform these baseline approaches over a range of adversarial attacks in both detection of whitebox and blackbox configurations. Even when the adversarial attacks are constructed with full knowledge of the detection mechanism, it is shown to be highly challenging to successfully generate an adversarial sample.
Exploring the importance of context and embeddings in neural NER models for task-oriented dialogue systems

Named Entity Recognition (NER), a classic sequence labelling task, is an essential component of natural language understanding (NLU) systems in task-oriented dialog systems for slot filling. For well over a decade, different methods from lookup using gazetteers and domain ontology, classifiers over handcrafted features to end-to-end systems involving neural network architectures have been evaluated mostly in language-independent non-conversational settings. In this paper, we evaluate a modified version of the recent state of the art neural architecture in a conversational setting where messages are often short and noisy. We perform an array of experiments with different combinations of including the previous utterance in the dialogue as a source of additional features and using word and character level embeddings trained on a larger external corpus. All methods are evaluated on a combined dataset formed from two public English task-oriented conversational datasets belonging to travel and restaurant domains respectively. For additional evaluation, we also repeat some of our experiments after adding automatically translated and transliterated (from translated) versions to the English only dataset.
PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. Our dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others. Using our dataset, we establish three benchmarking tasks for evaluating 3D part recognition: fine-grained semantic segmentation, hierarchical semantic segmentation, and instance segmentation. We benchmark four state-of-the-art 3D deep learning algorithms for fine-grained semantic segmentation and three baseline methods for hierarchical semantic segmentation. We also propose a baseline method for part instance segmentation and demonstrate its superior performance over existing methods.
New and Improved Algorithms for Unordered Tree Inclusion

The tree inclusion problem is, given two node-labeled trees P and T (the "pattern tree" and the "text tree"), to locate every minimal subtree in T (if any) that can be obtained by applying a sequence of node insertion operations to P. Although the ordered tree inclusion problem is solvable in polynomial time, the unordered tree inclusion problem is NP-hard. The currently fastest algorithm for the latter is from 1995 and runs in O(poly(m,n) * 2^{2d}) = O^*(2^{2d}) time, where m and n are the sizes of the pattern and text trees, respectively, and d is the maximum outdegree of the pattern tree. Here, we develop a new algorithm that improves the exponent 2d to d by considering a particular type of ancestor-descendant relationships and applying dynamic programming, thus reducing the time complexity to O^*(2^d). We then study restricted variants of the unordered tree inclusion problem where the number of occurrences of different node labels and/or the input trees' heights are bounded. We show that although the problem remains NP-hard in many such cases, it can be solved in polynomial time for c = 2 and in O^*(1.8^d) time for c = 3 if the leaves of P are distinctly labeled and each label occurs at most c times in T. We also present a randomized O^*(1.883^d)-time algorithm for the case that the heights of P and T are one and two, respectively.
A Novel Image Dehazing Algorithm Based on Dual-tree Complex Wavelet Transform

The quality of natural outdoor images captured by visible camera sensors is usually degraded by the haze present in the atmosphere. In this paper, a fast image dehazing method based on visible image and near-infrared fusion is proposed. In the proposed method, a visible and a near-infrared (NIR) image of the same scene is fused based on the dual-tree complex wavelet transform (DT-CWT) to generate a dehazed color image. The color of the fusion image is regulated through haze concentration estimated by dark channel prior (DCP). The experiment results demonstrate that the proposed method outperforms the conventional dehazing methods and effectively solves the color distortion problem in the dehazing process.
Active Learning Methods based on Statistical Leverage Scores

In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. To assess the representativeness of the examples in the pool, ALEVS and DBALEVS use the statistical leverage scores of the kernel matrices computed on the examples of each class. Additionally, DBALEVS selects a diverse a set of examples that are highly representative but are dissimilar to already labeled examples through maximizing a submodular set function defined with the statistical leverage scores and the kernel matrix computed on the pool of the examples. The submodularity property of the set scoring function let us identify batches with a constant factor approximate to the optimal batch in an efficient manner. Our experiments on diverse datasets show that querying based on leverage scores is a powerful strategy for active learning.
Methodologic recommendations and possible interpretations of video‐EEG recordings in immature rodents used as experimental controls: A TASK1‐WG2 report of the ILAE/AES Joint Translational Task Force

The use of immature rodents to study physiologic aspects of cortical development requires high‐quality recordings electroencephalography (EEG) with simultaneous video recording (vEEG) of behavior. Normative developmental vEEG data in control animals are fundamental for the study of abnormal background activity in animal models of seizures or other neurologic disorders. Electrical recordings from immature, freely behaving rodents can be particularly difficult because of the small size of immature rodents, their thin and soft skull, interference with the recording apparatus by the dam, and other technical challenges. In this report of the TASK1 Working Group 2 (WG2) of the International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force, we provide suggestions that aim to optimize future vEEG recordings from immature rodents, as well as their interpretation. We focus on recordings from immature rodents younger than 30 days old used as experimental controls, because the quality and correct interpretation of such recordings is important when interpreting the vEEG results of animals serving as models of neurologic disorders. We discuss the technical aspects of such recordings and compare tethered versus wireless approaches. We also summarize the appearance of common artifacts and various patterns of electrical activity seen in young rodents used as controls as a function of behavioral state, age, and (where known) sex and strain. The information herein will hopefully help improve the methodology of vEEG recordings from immature rodents and may lead to results and interpretations that are more consistent across studies from different laboratories.
Regional Homogeneity and Multivariate Pattern Analysis of Cervical Spondylosis Neck Pain and the Modulation Effect of Treatment

