Miracle-dz/UniFAR-FTUs huggingface dataset:
Parental Perspectives on Psychiatric Comorbidity in Preschoolers With Autism Spectrum Disorders Receiving Publicly Funded Mental Health Services

An increased prevalence of psychiatric comorbidity (PC) in individuals with Autism Spectrum Disorders (ASD) is consistently reported. While several studies have examined PC in school-aged children, adolescents and adults with ASD, investigations on PC in preschoolers are less common. In this study, we explore the prevalence and the type of PC in a sample of 989 preschoolers with ASD through the DSM-Oriented Scales (DOS) of the Child Behavior Checklist (CBCL 1½-5) and their possible links with the core features of ASD and cognitive functioning. Results indicated that 37.8% of the sample had at least one PC in addition to ASD; these subjects displayed significantly higher Total score (p = 0.02) and Social Affect score (p = 0.003) on the ADOS-based calibrated severity scores (CSS), as well as lower (p ≤ 0.0001) performance IQ (pIQ) compared to ASD individuals without PC. As far as the specific DOS, Affective Problems (AP) were detected in 23.4% of the whole sample, ADHD Problems (ADHD) in 17.3%, Anxiety Problems (AXP) in 16.7%, and Oppositional Problems (OP) in 7.9%. These different comorbidities were isolated in 195 subjects (Mono-comorbid group: 19.7% of the whole sample), while 179 subjects (18.1% of the whole sample) had two or more types of PC (Multi-comorbid group). One-way ANOVA revealed that subjects with multi-comorbidity have statistically significant lower pIQ and higher Total score and Social Affect score on CSS-ADOS. Specific differences for each type of comorbidity and gender differences were also discussed. Taken together, results indicate a considerable presence of PC in preschoolers with ASD that should be accurately considered during the assessment and diagnosis process in order to plan a tailored intervention based not only on core symptoms of ASD, but also on comorbid psychiatric condition since preschool age.
A new strategy to promote flu vaccination among health care workers: Molinette Hospital's experience.

RATIONALE, AIMS, AND OBJECTIVES Seasonal flu represents a major public health issue, especially for health care workers (HCWs). WHO highlights the need of higher awareness and flu vaccine coverage among HCWs. In Europe the coverage is less than 30-40%. Italy reports some of the lowest rates. The paper aims to illustrate the 2017/2018 flu vaccination campaign within Molinette, the third Italian hospital for dimensions, to provide inputs about strategies for increasing HCWs coverage. The campaign objective was to increase the administered doses at least of 30%. METHODS The intervention included informational material, direct educational sessions for workers, extension of the access time to Occupational Medicine Service, elimination of the reservation requirement, composition of "moving vaccination units" (MVUs), and organisation of vaccination sessions within departments. RESULTS In 2017/2018, 593 doses were administered. The doses percentage change between 2017/2018 and 2016/2017 vaccination seasons was +46.06%, while it was +84.74% compared with the previous 5-year period mean. The majority was administered by Occupational Medicine Service, while 6.75% by MVUs. Among the total doses, 72.68% were administered to workers, 13.49% to residents, 6.75% to students, and 7.08% to "other". So, 7.68% of total workforce was vaccinated. Only 0.3% of vaccinated people presented mild adverse reactions. CONCLUSIONS A combined campaign, that includes actions for education, increase of awareness, improved access to facilities and active offers to workers has potentiality but there is still work to do. The vaccination increase was determined mostly by the re-organization of the occupational medicine, while the MVUs were useful to HCWs of detached offices.
Stochastic Incremental Algorithms for Optimal Transport with SON Regularizer

We introduce a new regularizer for optimal transport (OT) which is tailored to better preserve the class structure. We give the first theoretical guarantees for an OT scheme that respects class structure. We give an accelerated proximal--projection scheme for this formulation with the proximal operator in closed form to give a highly scalable algorithm for computing optimal transport plans. We give a novel argument for the uniqueness of the optimum even in the absence of strong convexity. Our experiments show that the new regularizer preserves class structure better and is more robust compared to previous regularizers.
Remote sensing in the survey of mountain pine beetle impacts: Review and recommendations

