Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditions Implying Energy Equality for Weak Solutions of the Navier-Stokes Equations

When a Leray--Hopf weak solution to the NSE has a singularity set $S$ of dimension $d$ less than $3$---for example, a suitable weak solution---we find a family of new $L^q L^p$ conditions that guarantee validity of the energy equality. Our conditions surpass the classical Lions--Ladyženskaja $L^4 L^4$ result in the case $d<1$. Additionally, we establish energy equality in certain cases of Type-I blowup. The results are also extended to the NSE with fractional power of the Laplacian below $1$.
Geographic Variation of Strontium and Hydrogen Isotopes in Avian Tissue: Implications for Tracking Migration and Dispersal

Background Isotopes can provide unique solutions to fundamental problems related to the ecology and evolution of migration and dispersal because prior movements of individuals can theoretically be tracked from tissues collected from a single capture. However, there is still remarkably little information available about how and why isotopes vary in wild animal tissues, especially over large spatial scales. Methodology/Principal Findings Here, we describe variation in both stable-hydrogen (δDF) and strontium (87Sr/86SrF) isotopic compositions in the feathers of a migratory songbird, the Tree Swallow (Tachycineta bicolor), across 18 sampling sites in North America and then examine potential mechanisms driving this variation. We found that δDF was correlated with latitude of the sampling site, whereas 87Sr/86SrF was correlated with longitude. δDF was related to δD of meteoric waters where molting occurred and 87Sr/86SrF was influenced primarily by the geology in the area where feathers were grown. Using simulation models, we then assessed the utility of combining both markers to estimate the origin of individuals. Using 13 geographic regions, we found that the number of individuals correctly assigned to their site of origin increased from less than 40% using either δD or 87Sr/86Sr alone to 74% using both isotopes. Conclusions/Significance Our results suggest that these isotopes have the potential to provide predictable and complementary markers for estimating long-distance animal movements. Combining isotopes influenced by different global-scale processes may allow researchers to link the population dynamics of animals across large geographic ranges.
Multi-objective Contextual Bandit Problem with Similarity Information

In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the context, learning the best arm based on a single objective can jeopardize the rewards obtained from the other objectives. In order to evaluate the performance of the learner in this setup, we use a performance metric called the contextual Pareto regret. Essentially, the contextual Pareto regret is the sum of the distances of the arms chosen by the learner to the context dependent Pareto front. For this problem, we develop a new online learning algorithm called Pareto Contextual Zooming (PCZ), which exploits the idea of contextual zooming to learn the arms that are close to the Pareto front for each observed context by adaptively partitioning the joint context-arm set according to the observed rewards and locations of the context-arm pairs selected in the past. Then, we prove that PCZ achieves $\tilde O (T^{(1+d_p)/(2+d_p)})$ Pareto regret where $d_p$ is the Pareto zooming dimension that depends on the size of the set of near-optimal context-arm pairs. Moreover, we show that this regret bound is nearly optimal by providing an almost matching $\Omega (T^{(1+d_p)/(2+d_p)})$ lower bound.
Dinosaur Peptides Suggest Mechanisms of Protein Survival

Eleven collagen peptide sequences recovered from chemical extracts of dinosaur bones were mapped onto molecular models of the vertebrate collagen fibril derived from extant taxa. The dinosaur peptides localized to fibril regions protected by the close packing of collagen molecules, and contained few acidic amino acids. Four peptides mapped to collagen regions crucial for cell-collagen interactions and tissue development. Dinosaur peptides were not represented in more exposed parts of the collagen fibril or regions mediating intermolecular cross-linking. Thus functionally significant regions of collagen fibrils that are physically shielded within the fibril may be preferentially preserved in fossils. These results show empirically that structure-function relationships at the molecular level could contribute to selective preservation in fossilized vertebrate remains across geological time, suggest a ‘preservation motif’, and bolster current concepts linking collagen structure to biological function. This non-random distribution supports the hypothesis that the peptides are produced by the extinct organisms and suggests a chemical mechanism for survival.
Telephone Follow‐Up for Older Adults Discharged to Home from the Emergency Department: A Pragmatic Randomized Controlled Trial

Telephone calls after discharge from the emergency department (ED) are increasingly used to reduce 30‐day rates of return or readmission, but their effectiveness is not established. The objective was to determine whether a scripted telephone intervention by registered nurses from a hospital‐based call center would decrease 30‐day rates of return to the ED or hospital or of death.
Interactive comment on “Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network” by M. Telszewski

This manuscript presents an estimate of the basin-wide monthly pCO2 distribution from the sparsely measured pCO2 and the basin-wide SST, CHL and MLD maps by using the SOM neural network. The topic is within the scope of Biogeosciences, and the method is of interest to an even wider scope of readers. As a novel and powerful data analysis tool, the SOM is becoming popular in various disciplines. I believe this timely work will have an immediate impact in biogeosciences community.
New findings in pharmacogenetics of schizophrenia