Objects: We investigated brain functional alteration in patients with chronic cervical spondylosis neck pain (CSNP) compared to healthy controls (HCs) and the effect of intervention. Methods: 104 CSNP patients and 96 matched HCs were recruited. Patients received 4 weeks of treatment. Resting-state fMRI and Northwick Park Neck Pain Questionnaire (NPQ) were collected before and after treatment. Resting state regional homogeneity (rs-ReHo) and multivariate pattern analysis (MVPA) were applied to (1) investigate rs-ReHo differences between CSNP patients and controls and the effect of longitudinal treatment and (2) classify CSNP patients from HCs and predict clinical outcomes before treatment using MVPA. Results: We found that (1) CSNP patients showed decreased rs-ReHo in the left sensorimotor cortex and right temporo-parietal junction (rTPJ), and rs-ReHo at the rTPJ significantly increased after treatment; (2) rs-ReHo at rTPJ was associated with NPQ at baseline, and pre- and post-treatment rs-ReHo changes at rTPJ were associated with NPQ changes in CSNP patients; and (3) MVPA could discriminate CSNP patients from HCs with 72% accuracy and predict clinical outcomes with a mean absolute error of 19.6%. Conclusion: CSNP patients are associated with dysfunction of the rTPJ and sensorimotor area. Significance: rTPJ plays on important role in the pathophysiology and development of CSNP.
FMonE: A Flexible Monitoring Solution at the Edge

Monitoring has always been a key element on ensuring the performance of complex distributed systems, being a first step to control quality of service, detect anomalies, or make decisions about resource allocation and job scheduling, to name a few. Edge computing is a new type of distributed computing, where data processing is performed by a large number of heterogeneous devices close to the place where the data is generated. Some of the differences between this approach and more traditional architectures, like cloud or high performance computing, are that these devices have low computing power, have unstable connectivity, and are geo-distributed or even mobile. All of these aforementioned characteristics establish new requirements for monitoring tools, such as customized monitoring workflows or choosing different back-ends for the metrics, depending on the device hosting them. In this paper, we present a study of the requirements that an edge monitoring tool should meet, based on motivating scenarios drawn from literature. Additionally, we implement these requirements in a monitoring tool named FMonE. This framework allows deploying monitoring workflows that conform to the specific demands of edge computing systems. We evaluate FMonE by simulating a fog environment in the Grid’5000 testbed and we demonstrate that it fulfills the requirements we previously enumerated.
Improved Person Detection on Omnidirectional Images with Non-maxima Supression

We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: non-maximum suppression, soft non-maximum suppression and soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection

Distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. Naive classification approaches based on Convolutional Neural Networks (CNNs) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. However, on examples from unseen manipulation approaches, their performance drops significantly. To address this limitation in transferability, we introduce Forensic-Transfer (FT). We devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training. To this end, we learn a forensic embedding based on a novel autoencoder-based architecture that can be used to distinguish between real and fake imagery. The learned embedding acts as a form of anomaly detector; namely, an image manipulated from an unseen method will be detected as fake provided it maps sufficiently far away from the cluster of real images. Comparing to prior works, FT shows significant improvements in transferability, which we demonstrate in a series of experiments on cutting-edge benchmarks. For instance, on unseen examples, we achieve up to 85% in terms of accuracy, and with only a handful of seen examples, our performance already reaches around 95%.
AFM Characterization of the Internal Mammary Artery as a Novel Target for Arterial Stiffening

Using the atomic force microscopy- (AFM-) PeakForce quantitative nanomechanical mapping (QNM) technique, we have previously shown that the adventitia of the human internal mammary artery (IMA), tested under dehydrated conditions, is altered in patients with a high degree of arterial stiffening. In this study, we explored the nanoscale elastic modulus of the tunica media of the IMA in hydrated and dehydrated conditions from the patients with low and high arterial stiffening, as assessed in vivo by carotid-femoral pulse wave velocity (PWV). In both hydrated and dehydrated conditions, the medial layer was significantly stiffer in the high PWV group. The elastic modulus of the hydrated and dehydrated tunica media was significantly correlated with PWV. In the hydrated condition, the expression activity of certain small leucine-rich repeat proteoglycans (SLRPs), which are associated with arterial stiffening, were found to be negatively correlated to the medial elastic modulus. We also compared the data with our previous work on the IMA adventitia. We found that the hydrated media and dehydrated adventitia are both suitable for reflecting the development of arterial stiffening and SLRP expression. This comprehensive study of the nanomechanical properties integrated with the proteomic analysis in the IMAs demonstrates the possibility of linking structural properties and function in small biological samples with novel AFM methods. The IMA is a suitable target for predicting arterial stiffening.
Use of a deflectable tip catheter to facilitate complex interventions beyond insertion of coronary bypass grafts: Three case reports

We present three patients with angulated ostial lesions beyond the insertion point of coronary artery bypass grafts. Use of the Venture Catheter, a deflectable tip 3.5F wire control catheter, facilitated successful percutaneous coronary intervention after prolonged, unsuccessful attempts at conventional guidewire passage. © 2007 Wiley‐Liss, Inc.
Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder

Although state-of-the-art parallel WaveNet has addressed the issue of real-time waveform generation, there remains problems. Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms. Secondly, a parallel WaveNet is trained under a distillation framework, which makes it tedious to adapt a well trained model to a new speaker. To address these two problems, in this paper we propose an end-to-end adaptation method based on the generative adversarial network (GAN), which can reduce the computational cost for the training of new speaker adaptation. Our subjective experiments shows that the proposed training method can further reduce the quality gap between generated and natural waveforms.