Miracle-dz/UniFAR-FTUs huggingface dataset:
Tic Phenomenology and Tic Awareness in Adults With Autism

Background: Tics are common in people with autism spectrum disorder (ASD). However, their phenomenology and characteristics have not been studied in detail. Methods: Based on video sequences of 21 adults with ASD without intellectual disability and 16 adults with Gilles de la Tourette syndrome (GTS), tic severity, tic repertoires, and tic awareness were determined. Results: Ten ASD and all GTS participants had tics during video recordings. The ASD group had significantly fewer tics, compared to GTS. Tic distribution and tic repertoires were comparable, but more restricted in ASD. All GTS participants, but only 5 of the 10 ASD participants, were aware of their tics. Conclusions: Tics are common in adults with ASD. They are indistinguishable from tics in GTS and are similarly distributed, but less severe. Tic awareness is limited in ASD.
Folate/Vitamin B12 Supplementation Combats Oxidative Stress-Associated Carcinogenesis in a Rat Model of Colon Cancer

Abstract Folate and vitamin B12 deficiency is associated with depletion of the major intracellular antioxidant glutathione, and oxidative stress is emerging as an etiological mechanism for colon cancer. Azoxymethane (AOM), a potent carcinogen, induces colon cancer in rats by causing pathophysiological changes and oxidative stress. We investigated the synergistic effect of folate and vitamin B12 supplementation against AOM-induced carcinogenesis and oxidative stress in rat colon. Adult male rats were distributed into four groups: 1) Basal diet only; 2) AOM injection (15 mg/kg once per week in weeks 5 and 6); 3) Folate and vitamin B12 supplemented diet; 4) Folate and B12 diet with AOM injection. After 16 weeks, rats were sacrificed, colon tissue dissected, indicators of oxidative stress were measured, and immunohistochemical and ultrastructural changes were evaluated. AOM-injected rats showed oxidative stress, evident by glutathione depletion, oxidation of cellular proteins, and DNA oxidative damage. AOM increased mucosal levels of antiapoptotic and proapoptotic proteins Bcl2 and Bax and caused ultrastructure changes in colonic cell organelles. Folate and vitamin B12 supplementation decreased the level of oxidative stress and ameliorated the cytotoxic effects of AOM. In this in vivo experimental model of colon cancer, folate and vitamin B12 supplementation combats carcinogen-induced oxidative stress.
A Model for Auto-Programming for General Purposes

The Universal Turing Machine (TM) is a model for VonNeumann computers --- general-purpose computers. A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a machine can accomplish the same. This theoretical work shows how the Developmental Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it.
ACIQ: Analytical Clipping for Integer Quantization of neural networks

Unlike traditional approaches that focus on the quantization at the network level, in this work we propose to minimize the quantization effect at the tensor level. We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40\% accuracy improvement is obtained for the quantization of VGG16-BN to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time. One immediate application is for a rapid deployment of neural networks to low-precision accelerators without time-consuming fine tuning or the availability of the full datasets.
The Think/No‐Think Alcohol Task: A New Paradigm for Assessing Memory Suppression in Alcohol‐Related Contexts

BACKGROUND Research with the Think/No-Think (TNT) task has shown that voluntary suppression of an unwanted memory may lead to its later forgetting. To date, however, no study has assessed the memory suppression abilities in alcohol-related contexts despite the potential implications that it might have for alcohol research. With this aim, we developed a new version of the TNT paradigm, the TNT Alcohol (TNTA) task, which consists of 36 neutral pictures paired with 36 alcohol/no-alcohol images that are instructed to be suppressed or recollected. METHODS Electroencephalographic activity was recorded from 64 electrodes while 20 young healthy females performed the TNTA task. The event-related potentials (ERPs) typically involved in memory suppression/recollection were analyzed, namely the fronto-central N2, the late parietal positivity (LPP), and the frontal slow wave (FSW). RESULTS Findings revealed reduced recall for previously learned images that were subsequently instructed to be suppressed (No-Think) relative to those instructed to be retrieved (Think) and those not cued to be suppressed or retrieved (Baseline). This reduction seemed to be more prominent for alcohol-related memories. In addition, ERP analysis showed that compared to attempts of recollection, attempts of memory suppression were associated with attenuated LPP amplitude-more pronounced for alcohol-related memories-(indicating reduced conscious recollection for No-Think images) as well as with increased FSW (suggesting strategic control aiming at decrease accessibility of unwanted memories). CONCLUSIONS These results replicate and extend previously reported behavioral and ERP findings in the TNT paradigm and suggest that the TNTA task may be a useful instrument to measure the ability to suppress alcohol-related memories.
A Practical Intent Fuzzing Tool for Robustness of Inter-Component Communication in Android Apps

