Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Approximation of Minimum k-edge-connected Spanning Subgraphs

In the minimum k-edge-connected spanning subgraph (k-ECSS) problem the goal is to find the minimum weight subgraph resistant to up to k-1 edge failures. This is a central problem in network design, and a natural generalization of the minimum spanning tree (MST) problem. While the MST problem has been studied extensively by the distributed computing community, for k ≥2 less is known in the distributed setting. In this paper, we present fast randomized distributed approximation algorithms for k-ECSS in the CONGEST model. Our first contribution is an Õ (D + √ )-round O(logn )-approximation for 2-ECSS, for a graph with n vertices and diameter D. The time complexity of our algorithm is almost tight and almost matches the time complexity of the MST problem. For larger constant values of k we give an Õ (n) -round O(logn ) -approximation. Additionally, in the special case of unweighted 3-ECSS we show how to improve the time complexity to O(D log^3n ) rounds. All our results significantly improve the time complexity of previous algorithms.
Boosting Algorithm for Real-time Multivariate Time Series Classification

Multivariate time series (MTS) is useful for detecting abnormity cases in healthcare area. In this paper, we propose an ensemble boosting algorithm to classify abnormality surgery time series based on learning shapelet features. Specifically, we first learn shapelets by logistic regression from multivariate time series. Based on the learnt shapelets, we propose a MTS ensemble boosting approach when the time series arrives as stream fashion. Experimental results on a real-world medical dataset demonstrate the effectiveness of the proposed methods.
Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning

We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in {SGD}. No estimate of the Hessian is maintained. We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly generated architectures.
Leveraging Saccades to Learn Smooth Pursuit: A Self-Organizing Motion Tracking Model Using Restricted Boltzmann Machines

In this paper, we propose a biologically-plausible model to explain the emergence of motion tracking behaviour in early development using unsupervised learning. The model's training is biased by a concept called retinal constancy, which measures how similar visual contents are between successive frames. This biasing is similar to a reward in reinforcement learning, but is less explicit, as it modulates the model's learning rate instead of being a learning signal itself. The model is a two-layer deep network. The first layer learns to encode visual motion, and the second layer learns to relate that motion to gaze movements, which it perceives and creates through bi-directional nodes. By randomly generating gaze movements to traverse the local visual space, desirable correlations are developed between visual motion and the appropriate gaze to nullify that motion such that maximal retinal constancy is achieved. Biologically, this is similar to using saccades to look around and learning from moments where a target and the saccade move together such that the image stays the same on the retina, and developing smooth pursuit behaviour to perform this action in the future. Restricted Boltzmann machines are used to implement this model because they can form a deep belief network, perform online learning, and act generatively. These properties all have biological equivalents and coincide with the biological plausibility of using saccades as leverage to learn smooth pursuit. This method is unique because it uses general machine learning algorithms, and their inherent generative properties, to learn from real-world data. It also implements a biological theory, uses motion instead of recognition via local searches, without temporal filtering, and learns in a fully unsupervised manner. Its tracking performance after being trained on real-world images with simulated motion is compared to its tracking performance after being trained on natural video. Results show that this model is able to successfully follow targets in natural video, despite partial occlusions, scale changes, and nonlinear motion.
Psychedelic Fauna for Psychonaut Hunters: A Mini-Review

Currently different classes of psychoactive substances are easily available for abuse, including several hundred novel psychoactive substances (NPS). Some of these drugs occur naturally in plants and animals or are chemically modified from plant or animal compounds and have been abused by humans over millennia. Recently, the occurrence of a new “drug culture” (e.g., psychonauts) who consume a great variety of NPS with hallucinogenic/psychedelic properties, facilitated the development of a new “psychedelic trend” toward the consumption of substances contained in some species of animals (“psychedelic fauna”). The present review aims at providing an overview of the most commonly abused “psychedelic animals,” by combining a dual search strategy coming from online psychonauts' experiences and English literature searches on the PubMed/Medline Google Scholar databases. A multilingual qualitative assessment on a range of websites and online resources was performed in order to identify a list of animals who possess some psychoactive properties and could be abused by humans for recreational purposes. Several species are implicated (i.e., ants, amphibians, fish). Routes of administration depend on the animal, substance included, metabolism, toxicity and individual, social and cultural variability. Online purchase and access are easy through tourism-related search strategies (“frog trip,” “help of charmer snake,” “religious trip”).
A Comparison of Reproductive and Energetic States in a Marine Apex Predator (the Tiger Shark, Galeocerdo cuvier)