Résumé . v 1 Executive Summary . 1 2 Introduction . 2 3 Context of review . 4 3.1 Definitions . 4 3.2 Business drivers . 4 4 Phenology of the symptoms of mountain pine beetle attack . 5 5 Technological considerations of remote sensing . 8 5.1 Aerial photography and sampling strategies . 8 5.2 Planning digital data collection . 11 5.3 Assessing accuracy of results . 16 5.4 Factors affecting costs . 18 6 Remote sensing detection and mapping of forest damage – review of published results . 19 6.1 Green-attack . 19 6.1. 1 General results of detecting non-visual symptoms of stress . 19 6.1. 2 Specific studies detecting green-attack . 22 6.1. 3 Research trends, gaps, and recommendations . 23 6.2 Red-attack . 24 6.2. 1 General results of mapping foliar damage . 24 6.2. 2 Specific studies mapping red-attack . 25 6.2. 3 Research trends, gaps, and recommendations . 36 6.3 Grey-attack . 37 6.3.1 General results of mapping complete defoliation . 37 6.3.2 Specific studies mapping grey-attack . 39 6.3. 3 Research trends, gaps, and recommendations . 41 7 Conclusion . 41 8 Recommendations . 43 Scheduling of data collection: . 43 Provincial scale operations: . 44 Landscape scale operations: . 44 Local scale operations: . 44 Green-attack research: . 44 Red-attack research: . 45 Grey-attack research: . 45 Matching data sources to information needs: . 45 9 Acknowledgements . 46 10 References . 47 Mention in this publication of specific commercial products or services does not constitute endorsement of such by the Canadian Forest Service or the Government of Canada.
Log-seed pseudorandom generators via iterated restrictions

There are only a few known general approaches for constructing explicit pseudorandom generators (PRGs). The "iterated restrictions" approach, pioneered by Ajtai and Wigderson [2], has provided PRGs with seed length polylog n or even Õ(log n) for several restricted models of computation. Can this approach ever achieve the optimal seed length of O(log n)? In this work, we answer this question in the affirmative. Using the iterated restrictions approach, we construct an explicit PRG for read-once depth-2 AC0[⊕] formulas with seed length [MATH HERE]. In particular, we achieve optimal seed length O(log n) with near-optimal error ε = exp(-ΩT(log n)). Even for constant error, the best prior PRG for this model (which includes read-once CNFs and read-once F2-polynomials) has seed length Θ(log n · (log log n)2) [22]. A key step in the analysis of our PRG is a tail bound for subset-wise symmetric polynomials, a generalization of elementary symmetric polynomials. Like elementary symmetric polynomials, subset-wise symmetric polynomials provide a way to organize the expansion of [MATH HERE]. Elementary symmetric polynomials simply organize the terms by degree, i.e., they keep track of the number of variables participating in each monomial. Subset-wise symmetric polynomials keep track of more data: for a fixed partition of [m], they keep track of the number of variables from each subset participating in each monomial. Our tail bound extends prior work by Gopalan and Yehudayoff [17] on elementary symmetric polynomials.
Aetiology and outcome of acute diarrhoea in children with severe acute malnutrition: a comparative study

Abstract Objective: To compare the microbiological profile, clinical course and outcome of acute diarrhoea in children aged <5 years having severe acute malnutrition (SAM) with those of children having normal nutritional status. Design: Cross-sectional comparative study. Setting: Tertiary-care hospital catering mainly to the urban poor of East Delhi, India. Participants: Children aged <5 years (n 140; seventy with SAM (cases) and seventy with normal anthropometry (controls)) with acute diarrhoea (duration < 14 d). Stool samples were collected for conventional culture, microscopy, acid-fast staining, rotavirus and Cryptosporidium antigen detection, and subtyping of diarrhoeagenic Escherichia coli (DEC). We followed-up these children for persistent diarrhoea and subsequent diarrhoeal episode in the next 3 months. Results: Rotavirus was detected in six (9 %) cases and in fifteen (21 %) controls (P = 0·03; OR = 0·34; 95 % CI 0·12, 0·94). DEC was isolated significantly more in cases compared with controls (93 v. 64 %; P < 0·001; OR = 7·25; 95 % CI 2·57, 20·4). Cryptosporidium was detected in seven (10 %) cases and five (7 %) controls. Total duration of diarrhoea and percentage change in weight after resolution of diarrhoea were comparable between cases and controls. At 3-month follow-up, number of subsequent episodes of diarrhoea and persistent diarrhoea were comparable between the two groups. Conclusions: Rotavirus was found significantly less frequently, whereas DEC was detected more frequently in children with SAM in comparison to non-malnourished children. To further reduce diarrhoea-related mortality, preventive and therapeutic interventions need to be designed against organisms causing diarrhoea in children with SAM.
Emotional Memory Moderates the Relationship Between Sigma Activity and Sleep-Related Improvement in Affect

