Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerically stable parallel computation of (co-)variance

With the advent of big data, we see an increasing interest in computing correlations in huge data sets with both many instances and many variables. Essential descriptive statistics such as the variance, standard deviation, covariance, and correlation can suffer from a numerical instability known as "catastrophic cancellation" that can lead to problems when naively computing these statistics with a popular textbook equation. While this instability has been discussed in the literature already 50 years ago, we found that even today, some high-profile tools still employ the instable version. In this paper, we study a popular incremental technique originally proposed by Welford, which we extend to weighted covariance and correlation. We also discuss strategies for further improving numerical precision, how to compute such statistics online on a data stream, with exponential aging, with missing data, and a batch parallelization for both high performance and numerical precision. We demonstrate when the numerical instability arises, and the performance of different approaches under these conditions. We showcase applications from the classic computation of variance as well as advanced applications such as stock market analysis with exponentially weighted moving models and Gaussian mixture modeling for cluster analysis that all benefit from this approach.
A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning

Computer-aided diagnosis (CAD) techniques for lung field segmentation from chest radiographs (CXR) have been proposed for adult cohorts, but rarely for pediatric subjects. Statistical shape models (SSMs), the workhorse of most state-of-the-art CXR-based lung field segmentation methods, do not efficiently accommodate shape variation of the lung field during the pediatric developmental stages. The main contributions of our work are: 1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; 2) a deep representation learning detection mechanism, ensemble space learning, for robust object localization; and 3) marginal shape deep learning for the shape deformation parameter estimation. Unlike the iterative approach of conventional SSMs, the proposed shape learning mechanism transforms the parameter space into marginal subspaces that are solvable efficiently using the recursive representation learning mechanism. Furthermore, our method is the first to include the challenging retro-cardiac region in the CXR-based lung segmentation for accurate lung capacity estimation. The framework is evaluated on 668 CXRs of patients between 3 month to 89 year of age. We obtain a mean Dice similarity coefficient of $0.96 \pm 0.03$ (including the retro-cardiac region). For a given accuracy, the proposed approach is also found to be faster than conventional SSM-based iterative segmentation methods. The computational simplicity of the proposed generic framework could be similarly applied to the fast segmentation of other deformable objects.
Self-Damage Caused by Dysregulation of the Complement Alternative Pathway: Relevance of the Factor H Protein Family

The alternative pathway is a continuously active surveillance arm of the complement system, and it can also enhance complement activation initiated by the classical and the lectin pathways. Various membrane-bound and plasma regulatory proteins control the activation of the potentially deleterious complement system. Among the regulators, the plasma glycoprotein factor H (FH) is the main inhibitor of the alternative pathway and its powerful amplification loop. FH belongs to a protein family that also includes FH-like protein 1 and five factor H-related (FHR-1 to FHR-5) proteins. Genetic variants and abnormal rearrangements involving the FH protein family have been linked to numerous systemic and organ-specific diseases, including age-related macular degeneration, and the renal pathologies atypical hemolytic uremic syndrome, C3 glomerulopathies, and IgA nephropathy. This review covers the known and recently emerged ligands and interactions of the human FH family proteins associated with disease and discuss the very recent experimental data that suggest FH-antagonistic and complement-activating functions for the FHR proteins.
Soft-TTL: Time-Varying Fractional Caching

Standard time-to-live (TTL) cache management prescribes the storage of entire files, or possibly fractions thereof, for a given amount of time after a request. As a generalization of this approach, this letter proposes the storage of a time-varying, diminishing, fraction of a requested file. Accordingly, the cache progressively evicts parts of the file over an interval of time following a request. The strategy, which is referred to as soft-TTL, is justified by the fact that traffic traces are often characterized by arrival processes that display a decreasing, but non-negligible, probability of observing a request as the time elapsed since the last request increases. An optimization-based analysis of soft-TTL is presented, demonstrating the important role played by the hazard function of the inter-arrival request process, which measures the likelihood of observing a request as a function of the time since the most recent request.
Towards Intelligent Interfaces for Mixed-Focus Collaboration

