Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Exact Matrix Completion with Finite Samples

Matrix completion is the problem of recovering a low rank matrix by observing a small fraction of its entries. A series of recent works [KOM12,JNS13,HW14] have proposed fast non-convex optimization based iterative algorithms to solve this problem. However, the sample complexity in all these results is sub-optimal in its dependence on the rank, condition number and the desired accuracy. In this paper, we present a fast iterative algorithm that solves the matrix completion problem by observing $O(nr^5 \log^3 n)$ entries, which is independent of the condition number and the desired accuracy. The run time of our algorithm is $O(nr^7\log^3 n\log 1/\epsilon)$ which is near linear in the dimension of the matrix. To the best of our knowledge, this is the first near linear time algorithm for exact matrix completion with finite sample complexity (i.e. independent of $\epsilon$). Our algorithm is based on a well known projected gradient descent method, where the projection is onto the (non-convex) set of low rank matrices. There are two key ideas in our result: 1) our argument is based on a $\ell_{\infty}$ norm potential function (as opposed to the spectral norm) and provides a novel way to obtain perturbation bounds for it. 2) we prove and use a natural extension of the Davis-Kahan theorem to obtain perturbation bounds on the best low rank approximation of matrices with good eigen-gap. Both of these ideas may be of independent interest.
Light, heat, action: neural control of fruit fly behaviour

The fruit fly Drosophila melanogaster has emerged as a popular model to investigate fundamental principles of neural circuit operation. The sophisticated genetics and small brain permit a cellular resolution understanding of innate and learned behavioural processes. Relatively recent genetic and technical advances provide the means to specifically and reproducibly manipulate the function of many fly neurons with temporal resolution. The same cellular precision can also be exploited to express genetically encoded reporters of neural activity and cell-signalling pathways. Combining these approaches in living behaving animals has great potential to generate a holistic view of behavioural control that transcends the usual molecular, cellular and systems boundaries. In this review, we discuss these approaches with particular emphasis on the pioneering studies and those involving learning and memory.
Debt and Growth: Is There a Magic Threshold?

Using a novel empirical approach and an extensive dataset developed by the Fiscal Affairs Department of the IMF, we find no evidence of any particular debt threshold above which medium-term growth prospects are dramatically compromised. Furthermore, we find the debt trajectory can be as important as the debt level in understanding future growth prospects, since countries with high but declining debt appear to grow equally as fast as countries with lower debt. Notwithstanding this, we find some evidence that higher debt is associated with a higher degree of output volatility.
Recognition of Pen-Based Music Notation: The HOMUS Dataset

A profitable way of digitizing a new musical composition is by using a pen-based (online) system, in which the score is created with the sole effort of the composition itself. However, the development of such systems is still largely unexplored. Some studies have been carried out but the use of particular little datasets has led to avoid objective comparisons between different approaches. To solve this situation, this work presents the Handwritten Online Musical Symbols (HOMUS) dataset, which consists of 15200 samples of 32 types of musical symbols from 100 different musicians. Several alternatives of recognition for the two modalities -online, using the strokes drawn by the pen, and offline, using the image generated after drawing the symbol- are also presented. Some experiments are included aimed to draw main conclusions about the recognition of these data. It is expected that this work can establish a binding point in the field of recognition of online handwritten music notation and serve as a baseline for future developments.
Application of Total-Variation-Based Curvelet Shrinkage for Three-Dimensional Seismic Data Denoising

Transform-based denoising methods are popularly used in image and signal processing, including seismic data processing. However, they often suffer from unwanted artifacts, e.g., nonsmooth edges and pesudo-Gibbs phenomena. A total variation (TV) minimization technique has the ability to suppress these artifacts, particularly in the vicinity of discontinuities. In this letter, we employ the almost optimal sparse transform for seismic data, i.e., curvelet transform, to represent and denoise seismic cubes, combining a projected TV technique as a postprocessing method, in order to reduce unwanted nonsmooth artifacts caused by the curvelet transform. We shrink seismic noise via retaining the significant curvelet coefficients, but for the small ones under a given threshold, we modify them by searching for the minimization of their TV values, instead of setting them to zeros, i.e., TV-combined curvelets with adjustment of small curvelet coefficients by TV minimization. We prove its validity in seismic denoising by comparing with existing methods, including curvelets, TV denoising, and TV-combined curvelets with adjustment of large curvelet coefficients by TV minimization. Numerical experiments show that seismic noise is effectively suppressed by the present technique and that nonsmooth artifacts caused by the curvelet transform are also reduced significantly.
Financial incentives and the "performance of crowds"