Sleep is essential for regulating mood and affect, and it also consolidates emotional memories. The mechanisms underlying these effects may overlap. Here, we investigated whether the influence of sleep on affect may be moderated by emotional memory consolidation. Young adults viewed 45 negative and 45 neutral pictures before taking an afternoon nap measured with polysomnography. Following the nap period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Affect was measured with the Positive and Negative Affect Schedule (PANAS) at baseline before the initial picture viewing task, immediately following the initial picture viewing task, and following the nap. The ratio of positive to negative affect declined over the task period and recovered over the nap period. When controlling for pre-nap affect, NREM sigma activity significantly predicted post-nap affect. Memory for negative pictures moderated this relationship such that a positive association between sigma activity and affect occurred when memory was low but not when memory was high. These results indicate that emotional memory consolidation influences the relationship between nap physiology and mood.
ADS-ME: Anomaly Detection System for Micro-expression Spotting

Micro-expressions (MEs) are infrequent and uncontrollable facial events that can highlight emotional deception and appear in a high-stakes environment. This paper propose an algorithm for spatiotemporal MEs spotting. Since MEs are unusual events, we treat them as abnormal patterns that diverge from expected Normal Facial Behaviour (NFBs) patterns. NFBs correspond to facial muscle activations, eye blink/gaze events and mouth opening/closing movements that are all facial deformation but not MEs. We propose a probabilistic model to estimate the probability density function that models the spatiotemporal distributions of NFBs patterns. To rank the outputs, we compute the negative log-likelihood and we developed an adaptive thresholding technique to identify MEs from NFBs. While working only with NFBs data, the main challenge is to capture intrinsic spatiotemoral features, hence we design a recurrent convolutional autoencoder for feature representation. Finally, we show that our system is superior to previous works for MEs spotting.
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation

Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning. The Byzantine model captures workers that behave arbitrarily, including malicious and compromised workers. In this paper, we break two prevailing Byzantine-tolerant techniques. Specifically we show robust aggregation methods for synchronous SGD -- coordinate-wise median and Krum -- can be broken using new attack strategies based on inner product manipulation. We prove our results theoretically, as well as show empirical validation.
Joint Models for Event Prediction From Time Series and Survival Data

Abstract We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.
Soft-tissue prostate intrafraction motion tracking in 3D cine-MR for MR-guided radiotherapy

To develop a method to automatically determine intrafraction motion of the prostate based on soft tissue contrast on 3D cine-magnetic resonance (MR) images with high spatial and temporal resolution. Twenty-nine patients who underwent prostate stereotactic body radiotherapy (SBRT), with four implanted cylindrical gold fiducial markers (FMs), had cine-MR imaging sessions after each of five weekly fractions. Each cine-MR session consisted of 55 sequentially obtained 3D data sets (‘dynamics’) and was acquired over an 11 s period, covering a total of 10 min. The prostate was delineated on the first dynamic of every dataset and this delineation was used as the starting position for the soft tissue tracking (SST). Each subsequent dynamic was rigidly aligned to the first dynamic, based on the contrast of the prostate. The obtained translation and rotation describes the intrafraction motion of the prostate. The algorithm was applied to 6270 dynamics over 114 scans of 29 patients and the results were validated by comparing to previously obtained fiducial marker tracking data of the same dataset. Our proposed tracking method was also retro-perspectively applied to cine-MR images acquired during MR-guided radiotherapy of our first prostate patient treated on the MR-Linac. The difference in the 3D translation results between the soft tissue and marker tracking was below 1 mm for 98.2% of the time. The mean translation at 10 min were X: 0.0 0.8 mm, Y: 1.0 1.8 mm and Z: mm. The mean rotation results at 10 min were X: , Y: 0.1 0.6° and Z: 0.0 0.7°. A fast, robust and accurate SST algorithm was developed which obviates the need for FMs during MR-guided prostate radiotherapy. To our knowledge, this is the first data using full 3D cine-MR images for real-time soft tissue prostate tracking, which is validated against previously obtained marker tracking data.
Asynchronous Federated Optimization

Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergence to a global optimum, for both strongly and non-strongly convex problems, as well as a restricted family of non-convex problems. Empirical results show that the proposed algorithm converges fast and tolerates staleness.
Efficacy and Safety of CAR19/22 T-cell "Cocktail" Therapy in Patients with Refractory/ Relapsed B-Cell Malignancies.