Large, high-resolution displays are highly suitable for creation of digital environments for co-located collaborative task solving. Yet, placing multiple users in a shared environment may increase the risk of interferences, thus causing mental discomfort and decreasing efficiency of the team. To mitigate interferences coordination strategies and techniques were introduced. However, in a mixed-focus collaboration scenarios users switch now and again between loosely and tightly collaboration, therefore different coordination techniques might be required depending on the current collaboration state of team members. For that, systems have to be able to recognize collaboration states as well as transitions between them to ensure a proper adjustment of the coordination strategy. Previous studies on group behavior during collaboration in front of large displays investigated solely collaborative coupling states, not transitions between them though. To address this gap, we conducted a study with 12 participant dyads in front of a tiled display and let them solve two tasks in two different conditions (focus and overview). We looked into group dynamics and categorized transitions by means of changes in proximity, verbal communication, visual attention, visual interface, and gestures. The findings can be valuable for user interface design and development of group behavior models.
Metadata-driven error detection

Scientific data often originates from multiple sources and human agents. The integration of data from different sources must also resolve data quality problems that might occur because of inconsistency or different quality assurance levels of the sources. To identify various data quality problems in a dataset, it is necessary to use several error detection methods. Existing error detection solutions are usually tailored towards one specific type of data errors, such as rule violations or outliers, requiring the application of multiple strategies. Using all possible error detection methods is also not satisfying, as some systems might perform poorly on a particular dataset by producing a large number of false positives and missing some results. However, it is not trivial to assess the effectiveness of each strategy upfront. We propose two new holistic approaches for effectively combining off-the-shelf error detection systems. Our approaches are learning-based and incorporate metadata extracted from the dataset at hand. We empirically show, using four real-world datasets, that our method of combining error-detecting strategies achieves an average F1 score 15% higher than multiple heuristics-based baselines.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images

We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset. Existing datasets are typically limited to a single hand. By exploiting the visual cues given by an RGBD sensor and a pair of colored gloves, we automatically generate dense annotations for two hand segmentation. This lowers the cost/complexity of creating high quality datasets, and makes it easy to expand the dataset in the future. We further show that existing datasets, even with data augmentation, are not sufficient to train a hand segmentation algorithm that can distinguish two hands. Source and datasets are publicly available at the project page
When the inhibitor tells more than the substrate: the cyanide-bound state of a carbon monoxide dehydrogenase

An integral approach including experimental and theoretical analysis has been carried out with the wild-type and engineered CODHIICh variant to assess the parameters that control the C <svg xmlns="http://www.w3. org/2000/svg" version="1.0" width="16.000000pt" height="16.000000pt" viewBox="0 0 16.000000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate(1.000000,15.000000) scale(0.005147,-0.005147)" fill="currentColor" stroke="none"><path d="M0 1760 l0 -80 1360 0 1360 0 0 80 0 80 -1360 0 -1360 0 0 -80z M0 1280 l0 -80 1360 0 1360 0 0 80 0 80 -1360 0 -1360 0 0 -80z M0 800 l0 -80 1360 0 1360 0 0 80 0 80 -1360 0 -1360 0 0 -80z"/></g></svg> N stretching frequency.
A Multicenter, Randomized, Controlled Trial of Electroacupuncture for Perimenopause Women with Mild-Moderate Depression

Objective Up to 62% of perimenopausal women have depression symptoms. However, there is no efficacy treatment. The aim of this study is to compare the clinical efficacy and safety of EA therapy and escitalopram on perimenopause women with mild-moderate depressive symptom. Method A multicenter, randomized, positive-controlled clinical trial was conducted at 6 hospitals in China. 242 perimenopause women with mild-moderate depressive symptom were recruited and randomly assigned to receive 36 sessions of EA treatment or escitalopram treatment. The primary outcome measure was the 17-item Hamilton Depression Rating Scale (HAMD-17). The secondary outcome measures include menopause-specific quality of life (MENQOL) and serum sexual hormones which include estrogen, follicle-stimulating hormone, and luteinizing hormone. Results 221 (91.3%) completed the study, including 116 in the EA group and 105 in the escitalopram group. The baseline levels of demographic and outcome measurements were similar in the two groups. In the intervention period, there was no difference between two groups. However, in the follow-up, both HAMD-17 and MENQOL were significantly decreased, and at week 24 the mean differences were −2.23 and −8.97, respectively. There were no significant differences in the change of serum sexual hormones between the two groups. No serious adverse events occurred. Conclusion EA treatment is effective and safe in relieving depression symptom and improving the quality of life in the perimenopausal depression. Further research is needed to understand long-term efficacy and explore the mechanism of this intervention. This study is registered with ClinicalTrials.gov NCT02423694.
Combined ATR and DNA-PK Inhibition Radiosensitizes Tumor Cells Independently of Their p53 Status