This research aims at a new practical Intent fuzzing tool for detecting Intent vulnerabilities of Android apps causing the robustness problem. We proposed two new ideas. First, we designed an Intent specification language to describe the structure of Intent, which makes our Intent fuzz testing tool flexible. Second, we proposed an automatic tally method classifying unique failures. With the two ideas, we implemented an Intent fuzz testing tool called Hwacha, and evaluated it with 50 commercial Android apps. Our tool offers an arbitrary combination of automatic and manual Intent generators with executors such as ADB and JUnit due to the use of the Intent specification language. The automatic tally method excluded almost 80% of duplicate failures in our experiment, reducing efforts of testers very much in review of failures. The tool uncovered more than 400 unique failures including what is unknown so far. We also measured execution time for Intent fuzz testing, which has been rarely reported before. Our tool is practical because the whole procedure of fuzz testing is fully automatic and the tool is applicable to the large number of Android apps with no human intervention.
Natural language processing (NLP) applied on issue trackers

In the domain of software engineering NLP techniques are needed to use and find duplicate or similar development knowledge which are stored in development documentation as development tasks. To understand duplicate and similar development documentations we will discuss different NLP techniques as descriptive statistics, topic analysis and similarity algorithms as N-grams, the Jaccard or LSI algorithm as well as machine learning algorithms as Decision trees or support vector machines (SVM). Those techniques are used to reach a better understanding of the characteristics, the lexical relations (syntactical and semantical) and the classification and prediction of duplicate development tasks. We found that duplicate tasks share conceptual information and are rather created by inexperienced developers. By tuning different features to predict development tasks with a gradient or a Fidelity loss function a system can identify a duplicate tasks with a 100% accuracy.
Weak-supervision for Deep Representation Learning under Class Imbalance

Class imbalance is a pervasive issue among classification models including deep learning, whose capacity to extract task-specific features is affected in imbalanced settings. However, the challenges of handling imbalance among a large number of classes, commonly addressed by deep learning, have not received a significant amount of attention in previous studies. In this paper, we propose an extension of the deep over-sampling framework, to exploit automatically-generated abstract-labels, i.e., a type of side-information used in weak-label learning, to enhance deep representation learning against class imbalance. We attempt to exploit the labels to guide the deep representation of instances towards different subspaces, to induce a soft-separation of inherent subtasks of the classification problem. Our empirical study shows that the proposed framework achieves a substantial improvement on image classification benchmarks with imbalanced among large and small numbers of classes.
Marrying Universal Dependencies and Universal Morphology

The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages—UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project’s annotations could be used to validate the other’s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.
Relative Importance Sampling For Off-Policy Actor-Critic in Deep Reinforcement Learning

Off-policy learning is more unstable compared to on-policy learning in reinforcement learning (RL). One reason for the instability of off-policy learning is a discrepancy between the target ($\pi$) and behavior (b) policy distributions. The discrepancy between $\pi$ and b distributions can be alleviated by employing a smooth variant of the importance sampling (IS), such as the relative importance sampling (RIS). RIS has parameter $\beta\in[0, 1]$ which controls smoothness. To cope with instability, we present the first relative importance sampling-off-policy actor-critic (RIS-Off-PAC) model-free algorithms in RL. In our method, the network yields a target policy (the actor), a value function (the critic) assessing the current policy ($\pi$) using samples drawn from behavior policy. We use action value generated from the behavior policy in reward function to train our algorithm rather than from the target policy. We also use deep neural networks to train both actor and critic. We evaluated our algorithm on a number of Open AI Gym benchmark problems and demonstrate better or comparable performance to several state-of-the-art RL baselines.
Engineering electrospun multicomponent polyurethane scaffolding platform comprising grapeseed oil and honey/propolis for bone tissue regeneration

