Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate and robust segmentation of neuroanatomy in T1‐weighted MRI by combining spatial priors with deep convolutional neural networks

Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1‐weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration‐derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labeled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and subcortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state‐of‐the‐art accuracies and a high robustness to outliers. Further validation on a multistructure segmentation task in a scan–rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan–rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g., requiring only 10 s for left/right hippocampal segmentation in 1 × 1 × 1 mm3 MNI stereotaxic space), orders of magnitude faster than conventional multiatlas segmentation methods.
Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.
New combinational therapies for cancer using modern statistical mechanics

We investigate a new dynamical system that describes tumor-host interaction. The equation that describes the untreated tumor growth is based on non-extensive statistical mechanics. Recently, this model has been shown to fit successfully exponential, Gompertz, logistic, and power-law tumor growths. We have been able to include as many hallmarks of cancer as possible. We study also the dynamic response of cancer under therapy. Using our model, we can make predictions about the different outcomes when we change the parameters, and/or the initial conditions. We can determine the importance of different factors to influence tumor growth. We discover synergistic therapeutic effects of different treatments and drugs. Cancer is generally untreatable using conventional monotherapy. We consider conventional therapies, oncogene-targeted therapies, tumor-suppressors gene-targeted therapies, immunotherapies, anti-angiogenesis therapies, virotherapy, among others. We need therapies with the potential to target both tumor cells and the tumors' microenvironment. Drugs that target oncogenes and tumor-suppressor genes can be effective in the treatment of some cancers. However, most tumors do reoccur. We have found that the success of the new therapeutic agents can be seen when used in combination with other cancer-cell-killing therapies. Our results have allowed us to design a combinational therapy that can lead to the complete eradication of cancer.
Parameter-free Online Convex Optimization with Sub-Exponential Noise

We consider the problem of unconstrained online convex optimization (OCO) with sub-exponential noise, a strictly more general problem than the standard OCO. In this setting, the learner receives a subgradient of the loss functions corrupted by sub-exponential noise and strives to achieve optimal regret guarantee, without knowledge of the competitor norm, i.e., in a parameter-free way. Recently, Cutkosky and Boahen (COLT 2017) proved that, given unbounded subgradients, it is impossible to guarantee a sublinear regret due to an exponential penalty. This paper shows that it is possible to go around the lower bound by allowing the observed subgradients to be unbounded via stochastic noise. However, the presence of unbounded noise in unconstrained OCO is challenging; existing algorithms do not provide near-optimal regret bounds or fail to have a guarantee. So, we design a novel parameter-free OCO algorithm for Banach space, which we call BANCO, via a reduction to betting on noisy coins. We show that BANCO achieves the optimal regret rate in our problem. Finally, we show the application of our results to obtain a parameter-free locally private stochastic subgradient descent algorithm, and the connection to the law of iterated logarithms.
Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order

We address the problem of learning the parameters of a stable linear time invariant (LTI) system with unknown latent space dimension, or \textit{order}, from its noisy input-output data. In particular, we focus on learning the parameters of the best lower order approximation allowed by the finite data. This is achieved by constructing a Hankel-like representation of the underlying system using ordinary least squares. Such a representation circumvents the non-convexities that typically arise in system identification, and it allows accurate estimation of the underlying LTI system. Our results rely on a careful analysis of a self-normalized martingale difference term that helps bound identification error up to logarithmic factors of the lower bound. We provide a data-dependent scheme for order selection and find a realization of system parameters, corresponding to that order, by an approach that is closely related to the celebrated Kalman-Ho subspace algorithm. We show that this realization is a good approximation of the underlying LTI system with high probability. Finally, we demonstrate that the proposed model order selection procedure is minimax optimal, i.e., for the given data length it is not always possible to estimate higher order models or find higher order approximations with reasonable accuracy.
Reinforcement Learning for Uplift Modeling

Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that our method can achieve significant improvement over previous methods.
Abstractive Summarization of Spoken and Written Conversation

Nowadays, lots of information is available in form of dialogues. We propose a novel abstractive summarization system for conversations. We use sequence tagging of utterances for identifying the discourse relations of the dialogue. After aptly capturing these relations in a paragraph, we feed it into an Attention-based pointer network to produce abstractive summaries. We obtain ROUGE-1, 2 F-scores similar to those of extractive summaries of various previous works.
Dictionary learning approach to monitoring of wind turbine drivetrain bearings

Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations. Thus, we contribute novel test results and real world data that are made publicly available. The results of former studies addressing condition monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little insight into how useful these approaches are in practice. In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find the time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement.
How to “DODGE” Complex Software Analytics

Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring “redundant tunings”, i.e., pairs of tunings which lead to indistinguishable results. By ignoring redundant tunings, <bold>DODGE(<inline-formula><tex-math notation="LaTeX">$\mathcal {E})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">E</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="agrawal-ieq1-2945020.gif"/></alternatives></inline-formula></bold>, a tuning tool, runs orders of magnitude faster, while also generating learners with more accurate predictions than seen in prior state-of-the-art approaches.
Spaces of Clusterings

We propose two algorithms to cluster a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters, or with many initializations. We use these to study the effects of varying the parameters of HDBSCAN, and to study methods for initializing $k$-means.
A Modular Approach to Block-diagonal Hessian Approximations for Second-order Optimization Methods

We propose a modular extension of the backpropagation algorithm for computation of the block diagonal of the training objective's Hessian to various levels of refinement. The approach compartmentalizes the otherwise tedious construction of the Hessian into local modules. It is applicable to feedforward neural network architectures, and can be integrated into existing machine learning libraries with relatively little overhead, facilitating the development of novel second-order optimization methods. Our formulation subsumes several recently proposed block-diagonal approximation schemes as special cases. Our PyTorch implementation is included with the paper.
An Overview of Models for Response Times and Processes in Cognitive Tests

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches.
Coordinating Development: How Do Animals Integrate Plastic and Robust Developmental Processes?

Our developmental environment significantly affects myriad aspects of our biology, including key life history traits, morphology, physiology, and our susceptibility to disease. This environmentally-induced variation in phenotype is known as plasticity. In many cases, plasticity results from alterations in the rate of synthesis of important developmental hormones. However, while developmental processes like organ growth are sensitive to environmental conditions, others like patterning – the process that generates distinct cell identities – remain robust to perturbation. This is particularly surprising given that the same hormones that regulate organ growth also regulate organ patterning. In this review, we revisit the current approaches that address how organs coordinate their growth and pattern, and outline our hypotheses for understanding how organs achieve correct pattern across a range of sizes.
Non-invasive Brain Stimulation for the Rehabilitation of Children and Adolescents With Neurodevelopmental Disorders: A Systematic Review

In the last years, there has been a growing interest in the application of different non-invasive brain stimulation techniques to induce neuroplasticity and to modulate cognition and behavior in adults. Very recently, different attempts have been made to induce functional plastic changes also in pediatric populations. Importantly, not only sensorimotor processing, but also higher-level functions have been addressed, with the aim to boost rehabilitation in different neurodevelopmental disorders. However, efficacy and safety of using these techniques in pediatric population is still debated. The current article aims to review the non-invasive brain stimulation studies conducted in pediatric populations using Transcranial Magnetic Stimulation or transcranial Direct Current Stimulation. Specifically, the available proofs concerning the efficacy and safety of these techniques on Autism Spectrum Disorder, Attention-deficit/hyperactivity disorder, Dyslexia, Tourette syndrome, and tic disorders are systematically reviewed and discussed. The article also aims to provide an overview about other possible applications of these and other stimulation techniques for rehabilitative purposes in children and adolescents.
Pullulan Nanoparticles as Prebiotics Enhance the Antibacterial Properties of Lactobacillus plantarum Through the Induction of Mild Stress in Probiotics

Synbiotics, which are the combination of probiotics and prebiotics, have recently attracted attention because of their synergistic net health benefits. Probiotics have been used as alternatives to antibiotics. Among the probiotics, Lactobacillus plantarum (LP) has shown strong antimicrobial activity against Escherichia coli K99, a major livestock pathogen. In this study, we aimed to investigate the antimicrobial activity of phthalyl pullulan nanoparticle (PPN)-treated LP. Interestingly, when PPNs were added to LP, the PPNs were internalized into the LP through an energy-dependent and galactose transporter-dependent mechanism. Additionally, more plantaricin, a natural antibacterial peptide, was secreted from PPN-treated LP than from untreated or pullulan-treated LP. Furthermore, antimicrobial activity against Gram-negative Escherichia coli K99 and Gram-positive Listeria monocytogenes by PPN-treated LP was higher than those of untreated or pullulan-treated LP. It is thought that the enhanced antimicrobial properties of the PPN-treated LP are due to intracellular stimulation. Overall, this research provides a new method of producing plantaricin in LP through intracellular stimulation by internalized PPNs.
Rotational Mechanism Model of the Bacterial V1 Motor Based on Structural and Computational Analyses