To fuel the high energetic demands of reproduction, vertebrates employ different tactics of resource use. Large sharks exhibit long gestation periods and have relatively few well-developed young, which likely incurs high energetic costs. However, information on the relationship between the reproductive and energetic states for most shark species is lacking. In the present study, we used a noninvasive approach to assess relationships among reproductive stage, plasma triglyceride levels, body condition, and circulating reproductive hormones in free-ranging female tiger sharks (Galeocerdo cuvier). A total of 57 sharks were sampled (19 immature, 15 mature/nongravid, and 23 gravid). Circulating plasma triglycerides did not significantly differ among female tiger sharks of different reproductive stages, but body condition values were significantly higher for mature/nongravid sharks (1.29±0.23) compared with gravid (1.15±0.08) and immature (1.13±0.07) sharks. For gravid and mature/nongravid sharks, no significant correlations existed among values of reproductive hormones, plasma triglycerides, and body condition. However, for immature sharks, estradiol values were negatively correlated with body condition values. Additionally, progesterone levels were positively correlated with testosterone levels in immature females. Our results suggest that this large generalist predator may not necessarily be easily characterized as a pure capital breeder, as has been previously hypothesized for ectotherms, but may rely on a mix of energy stores and opportunistic feeding to support reproduction. We present several hypotheses to explain these patterns and discuss our results in terms of energetic reproductive strategies.
Fumonisin B1 actuates oxidative stress‐associated colonic damage via apoptosis and autophagy activation in murine model

In the present study, we investigated the cytotoxic mechanism of Fumonisin B1 (FB1) in mice colonic region in a time course manner. Herein, after consecutive 4 days of exposure to FBI (2.5 mg/kg body weight), we observed disintegration of mice colon, as evidenced by histopathological analysis. FB1 significantly increased alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase activities in serum and plasma, decreased ceramide level, increased sphinganine level, and increased lipid peroxidase level along with the breakdown of the antioxidant system. Further, FB1‐induced ER stress caused apoptosis and autophagy activation in mice colon, evidenced by increased expression of IRE1‐α, p‐JNK, Casp3, and LC3I/II. In addition, we also noticed a reduced protein kinase C expression in mice colon exposed to FB1, suggesting its role in ER stress‐induced cell death. Taken together, study suggests both physiologically and biochemically, FB1 toxicity to mice colon induced by oxidative stress‐associated apoptosis and autophagy activation.
Greedy Flipping for Constrained Word Deletion

In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer linear programming solvers. Our formulation suits for any objective function involving arbitrary local score definition. Experimental results show that the proposed method achieves nearly identical performance with explicit ILP formulation while being much more efficient.
Reliable counting of weakly labeled concepts by a single spiking neuron model

Making an informed, correct and quick decision can be life-saving. It's crucial for animals during an escape behaviour or for autonomous cars during driving. The decision can be complex and may involve an assessment of the amount of threats present and the nature of each threat. Thus, we should expect early sensory processing to supply classification information fast and accurately, even before relying the information to higher brain areas or more complex system components downstream. Today, advanced convolutional artificial neural networks can successfully solve visual detection and classification tasks and are commonly used to build complex decision making systems. However, in order to perform well on these tasks they require increasingly complex, "very deep" model structure, which is costly in inference run-time, energy consumption and number of training samples, only trainable on cloud-computing clusters. A single spiking neuron has been shown to be able to solve recognition tasks for homogeneous Poisson input statistics, a commonly used model for spiking activity in the neocortex. When modeled as leaky integrate and fire with gradient decent learning algorithm it was shown to posses a variety of complex computational capabilities. Here we improve its implementation. We also account for more natural stimulus generated inputs that deviate from this homogeneous Poisson spiking. The improved gradient-based local learning rule allows for significantly better and stable generalization. We also show that with its improved capabilities it can count weakly labeled concepts by applying our model to a problem of multiple instance learning (MIL) with counting where labels are only available for collections of concepts. In this counting MNIST task the neuron exploits the improved implementation and outperforms conventional ConvNet architecture under similar condtions.
New Era of Studying RNA Secondary Structure and Its Influence on Gene Regulation in Plants