Essential oils play an important role in reducing the pain and inflammation caused by bone fracture.In this study, a scaffold was electrospun based on polyurethane (PU), grape seed oil, honey and propolis for bone tissue-engineering applications. The fiber diameter of the electrospun PU/grape seed oil scaffold and PU/grape seed oil/honey/propolis scaffold were observed to be reduced compared to the pristine PU control. FTIR analysis revealed the existence of grape seed oil, honey and propolis in PU identified by CH band peak shift and also hydrogen bond formation. The contact angle of PU/grape seed oil scaffold was found to increase owing to hydrophobic nature and the contact angle for the PU/grape seed/honey oil/propolis scaffold were decreased because of hydrophilic nature. Further, the prepared PU/grape seed oil and PU/grape seed oil/honey/propolis scaffold showed enhanced thermal stability and reduction in surface roughness than the control as revealed in thermogravimetric analysis (TGA) and atomic force microscopy (AFM) analysis. Further, the developed nanocomposite scaffold displayed delayed blood clotting time than the pristine PU in the activated prothrombin time (APTT) and partial thromboplastin time (PT) assay. The hemolytic assay and cytocompatibility studies revealed that the electrospun PU/grape seed oil and PU/grape seed oil/honey/propolis scaffold possess non-toxic behaviour to red blood cells (RBC) and human fibroblast cells (HDF) cells indicating better blood compatibility and cell viability rates. Hence, the newly developed electrospun nanofibrous composite scaffold with desirable characteristics might be used as an alternative candidate for bone tissue engineering applications.
Temporal trends in biopsy proven glomerular disease in Uruguay, 1990-2014

Our aim is to describe variations in the incidence rates of glomerular disease diagnosed by renal biopsies performed in Uruguay over the last 25 years in relation to sex, age, clinical presentation and histological diagnosis. We analyzed all renal biopsies performed in Uruguay during the 25 years period and estimated incidence rates per million people per year (pmp/yr) for the population older than 14 years. Mann Kendall's trend analysis was used to assess incidence trends. In order to identify changes in trends, we compared annual incidence rates with the Joinpoint method. From 1990 to 2014, 3390 biopsies of native kidneys corresponding to glomerular disease were performed in patients older than 14 years. The average biopsy rate was 58 per pmp/yr. The glomerular disease incidence rate increased progressively over the period (p<0.05). Trends analysis over five-year periods demonstrated a progressive increase of IgA nephropathy (3.08 pmp/yr 1990–1994 to 12.53 pmp/yr 2010–2014 p<0.05), membranous nephropathy (2.38 pmp/yr 1990–1994 to 8.04 pmp/yr 2010–2014 p< 0.05) and lupus nephritis (4,23 pmp/yr 1990–1994 to 7,81 pmp/yr 2010–2014 p<0.05). There was a change in the trend of focal segmental glomerular sclerosis (FSGS) which increased until 1996 and decreased afterwards. The incidence rates of glomerular disease have doubled globally in the last quarter of a century in Uruguay, mainly related to the increase of IgA nephropathy, membranous nephropathy and lupus nephritis. There was a change in the slope of the incidence rate of FSGS.
Predicting image properties in penalized‐likelihood reconstructions of flat‐panel CBCT

PURPOSE Model-based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods exhibit data-dependent and shift-variant properties. Image quality predictors have been derived to prospectively estimate local noise and spatial resolution, facilitating both system hardware design and tuning of reconstruction methods. However, current MBIR image quality predictors rely on idealized system models, ignoring physical blurring effects and noise correlations found in real systems. In this work, we develop and validate a new set of predictors using a physical system model specific to flat-panel cone-beam CT (FP-CBCT). METHODS Physical models appropriate for integration with MBIR analysis are developed and parameterized to represent nonidealities in FP projection data including focal spot blur, scintillator blur, detector aperture effect, and noise correlations. Flat-panel-specific predictors for local spatial resolution and local noise properties in PL reconstructions are developed based on these realistic physical models. Estimation accuracy of conventional (idealized) and FP-specific predictors is investigated and validated against experimental CBCT measurements using specialized phantoms. RESULTS Validation studies show that flat-panel-specific predictors can accurately estimate the local spatial resolution and noise properties, while conventional predictors show significant deviations in the magnitude and scale of the spatial resolution and local noise. The proposed predictors show accurate estimations over a range of imaging conditions including varying x-ray technique and regularization strength. The conventional spatial resolution prediction is sharper than ground truth. Using conventional spatial resolution predictor, the full width at half maximum (FWHM) of local point spread function (PSF) is underestimated by 0.2 mm. This mismatch is mostly eliminated in FP-specific prediction. The general shape and amplitude of local noise power spectrum (NPS) FP-specific predictions are consistent with measurement, while the conventional predictions underestimated the noise level by 70%. CONCLUSION The proposed image quality predictors permit accurate estimation of local spatial resolution and noise properties for PL reconstruction, accounting for dependencies on the system geometry, x-ray technique, and patient-specific anatomy in real FP-CBCT. Such tools enable prospective analysis of image quality for a range of goals including novel system and acquisition design, adaptive and task-driven imaging, and tuning of MBIR for robust and reliable behavior.
RTPDB: a database providing associations between genetic variation or expression and cancer prognosis with radiotherapy-based treatment

