Miracle-dz/UniFAR-FTUs huggingface dataset:
Nest defence of Tengmalm's owls reflects offspring survival prospects under fluctuating food conditions

Abstract Abstract. The nest defence behaviour of vole-eating Tengmalm's owls, Aegolius funereus , in the presence of a live American mink, Mustela vison, was studied. The study covered a 3-year vole cycle, where low, increasing and peak vole years followed each other in a predictable manner. First-year survival prospects of owlets in the increasing vole years were twice as high as in the low and peak years. Males may have adapted to local fluctuations in food abundance because of restricted natal and breeding dispersal, but nomadic females with wide natal dispersal may not possess such a response. As expected, male, but not female, nest defence was related to the survival prospects of offspring. Age-related differences in the intensity of nest defence were also studied. Surprisingly, a negative relationship was found between male age and the intensity of nest defence in the peak vole year. This suggests that before a vole crash, old males may have higher residual reproductive value than young ones or that old males may reduce their investment at a time when large investment in offspring is not beneficial.
MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.
Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance. Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation. Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain. Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75. Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.
Nocturnal and diurnal singing activity in the nightingale: correlations with mating status and breeding cycle

This study on the nightingale, Luscinia megarhynchos, is the first to examine both nocturnal and diurnal singing activity of mated and unmated males throughout a species’ entire breeding cycle. Nocturnal song was sung mostly by unmated males. After pair formation, males ceased nocturnal singing and resumed it if their mate deserted. These results strongly suggest that nocturnal song of unmated males functions to attract a mate. Diurnal singing activity before females settled was low and did not predict future mating status. However, unmated males showed a continuous increase in diurnal singing activity until the end of the breeding cycle, but diurnal singing activity of mated males decreased after the egg-laying period. Mated males resumed nocturnal song for, on average, 3 nights during egg laying by their mates. This second period of nocturnal song coincided with the peak of diurnal singing activity. Such a high male singing effort during egg laying might allow the female to adjust her reproductive effort to male quality, deter rival males (e.g. through honest announcement of the female’s fertility) or attract females for extrapair copulations.
Selective Laser-Induced Etching of 3D Precision Quartz Glass Components for Microfluidic Applications—Up-Scaling of Complexity and Speed

By modification of glasses with ultrafast laser radiation and subsequent wet-chemical etching (here named SLE = selective laser-induced etching), precise 3D structures have been produced, especially in quartz glass (fused silica), for more than a decade. By the combination of a three-axis system to move the glass sample and a fast 3D system to move the laser focus, the SLE process is now suitable to produce more complex structures in a shorter time. Here we present investigations which enabled the new possibilities. We started with investigations of the optimum laser parameters to enable high selective laser-induced etching: surprisingly, not the shortest pulse duration is best suited for the SLE process. Secondly we investigated the scaling of the writing velocity: a faster writing speed results in higher selectivity and thus higher precision of the resulting structures, so the SLE process is now even suitable for the mass production of 3D structures. Finally we programmed a printer driver for commercial CAD software enabling the automated production of complex 3D glass parts as new examples for lab-on-a-chip applications such as nested nozzles, connectors and a cell-sorting structure.
A bioenergetics evaluation of temperature‐dependent selection for the spawning phenology by Snake River fall Chinook salmon