The dynamic structure of RNA plays a central role in post-transcriptional regulation of gene expression such as RNA maturation, degradation, and translation. With the rise of next-generation sequencing, the study of RNA structure has been transformed from in vitro low-throughput RNA structure probing methods to in vivo high-throughput RNA structure profiling. The development of these methods enables incremental studies on the function of RNA structure to be performed, revealing new insights of novel regulatory mechanisms of RNA structure in plants. Genome-wide scale RNA structure profiling allows us to investigate general RNA structural features over 10s of 1000s of mRNAs and to compare RNA structuromes between plant species. Here, we provide a comprehensive and up-to-date overview of: (i) RNA structure probing methods; (ii) the biological functions of RNA structure; (iii) genome-wide RNA structural features corresponding to their regulatory mechanisms; and (iv) RNA structurome evolution in plants.
Direct cortical stimulation of inferior frontal cortex disrupts both speech and music production in highly trained musicians

ABSTRACT Music and speech are human-specific behaviours that share numerous properties, including the fine motor skills required to produce them. Given these similarities, previous work has suggested that music and speech may at least partially share neural substrates. To date, much of this work has focused on perception, and has not investigated the neural basis of production, particularly in trained musicians. Here, we report two rare cases of musicians undergoing neurosurgical procedures, where it was possible to directly stimulate the left hemisphere cortex during speech and piano/guitar music production tasks. We found that stimulation to left inferior frontal cortex, including pars opercularis and ventral pre-central gyrus, caused slowing and arrest for both speech and music, and note sequence errors for music. Stimulation to posterior superior temporal cortex only caused production errors during speech. These results demonstrate partially dissociable networks underlying speech and music production, with a shared substrate in frontal regions.
Fundamental Trade-offs in Communication and Trajectory Design for UAV-Enabled Wireless Network

The use of unmanned aerial vehicles (UAVs) as aerial communication platforms is of high practical value to future wireless systems such as 5G, especially for swift and on-demand deployment in temporary events and emergency situations. Compared to the static terrestrial base stations (BSs) in cellular networks, UAV-mounted aerial BSs possess stronger line-of-sight links with the ground users due to their high altitude as well as high and flexible mobility in 3D space, which can be exploited to enhance the communication performance. On the other hand, unlike terrestrial BSs that have reliable power supply, aerial BSs in practice have limited onboard energy, but require high propulsion power to stay airborne and support high mobility. Motivated by the above new considerations, this article aims to revisit some fundamental trade-offs in UAV-enabled communication and trajectory design. Specifically, it is shown that the communication throughput, delay, and (propulsion) energy consumption can be traded off among each other by adopting different UAV trajectory designs, which sheds new light on their existing trade-offs in terrestrial communication. The main design challenges and promising directions for future research are also discussed.
Research on Blind Beamforming Algorithm Based on Signal's Cyclostationary

The blind beamforming algorithm based on the signal's cyclostationary can achieve the desired signal blind detection and recognition only by the known cycle frequency of the signal, so has been increasingly widely used in the field of signal processing. However, the estimation of the cyclic frequency of the signal is inaccurate in the actual situation, and the performance degradation of the algorithm increases with the increase of error. As for this, on the basis of the traditional blind beamforming algorithm(CAB) and the improved algorithm (F-CAB), it proposed a CAB algorithm with adaptive forgetting factor. The simulation results show that the proposed algorithm has strong robustness and adaptability to the cyclic frequency mismatch.
Identifying Change Point in Production Time-Series Volatility Using Control Charts and Stochastic Differential Equations

The article focuses on volatility change point detection using SPC (Statistical Process Control) meth- ods, specifically time-series control charts and stochastic differential equations (SDEs). Contribution will review recent advances in change point detection for the volatility component of a process satisfying stochastic differen- tial equation (SDE) based on discrete observations, and also by using time-series control charts. Theoretical part will discuss methodology of time-series control charts and SDEs driven by a Brownian motion. Research part will demonstrate the methodologies in a simulation study focusing on analysis of the AR(1) process by means of time-series control charts and SDEs. The aim is to make use of change point detection in time series of production processes and highlight versatility of control charts not only in manufacturing but also in managing financial cash flow stability.
Towards remote sensing of vegetation processes