Head and neck squamous cell carcinoma (HNSCC) is a significant cause of cancer deaths. Cisplatin-based chemoradiotherapy is a standard of care for locally advanced disease. ATR and DNA-PK inhibition (DNA-PKi) are actively being investigated in clinical trials with preclinical data supporting clinical translation as radiosensitizers. Here, we hypothesized that targeting both ATR and DNA-PK with small molecule inhibitors would increase radiosensitization of HNSCC cell lines. Radiosensitization was assessed by Bliss independence analysis of colony survival data. Strong cell cycle perturbing effects were observed with ATR inhibition reversing the G2/M arrest observed for radiation-DNA-PKi. Increased apoptosis in combination groups was measured by Sub-G1 DNA populations. DNA-PKi increased radiation-induced RAD51 and gamma-H2Ax foci, with the addition of ATR inhibition reducing levels of both. A sharp increase in nuclear fragmentation after aberrant mitotic transit appears to be the main driver of decreased survival due to irradiation and dual ATR/DNA-PKi. Dual inhibition of DNA-PK and ATR represents a novel approach in combination with radiation, with efficacy appearing to be independent of p53 status. Due to toxicity concerns, careful assessment is necessary in any future translation of single or dual radiosensitization approaches. Ongoing clinical trials into the ATR inhibitor AZD6738 plus radiation, and the phenotypically similar combination of AZD6738 and the PARP inhibitor olaparib, are likely to be key in ascertaining the toxicity profile of such combinations.
A new graph modelisation for molecule similarity

In order to define the process of restrosynthesis of a new organic molecule, it is often necessary to be able to draw inspiration from that of a molecule similar to the target one of which we know such a process. To compute such a similarity, an oftently used approach is to solve a Maximum Common Edge Subgraph (MCES) problem on molecular graphs, but such an approach is limited by computation time and pertinence of similarity measurement. In this paper, we define and analyse here a new graph representation of molecules to algorithmically compare them. The purpose is to model the structure of molecule by a graph smaller than the molecular graph and representing the interconnexion of its elementary cycles. We provide an algorithm to efficiently obtain such a graph of cycles from a molecular graph. Then by solving MCES problems on those graphs, we evaluate the pertinence of using graphs of cycles for molecular similarity on a select set of molecules.
An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing

We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 “big” Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP
The Tu-Deng Conjecture holds almost surely

The Tu–Deng Conjecture is concerned with the sum of digits $w(n)$ of $n$ in base $2$ (the Hamming weight of the binary expansion of $n$) and states the following: assume that $k$ is a positive integer and$1\leqslant t<2^k-1$. Then\[\Bigl \lvert\Bigl\{(a,b)\in\bigl\{0,\ldots,2^k-2\bigr\}^2:a+b\equiv t\bmod 2^k-1, w(a)+w(b)<k\Bigr\}\Bigr \rvert\leqslant 2^{k-1}. \]We prove that the Tu–Deng Conjecture holds almost surely in the following sense: the proportion of $t\in[1,2^k-2]$ such that the above inequality holds approaches $1$ as $k\rightarrow\infty$. Moreover, we prove that the Tu–Deng Conjecture implies a conjecture due to T. W. Cusick concerning the sum of digits of $n$ and $n+t$.
Replicating and Improving Top-N Recommendations in Open Source Packages

Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have them implemented, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighbourhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In this paper, we have therefore implemented extensions to the open source recommendation package for the R language - denoted rrecsys - and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on two public datasets.
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees. We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward. The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model. The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires \textit{no explicit} uncertainty quantification. Instantiating our framework with simplification gives a variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO). Experiments demonstrate that SLBO achieves state-of-the-art performance when only one million or fewer samples are permitted on a range of continuous control benchmark tasks.
Local and relaxed clocks: the best of both worlds

Time-resolved phylogenetic methods use information about the time of sample collection to estimate the rate of evolution. Originally, the models used to estimate evolutionary rates were quite simple, assuming that all lineages evolve at the same rate, an assumption commonly known as the molecular clock. Richer and more complex models have since been introduced to capture the phenomenon of substitution rate variation among lineages. Two well known model extensions are the local clock, wherein all lineages in a clade share a common substitution rate, and the uncorrelated relaxed clock, wherein the substitution rate on each lineage is independent from other lineages while being constrained to fit some parametric distribution. We introduce a further model extension, called the flexible local clock (FLC), which provides a flexible framework to combine relaxed clock models with local clock models. We evaluate the flexible local clock on simulated and real datasets and show that it provides substantially improved fit to an influenza dataset. An implementation of the model is available for download from https://www.github. com/4ment/flc.
Correlations of Neural Activity with Behavioral Kinematics during Active Tactile Decision Making

Most real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus in order to reduce uncertainty and maximize information gain. Though ecologically pervasive, relatively limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object or surface by actively exploring its shape and texture. Here we investigate the neural mechanisms of active tactile sensing by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgements. We hypothesized that one's sensorimotor behavior provides a view into the cognitive processes leading to decision formation, and the neural correlates of these processes would be detectable by relating kinematics to neural activity. Using an adaptation of canonical correlation analysis (CCA), we regressed the EEG onto kinematics and found three distinct, task-related EEG components that localized to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively. To probe the functional role of these components, we fit their single-trial activity to behavior using a hierarchical drift diffusion model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. This study provides direct evidence that, how we explore the stimulus yields insight into how our brain is forming a decision and uncovers the neural correlates of distinct sensory encoding and evidence accumulation processes during active tactile sensing.
Task Specific Disambiguation in a Web Page: Towards Symbiotic Annotation

We describe Information Systems where users employ semantic tagging to annotate resources accessible through a web browser. Semantic tags are disambiguated key terms assigned by users. To facilitate the procedure for human users, we investigated algorithms which automatically disambiguate words selected from the text, using the surrounding context. We found existing generic methods of Word Sense Disambiguation (WSD) to be insufficient, and developed several new algorithms which take into consideration the specific structural properties of the annotated resources. Our preliminary findings are that it is relatively straightforward to hand craft simple task specific disambiguation algorithms using freely available libraries. However it is difficult to surpass the performance of state-of-the-art general algorithms. We observe that part of the problem is caused by the fine grained word senses used in disambiguation, which makes agreement difficult. We propose a task specific measure of accuracy which, combined with the task specific disambiguation algorithm, provides a more satisfactory solution to the problem than generic approaches. That is, while the hand crafted disambiguation algorithm could not out-perform standard algorithms in a general measure of accuracy, it did lead to an overall solution for the tagging task which proved superior to the standard algorithms. The final solution is an example of symbiotic computing, in which the human and machine actors interact in a mutually beneficent manner to achieve an outcome.
Incorporation of a decision space diversity maintenance mechanism into MOEA/D for multi-modal multi-objective optimization

A Multi-objective optimization problem with several different Pareto optimal solution sets is defined as a multi-modal multi-objective optimization problem. Finding all the Pareto optimal solution sets for this type of problem can provide more options for the decision maker, which is important in some real-world situations. The Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been proved to perform well in various multi-objective problems but it does not perform well in finding all the Pareto optimal solution sets for multi-modal multi-objective optimization problems. In this paper, a MOEA/D variant is proposed to solve these problems. K solutions are assigned to each weight vector in the MOEA/D variant and the solutions are evaluated by not only the scalarizing function values but also the minimum distance from other solutions with the same weight vector and the average distance from the neighboring solutions in the same weight vector grid. Experimental results show that the MOEA/D variant performs much better than the original MOEA/D on the multi-modal distance minimization problems.