V1-ATPase exemplifies the ubiquitous rotary motor, in which a central shaft DF complex rotates inside a hexagonally arranged catalytic A3B3 complex, powered by the energy from ATP hydrolysis. We have recently reported a number of crystal structures of the Enterococcus hirae A3B3DF (V1) complex corresponding to its nucleotide-bound intermediate states, namely the forms waiting for ATP hydrolysis (denoted as catalytic dwell), ATP binding (ATP-binding dwell), and ADP release (ADP-release dwell) along the rotatory catalytic cycle of ATPase. Furthermore, we have performed microsecond-scale molecular dynamics simulations and free-energy calculations to investigate the conformational transitions between these intermediate states and to probe the long-time dynamics of the molecular motor. In this article, the molecular structure and dynamics of the V1-ATPase are reviewed to bring forth a unified model of the motor’s remarkable rotational mechanism.
Soil Bacterial Communities From the Chilean Andean Highlands: Taxonomic Composition and Culturability

The Atacama Desert is a highly complex, extreme ecosystem which harbors microorganisms remarkable for their biotechnological potential. Here, a soil bacterial prospection was carried out in the high Altiplano region of the Atacama Desert (>3,800 m above sea level; m a.s.l.), where direct anthropogenic interference is minimal. We studied: (1) soil bacterial community composition using high-throughput sequencing of the 16S rRNA gene and (2) bacterial culturability, by using a soil extract medium (SEM) under a factorial design of three factors: temperature (15 and 30°C), nutrient content (high and low nutrient disposal) and oxygen availability (presence and absence). A total of 4,775 OTUs were identified and a total of 101 isolates were selected for 16S rRNA sequencing, 82 of them corresponded to unique or non-redundant sequences. To expand our view of the Altiplano landscape and to obtain a better representation of its microbiome, we complemented our Operational Taxonomic Units (OTUs) and isolate collection with data from other previous data from our group and obtained a merged set of OTUs and isolates that we used to perform our study. Taxonomic comparisons between culturable microbiota and metabarcoding data showed an overrepresentation of the phylum Firmicutes (44% of isolates vs. 2% of OTUs) and an underrepresentation of Proteobacteria (8% of isolates vs. 36% of OTUs). Within the Next Generation Sequencing (NGS) results, 33% of the OTUs were unknown up to genus, revealing an important proportion of putative new species in this environment. Biochemical characterization and analysis extracted from the literature indicated that an important number of our isolates had biotechnological potential. Also, by comparing our results with similar studies on other deserts, the Altiplano highland was most similar to a cold arid desert. In summary, our study contributes to expand the knowledge of soil bacterial communities in the Atacama Desert and complements the pipeline to isolate selective bacteria that could represent new potential biotechnological resources.
A proximal Peaceman–Rachford splitting method for solving the multi-block separable convex minimization problems

ABSTRACT The Peaceman–Rachford splitting method (PRSM) is well studied for solving the two-block separable convex minimization problems with linear constraints recently. In this paper, we consider the separable convex minimization problem where its objective function is the sum of more than two functions without coupled variables, when applying the PRSM to this case directly, it is not necessarily convergent. To remedy this difficulty, we propose a proximal Peaceman–Rachford splitting method for solving this multi-block separable convex minimization problems, which updates the Lagrangian multiplier two times at each iteration and solves some subproblems parallelly. Under some mild conditions, we prove global convergence of the new method and analyse the worst-case convergence rate in both ergodic and nonergodic senses. In addition, we apply the new method to solve the robust principal component analysis problem and report some preliminary numerical results to indicate the feasibility and effectiveness of the proposed method.
The Use of Data-driven Transformations and Their Applicability in Small Area Estimation

The R Package trafo for Transforming Linear Regression Models The linear regression model has been widely used for descriptive, predictive, and inferential purposes. This model relies on highly restrictive set of assumptions, which are not always fulfilled when working with empirical data. In this case, one solution could be the use of more complex regression methods that do not strictly rely in the same assumptions. However, in order to improve the validity of model assumptions, transformations are a simpler approach and enable the user to keep using the well-known linear regression model. But how can a user find a suitable transformation? The R package trafo offers a simple user-friendly framework for selecting a suitable transformation depending on the user needs. The collection of selected transformations and estimation methods in the package trafo complement and enlarge the methods that are existing in R so far.
Sustainable Transmission Planning in Imperfectly Competitive Electricity Industries: Balancing Economic and Environmental Outcomes

We explore the role of a transmission system operator (TSO) that builds a transmission line to accommodate renewable energy in order to lower emissions as required by government policy. In contrast to central planning, a TSO in a deregulated electricity industry can only indirectly influence outcomes through its choice of the transmission line capacity. Via a bi-level model, we show that this results in less transmission capacity and with limited emissions control in a perfectly competitive industry vis-a-vis a benchmark centrally planned system. A carbon charge on industry that fully accounts for the cost of pollution damage leads to a perfect alignment of incentives and maximised social welfare only under perfect competition. By contrast, a carbon charge may actually lower social welfare under a Cournot oligopoly as the resulting reduction in consumption facilitates the further exercise of market power.