The latest advances in imaging spectroscopy of vege tation enabled remote sensing (RS) of plant reflected or emitted signals associated wi th photosynthetic processes as the photoprotective transformation of xanthophyll pigments or the chlor ophyll fluorescence (Chl-F). A potential future European Space Agency (ESA) satellite mission FLEX is expected to sense, apart from other parameters, so-called steady-state chlorophyll fluo rescence (Chl-FS) signal, which may be potentially used for monitoring of photosynthesis ( vegetation canopy carbon assimilation rate). Nevertheless, geometric complexity of plant canopie s and signal disturbing atmospheric factors require a proper approach for scaling the informati on of a single leaf optical properties up to the RS image data of anisotropic vegetation canopies. Such up-scaling approach can be established only via synergic measurements of ground based and air-/space-borne optical sensors. Our initial experiment revealed that Chl-FS, being strongly driven by the air temperature, is able to accurately indicate on- set and off-set of the photosynthetically active pe riod for the evergreen plants. Next field experiment, carried out with the VNIR imaging spectroradiometer AISA Eagle (SPECIM Ltd., Finland) mounted above the montane grassland and Norway spruce ( Picea abies /L./ Karst.) canopies, showed that the fluorescence signal is re trievable from passive optical imaging spectroscopy data. Further analyses revealed that s ome of the vegetation 'process-related' optical indices (e.g., photochemical reflectance index - PR I) are closely correlated to the parameters measured over the experimental canopies by eddy-covariance flux systems. The future objective is to continue in development the leaf-canopy Chl-F up -scaling approach by setting up local scale experiments employing the field pocket-size cost ef fective instruments measuring the leaf optical indices and Chl-F parameters simultaneously with ca nopy reflectance acquired by RS sensors from tower and aircraft platforms.
Session 1: Public health nutrition Breast-feeding practices in Ireland

Breast-feeding is the superior infant feeding method from birth, with research consistently demonstrating its numerous short- and long-term health benefits for both mother and infant. As a global recommendation the WHO advises that mothers should exclusively breast-feed for the first 6-months of life, thus delaying the introduction of solids during this time. Historically, Irish breast-feeding initiation rates have remained strikingly low in comparison with international data and there has been little improvement in breast-feeding duration rates. There is wide geographical variation in terms of breast-feeding initiation both internationally and in Ireland. Some of these differences in breast-feeding rates may be associated with differing socio-economic characteristics. A recent cross-sectional prospective study of 561 pregnant women attending a Dublin hospital and followed from the antenatal period to 6 months post partum has found that 47% of the Irish-national mothers initiated breast-feeding, while only 24% were still offering ‘any’ breast milk to their infants at 6 weeks. Mothers' positive antenatal feeding intention to breast-feed is indicated as one of the most important independent determinants of initiation and ‘any’ breast-feeding at 6 weeks, suggesting that the antenatal period should be targeted as an effective time to influence and affect mothers' attitudes and beliefs pertaining to breast-feeding. These results suggest that the ‘cultural’ barrier towards breast-feeding appears to still prevail in Ireland and consequently an environment that enables women to breast-feed is far from being achieved. Undoubtedly, a shift towards a more positive and accepting breast-feeding culture is required if national breast-feeding rates are to improve.
The sufficiently smart compiler is a theorem prover

That the Haskell Compiler GHC is capable of proving non-trivial equalities between Haskell code, by virtue of its aggressive optimizer, in particular the term rewriting engine in the simplifier. We demonstrate this with a surprising little code in a GHC plugin, explains the knobs we had to turn, discuss the limits of the approach and related applications of the same idea, namely testing that promises from Haskell libraries with domain-specific optimizations hold.
Pure erythroid leukemia in a polymyositis patient treated with azathioprine

Acute erythroid leukemia, also known as acute myeloid leukemia-M6, may be associated with previous chemotherapy or immunosuppressive therapy. For 10 years, a 69-year-old Japanese female patient with pure erythroid leukemia (or acute myeloid leukemia-M6b) was treated for polymyositis with 50–100 mg/day azathioprine. She complained of dyspnea with low-grade fever and was diagnosed as having pure erythroid leukemia. Chromosomal analysis revealed a complex karyotype abnormality, with the deletion of 5q, -6, -7 and addition of 11q13. No morphological myelodysplastic changes were observed in her bone marrow cells. In this study, azathioprine accumulation was considered to be associated with the patient’s leukemogenesis.
Psychological Forest: Predicting Human Behavior

We introduce a synergetic approach incorporating psychological theories and data science in service of predicting human behavior. Our method harnesses psychological theories to extract rigorous features to a data science algorithm. We demonstrate that this approach can be extremely powerful in a fundamental human choice setting. In particular, a random forest algorithm that makes use of psychological features that we derive, dubbed psychological forest, leads to prediction that significantly outperforms best practices in a choice prediction competition. Our results also suggest that this integrative approach is vital for data science tools to perform reasonably well on the data. Finally, we discuss how social scientists can learn from using this approach and conclude that integrating social and data science practices is a highly fruitful path for future research of human behavior.