Antigen escape relapse has emerged as a major challenge for long-term disease control post CD19-directed therapies, to which dual-targeting of CD19 and CD22 has been proposed as a potential solution. Between March 2016 and January 2018, we conducted a pilot study in 89 patients, who had refractory/relapsed B-cell malignancies, to evaluate the efficacy and safety of sequential infusion of anti-CD19 and anti-CD22, two single-specific, third-generation chimeric antigen receptor-engineered (CAR19/22) T-cell "cocktail". Among the 51 patients with acute lymphoblastic leukemia, the minimal residual disease-negative response rate was 96.0% (95% confidence interval (CI), 86.3 to 99.5). With a median follow-up of 16.7 months (range, 1.3 to 33.3), the median progression-free survival (PFS) was 13.6 months (95% CI, 6.5 to not reached, NR), and the median overall survival (OS) was 31.0 months (95% CI, 10.6 to NR). Among the 38 patients with non-Hodgkin lymphoma, the overall response rate was 72.2% (95% CI, 54.8 to 85.8), with a complete response rate of 50.0% (95% CI, 32.9 to 67.1). With a median follow-up of 14.4 months (range, 0.4 to 27.4), the median PFS was 9.9 months (95% CI, 3.3 to NR), and the median OS was 18.0 months (95% CI, 6.1 to NR). Antigen loss relapse occurred in one patient during follow-up. High-grade cytokine release syndrome and neurotoxicity occurred in 22.4% and 1.12% patients, respectively. All except one were reversible. Our results indicated that sequential infusion of CAR19/22 T-cell was safe, efficacious, and may have reduced the rate of antigen escape relapse in B-cell malignancies. ChiCTR, number ChiCTR-OPN-16008526.
Pluralistic Image Completion

Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion – the task of generating multiple and diverse plausible solutions for image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higherquality completion results, but also with multiple and diverse plausible outputs.
Hookwire-guided Sentinel Lymph Node Biopsy Using Contrast-enhanced Ultrasonography Followed by a One-step Nucleic Acid Amplification (OSNA) Assay for Breast Cancer

Aim: To investigate the feasibility of hookwire-guided sentinel lymph node biopsy (SLNB) using contrast-enhanced ultrasonography (CEUS) followed by a one-step nucleic acid amplification (OSNA) assay. Patients and Methods: Clinical T1-2N0M0 breast cancer patients scheduled to undergo SLNB participated in this study. Both Sonazoid® and dye were used as tracers, and the most upstream sentinel lymph node (SLN) at each lymphatic flow detected by CEUS (First-SLN) was sampled under hookwire guidance, a procedure called “Sona-Hook”. Results: In each of the 50 cases, at least one First-SLN was extracted by “Sona-Hook”. All contrast-enhanced SLNs (CE-SLNs) were dye-positive, and the mean number of CE-SLNs sampled per patient was lower than that of dye-positive SLNs (1.48 vs. 1.88, p<0.01). Through OSNA, qualitative assessment of tumor metastasis between First-SLNs and all SLNs completely matched together. Conclusion: “Sona-Hook” for First-SLN followed by an OSNA assay may be a feasible minimally invasive SLNB strategy.
The Past and the Present of the Color Checker Dataset Misuse

The pipelines of digital cameras contain a part for computational color constancy, which aims to remove the influence of the illumination on the scene colors. One of the best known and most widely used benchmark datasets for this problem is the Color Checker dataset. However, due to the improper handling of the black level in its images, this dataset has been widely misused and while some recent publications tried to alleviate the problem, they nevertheless erred and created additional wrong data. This paper gives a history of the Color Checker dataset usage, it describes the origins and reasons for its misuses, and it explains the old and new mistakes introduced in the most recent publications that tried to handle the issue. This should, hopefully, help to prevent similar future misuses.
Towards Weighted-Sampling Audio Adversarial Example Attack.

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.
Large Scale Product Categorization using Structured and Unstructured Attributes

Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with treating product content as a single pile of text, or only consider a few product attributes for modelling purposes. Given the variety of products sold on popular eCommerce platforms, it is hard to consider all available product attributes as part of the modeling exercise, considering that products possess their own unique set of attributes based on category. In this paper, we compare hierarchical models to flat models and show that in specific cases, flat models perform better. We explore two Deep Learning based models that extract features from individual pieces of unstructured data from each product and then combine them to create a product signature. We also propose a novel idea of using structured attributes and their values together in an unstructured fashion along with convolutional filters such that the ordering of the attributes and the differing attributes by product categories no longer becomes a modelling challenge. This approach is also more robust to the presence of faulty product attribute names and values and can elegantly generalize to use both closed list and open list attributes.