Abstract High water temperatures can increase the energetic cost for salmon to migrate and spawn, which can be important for Snake River fall‐run Chinook salmon because they migrate great distances (>500 km) at a time when river temperatures (18–24°C) can be above their optimum temperatures (16.5°C). Average river temperatures and random combinations of migration and spawning dates were used to simulate fish travel times and determine the energetic consequences of different thermal experiences during migration. An energy threshold criterion (4 kJ/g) was also imposed on survival and spawning success, which was used to determine how prevailing temperatures might select against certain migration dates and thermal experiences, and in turn, explain the selection for the current spawning phenology of the population. Scenarios of tributary use for thermal refugia under increasing water temperatures (1, 2, and 3°C) were also run to determine which combinations of migration dates, travel rates, and resulting thermal experiences might be most affected by energy exhaustion. As expected, when compared to observations, the model under existing conditions and energy use could explain the onset, but not the end of the observed spawning migration. Simulations of early migrants had greater energy loss than late migrants regardless of the river temperature scenario, but higher temperatures disproportionately selected against a larger fraction of early‐migrating fish, although using cold‐water tributaries during migration provided a buffer against higher energy use at higher temperatures. The fraction of simulated fish that exceeded the threshold for migration success increased from 58% to 72% as average seasonal river temperatures over baseline temperatures increased. The model supports the conclusion that increases in average seasonal river temperatures as little as 1°C could impose greater thermal constraints on the fish, select against early migrants, and in turn, truncate the onset of the current spawning migration.
Demonstration of a Robust All-Silicon-Carbide Intracortical Neural Interface

Intracortical neural interfaces (INI) have made impressive progress in recent years but still display questionable long-term reliability. Here, we report on the development and characterization of highly resilient monolithic silicon carbide (SiC) neural devices. SiC is a physically robust, biocompatible, and chemically inert semiconductor. The device support was micromachined from p-type SiC with conductors created from n-type SiC, simultaneously providing electrical isolation through the resulting p-n junction. Electrodes possessed geometric surface area (GSA) varying from 496 to 500 K μm2. Electrical characterization showed high-performance p-n diode behavior, with typical turn-on voltages of ~2.3 V and reverse bias leakage below 1 nArms. Current leakage between adjacent electrodes was ~7.5 nArms over a voltage range of −50 V to 50 V. The devices interacted electrochemically with a purely capacitive relationship at frequencies less than 10 kHz. Electrode impedance ranged from 675 ± 130 kΩ (GSA = 496 µm2) to 46.5 ± 4.80 kΩ (GSA = 500 K µm2). Since the all-SiC devices rely on the integration of only robust and highly compatible SiC material, they offer a promising solution to probe delamination and biological rejection associated with the use of multiple materials used in many current INI devices.
Evaluation of Bluestar® Forensic Magnum and Other Traditional Blood Detection Methods on Bloodstained Wood Subjected to a Variety of Burn Conditions ,

Accurate blood detection is a primary concern for forensic scientists, especially in highly compromised situations. In this study, blood was added to wood blocks and subjected to a variety of fire treatments: the absence or presence of accelerant, burn time (1, 3, or 5 min), and extinguishment method (smothering or dousing with water). Burned blocks were given a qualitative burn score, followed by removal of half of the char from each block and subsequent testing of each half for blood using luminol (13% positive; n = 96), Bluestar® Forensic Magnum (5.2% positive; n = 96), and combined phenolphthalein tetramethylbenzidine test (0% positive; n = 192). Luminol and Bluestar® Forensic Magnum performed similarly, both outperforming PTMB. Additionally, positive results were more likely from samples that were smothered, had a low burn score, and had more concentrated blood solutions (neat or 1:2). Overall, it is extremely unlikely that blood would be detected on combustible substrates exposed to direct fire.
Multi-Objective Optimizations of a Serpentine Micromixer with Crossing Channels at Low and High Reynolds Numbers

In order to maximize the mixing performance of a micromixer with an integrated three-dimensional serpentine and split-and-recombination configuration, multi-objective optimizations were performed at two different Reynolds numbers, 1 and 120, based on numerical simulation. Numerical analyses of fluid flow and mixing in the micromixer were performed using three-dimensional Navier-Stokes equations and convection-diffusion equation. Three dimensionless design variables that were related to the geometry of the micromixer were selected as design variables for optimization. Mixing index at the exit and pressure drop through the micromixer were employed as two objective functions. A parametric study was carried out to explore the effects of the design variables on the objective functions. Latin hypercube sampling method as a design-of-experiment technique has been used to select design points in the design space. Surrogate modeling of the objective functions was performed by using radial basis neural network. Concave Pareto-optimal curves comprising of Pareto-optimal solutions that represents the trade-off between the objective functions were obtained using a multi-objective genetic algorithm at Re = 1 and 120. Through the optimizations, maximum enhancements of 18.8% and 6.0% in mixing index were achieved at Re = 1 and 120, respectively.
Aging and Sex Influence Cortical Auditory-Motor Integration for Speech Control