The relationship between financial incentives and performance, long of interest to social scientists, has gained new relevance with the advent of web-based "crowd-sourcing" models of production. Here we investigate the effect of compensation on performance in the context of two experiments, conducted on Amazon's Mechanical Turk (AMT). We find that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an "anchoring" effect: workers who were paid more also perceived the value of their work to be greater, and thus were no more motivated than workers paid less. In contrast with compensation levels, we find the details of the compensation scheme do matter---specifically, a "quota" system results in better work for less pay than an equivalent "piece rate" system. Although counterintuitive, these findings are consistent with previous laboratory studies, and may have real-world analogs as well.
TCF1 and LEF1 act as T-cell intrinsic HTLV-1 antagonists by targeting Tax

Significance HTLV-1 is a peripheral T-cell tropic virus and induces proliferation of CD4+ T cells, resulting in T-cell malignancy and inflammatory diseases. Recent studies demonstrated that several restriction factors inhibiting HIV are also inhibitory to HTLV-1. We identified two T-cell–specific proteins, TCF1 and LEF1, as HTLV-1 restriction factors that determine the peripheral T-cell tropism of this virus by targeting Tax. They are highly expressed in immature thymocytes and thereby become a natural intrinsic barrier for HTLV-1 replication in the thymus. However, their expression can be down-regulated by Tax, as well as by activation and differentiation of T cells. These findings provide a mechanistic understanding of how HTLV-1 induces T-cell malignancies in the periphery but never in the thymus. Human T-cell leukemia virus type 1 (HTLV-1) is a delta-type retrovirus that induces malignant and inflammatory diseases during its long persistence in vivo. HTLV-1 can infect various kinds of cells; however, HTLV-1 provirus is predominantly found in peripheral CD4 T cells in vivo. Here we find that TCF1 and LEF1, two Wnt transcription factors that are specifically expressed in T cells, inhibit viral replication through antagonizing Tax functions. TCF1 and LEF1 can each interact with Tax and inhibit Tax-dependent viral expression and activation of NF-κB and AP-1. As a result, HTLV-1 replication is suppressed in the presence of either TCF1 or LEF1. On the other hand, T-cell activation suppresses the expression of both TCF1 and LEF1, and this suppression enables Tax to function as an activator. We analyzed the thymus of a simian T-cell leukemia virus type 1 (STLV-1) infected Japanese macaque, and found a negative correlation between proviral load and TCF1/LEF1 expression in various T-cell subsets, supporting the idea that TCF1 and LEF1 negatively regulate HTLV-1 replication and the proliferation of infected cells. Thus, this study identified TCF1 and LEF1 as Tax antagonistic factors in vivo, a fact which may critically influence the peripheral T-cell tropism of this virus.
Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well-known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, 3-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models.
Large Uncertainty in the Relative Rates of Dynamical and Hydrological Tropical Expansion

Climate models predict that the Hadley circulation will expand poleward in a warmer climate, a trend which may cause significant changes in global precipitation patterns. However, recent studies have disagreed as to how strongly changes in the Hadley circulation and changes in the hydrological cycle (specifically the latitude at which precipitation balances evaporation) are related. Here we analyze dynamical and hydrological measures of the Southern Hemisphere edge of the tropics using simulations from the fifth Coupled Model Intercomparison Project (CMIP5) and four reanalysis data sets. In simulations with an abrupt quadrupling of atmospheric CO2 concentrations, all models show a poleward expansion in both metrics. However, there is a large spread among models; the ratio of the hydrological to dynamical expansions varies from 0.6 to 1.4. We show that this model spread can be largely explained by differences in internal variability, which in turn is related to the mean state of models. Differences in mean states among reanalyses are similar to those of models, and so reanalyses do not help constrain uncertainty in model trends.
Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.