Abstract In recent years, lots of studies have reported the relationship between genetic variation or expression and cancer prognosis with radiotherapy-based treatment. However, due to limitation in available journals or literature database, inconsistent nomenclature system of genetic variation and cancer and time-consuming investigation on literature searching and reading, considerable researches could hardly get found and cited. In this study, we constructed the Radiotherapy Prognosis Database (RTPDB), which contains a comprehensive resource about genes and related cancer prognosis. It included 775 studies, which consist of 275 Single Nucleotide Polymorphism (SNP) studies with 59 765 patients, 261 genes, 708 SNPs, 16 tumors and 16 treatment types, and 500 expression studies with 55 751 patients, 264 genes, 27 tumors and 15 treatment types. The names of genes and their variants were converted and displayed in the form of the official symbol. The detailed information of the tumor, treatment and prognosis were classified. We hope RTPDB will be a useful resource with great potential for researches on genes, variants and cancer prognosis.
Children sustaining a severe acquired brain lesion before age 3 years: a follow-up study at 1 year from insult

ABSTRACT Purpose: To describe the functional and cognitive outcome of acquired brain injury of different aetiologies in children before age 3 years, at initial hospitalization and at a 1-year follow-up, after a rehabilitation programme. Method: Data were collected at 6 months and at 12 months from the event; cognitive data were collected as soon as possible at T1. The full sample was divided into three groups according to aetiology. Results: At T0, 74 patients showed so severe a cognitive impairment that they could not be evaluated, others presented with motor, linguistic and cognitive deficits. At T1, the proportion of non-evaluable patients decreased to 58 children. Patients with anoxic lesions showed the most unfavourable motor and visual outcome; patients with infectious lesions showed most frequently a motor global delay. Conclusions: At 1 year from insult about 50% of patients could undergo a cognitive evaluation. Improvement differed according to aetiology.
Always be Two Steps Ahead of Your Enemy

We investigate the maintenance of overlay networks under massive churn where an adversary may churn a constant fraction an of nodes over the course of O(log(n)) rounds. In particular, the adversary has an almost up-to-date information of the network topology as it can observe an only slightly outdated topology that is at least 2 rounds old. Other than that, we only have the provably minimal restriction that new nodes can only join the network via nodes that have taken part in the network for at least one round. Our contributions are as follows: First, we show that it is impossible to maintain a connected topology if the adversary has up-to-date information about the nodes' connections. As our main result, we present an algorithm that constructs a new overlay — completely independent of all previous overlays — every 2 rounds. Furthermore, each node sends and receives only O(log^3(n)) messages in each round. As part of our solution, we propose the Linearized DeBruijn Swarm (LDS), a highly churn resistant overlay, which will be maintained by the algorithm. However, our approaches can be transferred to a variety of classical P2P topologies where nodes are mapped into the [0, 1)-interval.
Rotational 3D Texture Classification Using Group Equivariant CNNs

Convolutional Neural Networks (CNNs) traditionally encode translation equivariance via the convolution operation. Generalization to other transformations has recently received attraction to encode the knowledge of the data geometry in group convolution operations. Equivariance to rotation is particularly important for 3D image analysis due to the large diversity of possible pattern orientations. 3D texture is a particularly important cue for the analysis of medical images such as CT and MRI scans as it describes different types of tissues and lesions. In this paper, we evaluate the use of 3D group equivariant CNNs accounting for the simplified group of right-angle rotations to classify 3D synthetic textures from a publicly available dataset. The results validate the importance of rotation equivariance in a controlled setup and yet motivate the use of a finer coverage of orientations in order to obtain equivariance to realistic rotations present in 3D textures.
Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine Translation

Recent work achieved remarkable results in training neural machine translation (NMT) systems in a fully unsupervised way, with new and dedicated architectures that rely on monolingual corpora only. In this work, we propose to define unsupervised NMT (UNMT) as NMT trained with the supervision of synthetic bilingual data. Our approach straightforwardly enables the use of state-of-the-art architectures proposed for supervised NMT by replacing human-made bilingual data with synthetic bilingual data for training. We propose to initialize the training of UNMT with synthetic bilingual data generated by unsupervised statistical machine translation (USMT). The UNMT system is then incrementally improved using back-translation. Our preliminary experiments show that our approach achieves a new state-of-the-art for unsupervised machine translation on the WMT16 German--English news translation task, for both translation directions.