It is well known that acoustic change in speech production is subject to age-related declines. How aging alters cortical sensorimotor integration in speech control, however, remains poorly understood. The present event-related potential study examined the behavioral and neural effects of aging and sex on the auditory-motor processing of voice pitch errors. Behaviorally, older adults produced significantly larger vocal compensations for pitch perturbations than young adults across the sexes, while the effects of sex on vocal compensation did not exist for both young and older adults. At the cortical level, there was a significant interaction between aging and sex on the N1-P2 complex. Older males produced significantly smaller P2 amplitudes than young males, while young males produced significantly larger N1 and P2 amplitudes than young females. In addition, females produced faster N1 responses than males regardless of age, while young adults produced faster P2 responses than older adults across the sexes. These findings provide the first neurobehavioral evidence that demonstrates the aging influence on auditory feedback control of speech production, and highlight the importance of sex in understanding the aging of the neuromotor control of speech production.
Fathead minnows, Pimephales promelas, acquire predator recognition when alarm substance is associated with the sight of unfamiliar fish

Abstract Abstract. To determine whether fathead minnows can learn to recognize potential predators through releaser-induced recognition learning, predator-naive minnows were simultaneously exposed to a neutral visual stimulus, the sight of a northern pike, Esox lucius , or a goldfish, Carassius auratus , paired with either minnow alarm substance (Schreckstoff) or water. Two days after this initial conditioning trial the visual stimuli were presented alone and minnows previously conditioned with alarm substance exhibited an appropriate anti-predator response, while those conditioned with water did not. The conditioned minnows were tested approximately 2 months later and again the minnows that were previously conditioned with alarm substance showed an appropriate anti-predator response, while those conditioned with water did not. A comparison of the reaction of minnows conditioned to a natural predator (the pike) and those conditioned to a non-piscivorous exotic (the goldfish) revealed a similar response intensity when tested 2 days after the initial conditioning trial. However, approximately 2 months after the conditioning trial, the reaction of minnows conditioned to pike was stronger than that of minnows conditioned to goldfish, indicating that learning may be constrained to favour a response to the natural predator. Minnows that were initially conditioned to pike did not show an anti-predator response to goldfish nor did minnows that were conditioned to goldfish respond to pike, demonstrating that the learned response was specific to the species used in the conditioning trials and not to any large fish. These results extend the known benefits to alarm-signal receivers.
Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data

We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors.
GlobalTrait: Personality Alignment of Multilingual Word Embeddings

We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian. Our analysis shows that words having a similar semantic meaning in different languages do not necessarily correspond to the same personality traits. Therefore, we propose a personality alignment method, GlobalTrait, which has a mapping for each trait from the source language to the target language (English), such that words that correlate positively to each trait are close together in the multilingual vector space. Using these aligned embeddings for training, we can transfer personality related training features from high-resource languages such as English to other low-resource languages, and get better multilingual results, when compared to using simple monolingual and unaligned multilingual embeddings. We achieve an average F-score increase (across all three languages except English) from 65 to 73.4 (+8.4), when comparing our monolingual model to multilingual using CNN with personality aligned embeddings. We also show relatively good performance in the regression tasks, and better classification results when evaluating our model on a separate Chinese dataset.
Collaboration and followership: A stochastic model for activities in social networks