BACKGROUND Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly. METHODS AND OBJECTIVES Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation. RESULTS A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre- versus post-training neuropsychological tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result. CONCLUSION The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.
The Usefulness of Standardized Uptake Value in Differentiation between Benign and Malignant Thyroid Lesions Detected Incidentally in 18F-FDG PET/CT Examination

Introduction In the last decade, (18)F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET and PET/CT) has become one of the major diagnostic tools used in oncology. A significant number of patients who undergo this procedure, due to non-thyroidal reasons, present incidental uptake of (18F-FDG) in the thyroid. The aim of the study was to compare the SUVmax (standardized uptake value) of thyroid focal lesions, which were incidentally found on PET/CT, in relation to the results of thyroid fine-needle aspiration biopsy (FNAB) and/or histopathological evaluation. Materials and Methods Patients referred for PET/CT examination, due to non-thyroidal illness, presented focal 18F-FDG uptake in the thyroid and were advised to undergo ultrasonography (US), hormonal evaluation, FNAB and/or total thyroidectomy at our institution. Results 6614 PET/CT examinations performed in 5520 patients were analyzed. Of the 122 patients with focal thyroid 18F-FDG activity, 82 patients (67.2%) underwent further thyroid evaluation using FNAB. Benign lesions were diagnosed in 46 patients, malignant - in 19 patients (confirmed by post-surgical histopathology), while 17 patients had inconclusive results of cytological assessment. Mean SUVmax of benign lesions was 3.2±2.8 (median = 2.4), while the mean SUVmax value for malignant lesions was 7.1±8.2 (median = 3.5). The risk of malignancy was 16.7% for lesions with a SUVmax under 3, 43.8% for lesions with a SUVmax between 3 and 6, and 54.6% for lesions with a SUVmax over 6. In the group of malignant lesions, a positive correlation between the lesion’s diameter and SUVmax was observed (p = 0.03, r = 0.57). Conclusions Subjects with incidental focal uptake of 18F-FDG in thyroid are at a high risk of thyroid malignancy. A high value of SUVmax further increases the risk of malignancy, indicating the necessity for further cytological or histological evaluation. However, as SUVmax correlated with the diameter of malignant lesions, small lesions with focal uptake of 18F-FDG should be interpreted cautiously.
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this “kernel-ABC” method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.
An efficient heuristic for state encoding minimizing the BDD representations of the transition relations of finite state machines

An efficient representation of the (smoothed) transition relation of a synchronous finite state machine (FSM) speeds up the traversal based symbolic verification and the functional simulation of the FSM. When using reduced ordered binary decision diagrams (BDDs), dynamic reordering algorithms are applied in order to keep the sizes of the BDDs tractable. However when FSMs are represented by BDDs, the state encoding can be used as an additional optimization criterion. In this paper, we present a new algorithm for state encoding of FSMs that minimizes the BDD representations of the corresponding (smoothed) transition relations. Experimental results show the approach to be very efficient.
Novel Genetic Regulation of  T Helper 1 (Th1)/Th2 Cytokine Production and Encephalitogenicity in Inbred Mouse Strains