In this work we investigate how future actions are influenced by the previous ones, in the specific contexts of scientific collaborations and friendships on social networks. We describe the activity of the agents, providing a model for the formation of the bipartite network of actions and their features. Therefore we only require to know the chronological order in which the actions are performed, and not the order in which the agents are observed. Moreover, the total number of possible features is not specified a priori but is allowed to increase along time, and new actions can independently show some new-entry features or exhibit some of the old ones. The choice of the old features is driven by a degree-fitness method: indeed, the probability that a new action shows one of the old features does not solely depend on the popularity of that feature (i.e. the number of previous actions showing it), but it is also affected by some individual traits of the agents or the features themselves, synthesized in certain quantities, called fitnesses or weights, that can have different forms and different meaning according to the specific setting considered. We show some theoretical properties of the model and provide statistical tools for the parameters’ estimation. The model has been tested on three different datasets and the numerical results are provided and discussed.
Augmenting yogurt quality attributes through hydrocolloidal gums

Objective The present work was undertaken to determine the possibility of using xanthan and guar gums as stabilizers to enhance the yogurt quality. Methods Yogurt was manufactured from standardized milk (3.5% fat, 8.5% solid-not-fat contents) with the addition of 2% to 3% starter culture. Enzyme-hydrolyzed xanthan gum (0.1%, 0.5%, 1.0%) and guar gum (0.1%, 0.5%, 1.0%) were added to the yogurt as stabilizers. Prepared yogurt samples were kept at refrigeration temperature (4°C±2°C) for 21 days and various quality and sensory parameters were studied at regular intervals (7 days). Results Results showed that yogurt with 0.5% xanthan gum (T5) was best in terms of preventing syneresis and improving the viscosity, water holding capacity and texture of the product. Additionally, adding gums did not adversely affect the sensorial attributes of the product. Conclusion Modified gums were found useful in augmenting yogurt quality and therefore addition of gums is highly recommended for manufacturing yogurt.
Management of anastomotic biliary stricture after liver transplantation: metal versus plastic stent

Background Post-transplant anastomotic biliary strictures remain refractory to endoscopic therapy in a considerable number of cases. The aim of this meta-analysis was to compare fully-covered self-expandable metal and plastic stents in the management of post-transplant biliary strictures. Methods A meta-analysis was performed using a random effects model; results were expressed as odds ratio (OR) and mean standardized difference. The primary outcome was stricture resolution, while recurrence rate after stent placement, treatment time, and safety of the procedure were the secondary outcomes. Results Through a systematic literature review until October 2017, we identified 7 studies, of which 4 were randomized controlled trials. Stricture resolution was slightly higher with metal stents, with no statistical difference between the two procedures (OR 1.38, 95% confidence interval [CI] 0.60-3.15; P=0.45) and low heterogeneity (I2=6%). Stricture recurrence showed a non-significant trend in favor of plastic stents (OR 1.82, 95%CI 0.52-6.31, P=0.35). Endoscopic retrograde cholangiopancreatography with placement of metal stents offered a significant improvement in terms of reduced treatment time (mean standardized difference: -3.58 months, 95%CI -6.23 to -0.93; P=0.008), but with more frequent complications, although not significantly so (OR 2.34, 95%CI 0.75-7.25; P=0.14). Sensitivity analysis confirmed all the findings. Conclusion Metal stents appear to be a promising tool that can decrease treatment time, although there is still no clear evidence of their superiority over plastic stents in terms of efficacy.
The UEA multivariate time series classification archive, 2018

In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing the total to 128. The new archive contains a wide range of problems, including variable length series, but it still only contains univariate time series classification problems. One of the motivations for introducing the archive was to encourage researchers to perform a more rigorous evaluation of newly proposed time series classification (TSC) algorithms. It has worked: most recent research into TSC uses all 85 datasets to evaluate algorithmic advances. Research into multivariate time series classification, where more than one series are associated with each class label, is in a position where univariate TSC research was a decade ago. Algorithms are evaluated using very few datasets and claims of improvement are not based on statistical comparisons. We aim to address this problem by forming the first iteration of the MTSC archive, to be hosted at the website this http URL. Like the univariate archive, this formulation was a collaborative effort between researchers at the University of East Anglia (UEA) and the University of California, Riverside (UCR). The 2018 vintage consists of 30 datasets with a wide range of cases, dimensions and series lengths. For this first iteration of the archive we format all data to be of equal length, include no series with missing data and provide train/test splits.