Development of T helper cell (Th)1 or Th2 cytokine responses is essential for effector and regulatory functions of T helper cells. We have compared cytokine profiles of myelin basic protein (MBP) Ac1-16 peptide-specific T helper cells from inbred mouse strains expressing identical k haplotype-derived MHC class II molecules B10.A and B10.BR. B10.BR T cell lines (TCL) produced Th1 cytokines (including high levels of TNF-α) and induced experimental autoimmune encephalomyelitis after adoptive transfer. In contrast, B10.A TCL produced Th2 cytokines (including low levels of TNF-α) and were poorly encephalitogenic. The contributions of the genetic origin of the T cells and the APC were explored. Serial restimulations of the B10.BR TCL with B10.A or (B10.A × B10.BR) F1 splenic antigen presenting cells (APC) during the establishment of TCL markedly reduced both Th1 cytokine production and encephalitogenicity. In addition, a single restimulation with B10.A splenic APC reduced IFN-γ and TNF-α production by established Th1 MBP-specific Ak-restricted B10.BR TCL and by a Th1 KLH-specific, Ek-restricted B10.BR T cell clone. These studies suggest that B10.A and B10.BR APC differ in their ability to stimulate IFN-γ and TNF-α production by mature Th1 cells and also influence their Th1/Th2 commitment in vivo. The nature of the downregulatory activity of B10.A APC on IFN-γ and TNF-α production was explored. 2-hour supernatants from antigen-activated B10.A APC/TCL cultures or from B10.A APC activated by LPS had the same inhibitory effects on IFN-γ and TNF-α production by B10.BR TCL. The downregulatory effects of B10.A APC are independent of TNF-α, IL-4, IL-10, IL-12p40, IFN-γ, IL-13, TGF-β, and PGE2. Thus, genetic difference(s) between B10.A and B10.BR APC appear(s) to control the production or activity of a novel soluble cytokine regulatory factor that influences Th1/Th2 commitment and controls production of IFN-γ and TNF-α by mature Th1 cells.
Robust Antigen Specific Th17 T Cell Response to Group A Streptococcus Is Dependent on IL-6 and Intranasal Route of Infection

Group A streptococcus (GAS, Streptococcus pyogenes) is the cause of a variety of clinical conditions, ranging from pharyngitis to autoimmune disease. Peptide-major histocompatibility complex class II (pMHCII) tetramers have recently emerged as a highly sensitive means to quantify pMHCII-specific CD4+ helper T cells and evaluate their contribution to both protective immunity and autoimmune complications induced by specific bacterial pathogens. In lieu of identifying an immunodominant peptide expressed by GAS, a surrogate peptide (2W) was fused to the highly expressed M1 protein on the surface of GAS to allow in-depth analysis of the CD4+ helper T cell response in C57BL/6 mice that express the I-Ab MHCII molecule. Following intranasal inoculation with GAS-2W, antigen-experienced 2W:I-Ab-specific CD4+ T cells were identified in the nasal-associated lymphoid tissue (NALT) that produced IL-17A or IL-17A and IFN-γ if infection was recurrent. The dominant Th17 response was also dependent on the intranasal route of inoculation; intravenous or subcutaneous inoculations produced primarily IFN-γ+ 2W:I-Ab+ CD4+ T cells. The acquisition of IL-17A production by 2W:I-Ab-specific T cells and the capacity of mice to survive infection depended on the innate cytokine IL-6. IL-6-deficient mice that survived infection became long-term carriers despite the presence of abundant IFN-γ-producing 2W:I-Ab-specific CD4+ T cells. Our results suggest that an imbalance between IL-17- and IFN-γ-producing CD4+ T cells could contribute to GAS carriage in humans.
Inhibition of rho kinase enhances survival of dopaminergic neurons and attenuates axonal loss in a mouse model of Parkinson’s disease

Axonal degeneration is one of the earliest features of Parkinson’s disease pathology, which is followed by neuronal death in the substantia nigra and other parts of the brain. Inhibition of axonal degeneration combined with cellular neuroprotection therefore seem key to targeting an early stage in Parkinson’s disease progression. Based on our previous studies in traumatic and neurodegenerative disease models, we have identified rho kinase as a molecular target that can be manipulated to disinhibit axonal regeneration and improve survival of lesioned central nervous system neurons. In this study, we examined the neuroprotective potential of pharmacological rho kinase inhibition mediated by fasudil in the in vitro 1-methyl-4-phenylpyridinium cell culture model and in the subchronic in vivo 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine mouse model of Parkinson’s disease. Application of fasudil resulted in a significant attenuation of dopaminergic cell loss in both paradigms. Furthermore, dopaminergic terminals were preserved as demonstrated by analysis of neurite network in vitro, striatal fibre density and by neurochemical analysis of the levels of dopamine and its metabolites in the striatum. Behavioural tests demonstrated a clear improvement in motor performance after fasudil treatment. The Akt survival pathway was identified as an important molecular mediator for neuroprotective effects of rho kinase inhibition in our paradigm. We conclude that inhibition of rho kinase using the clinically approved small molecule inhibitor fasudil may be a promising new therapeutic strategy for Parkinson’s disease.