Purpose of review This review highlights recent advances in the investigation of genetic factors for antipsychotic response and side effects. Recent findings Antipsychotics prescribed to treat psychotic symptoms are variable in efficacy and propensity for causing side effects. The major side effects include tardive dyskinesia, antipsychotic-induced weight gain (AIWG), and clozapine-induced agranulocytosis (CIA). Several promising associations of polymorphisms in genes including HSPG2, CNR1, and DPP6 with tardive dyskinesia have been reported. In particular, a functional genetic polymorphism in SLC18A2, which is a target of recently approved tardive dyskinesia medication valbenazine, was associated with tardive dyskinesia. Similarly, several consistent findings primarily from genes modulating energy homeostasis have also been reported (e.g. MC4R, HTR2C). CIA has been consistently associated with polymorphisms in the HLA genes (HLA-DQB1 and HLA-B). The association findings between glutamate system genes and antipsychotic response require additional replications. Summary The findings to date are promising and provide us a better understanding of the development of side effects and response to antipsychotics. However, more comprehensive investigations in large, well characterized samples will bring us closer to clinically actionable findings.
The Effectiveness of Dialectical Behavior Therapy Skills Training Group vs. Cognitive Therapy Group on Reducing Depression and Suicide Attempts for Borderline Personality Disorder in Taiwan

In this study the effectiveness of the condensed Dialectical Behavior Therapy Skills Training Group (DBTSTG) was compared to the Cognitive Therapy Group (CTG) in reducing depression and suicide reattempt and modifying emotion regulation strategies among those with borderline personality disorder (BPD). A total of 82 depressed BPD college students with a suicidal history within the past 6-months were randomly allocated to DBTSTG or CTG. Both groups had similar reductions in suicide reattempts and depression after the intervention and 6-month follow-ups. However, the CTG showed improvements in cognitive errors, but the DBTSTG revealed increases in acceptance and decreases in suppression scores. Both groups were effective in decreasing depression and suicide reattempt in BPD college students, probably through increasing adaptive antecedent-focused or response-focused strategies of emotion regulation, respectively.
CLARIT Experiments in Batch Filtering: Term Selection and Threshold Optimization in IR and SVM Filters

The Clairvoyance team participated in the Filtering Track, submitting two runs in the Batch Filtering category. While we have been exploring the question of both topic modeling and ensemble filter construction (as in our previous TREC filtering experiments [5]), we had one distinct objective this year, to explore the viability of monolithic filters in classification-like tasks. This is appropriate to our work, in part, because monolithic filters are a crucial starting point for ensemble filtering, and it is possible for them to contribute substantially in the ensemble approach. Our primary goal in experiments this year, thus, was to explore two issues in monolithic filter construction: (1) term count selection and (2) filter threshold optimization.
Characterization of chemical contaminants generated by a desktop fused deposition modeling 3-dimensional Printer

ABSTRACT Printing devices are known to emit chemicals into the indoor atmosphere. Understanding factors that influence release of chemical contaminants from printers is necessary to develop effective exposure assessment and control strategies. In this study, a desktop fused deposition modeling (FDM) 3-dimensional (3-D) printer using acrylonitrile butadiene styrene (ABS) or polylactic acid (PLA) filaments and two monochrome laser printers were evaluated in a 0.5 m3 chamber. During printing, chamber air was monitored for vapors using a real-time photoionization detector (results expressed as isobutylene equivalents) to measure total volatile organic compound (TVOC) concentrations, evacuated canisters to identify specific VOCs by off-line gas chromatography-mass spectrometry (GC-MS) analysis, and liquid bubblers to identify carbonyl compounds by GC-MS. Airborne particles were collected on filters for off-line analysis using scanning electron microscopy with an energy dispersive x-ray detector to identify elemental constituents. For 3-D printing, TVOC emission rates were influenced by a printer malfunction, filament type, and to a lesser extent, by filament color; however, rates were not influenced by the number of printer nozzles used or the manufacturer's provided cover. TVOC emission rates were significantly lower for the 3-D printer (49–3552 µg h−1) compared to the laser printers (5782–7735 µg h−1). A total of 14 VOCs were identified during 3-D printing that were not present during laser printing. 3-D printed objects continued to off-gas styrene, indicating potential for continued exposure after the print job is completed. Carbonyl reaction products were likely formed from emissions of the 3-D printer, including 4-oxopentanal. Ultrafine particles generated by the 3-D printer using ABS and a laser printer contained chromium. Consideration of the factors that influenced the release of chemical contaminants (including known and suspected asthmagens such as styrene and 4-oxopentanal) from a FDM 3-D printer should be made when designing exposure assessment and control strategies.
A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.
Injuries in Austrian football players: Are they an issue?

Abstract Purpose and Hypothesis With its load characteristics, the team sport of football places high physical demands on players and thus is associated with a high risk of injury. Therefore, the purpose of this study was to collect information about injuries suffered by football players in Austria during training or matches to enable participating clubs to learn about injuries. Methods One hundred and forty-five players from seven teams with age ranging from 16 to 38 years participated in this prospective study during the 2015/16 season. Injury surveillance was conducted according to the consensus statement of Fuller et al. (2006). Results During the period of study, a total of 83 injuries were recorded, which represents an overall incidence of 4.5 injuries/1000 hours (2.3 injuries/1000 hours in training and 14.2 injuries/1000 hours of match play). There was a significant difference for older age in injured players compared to non-injured players (p = 0.019). No differences in BMI were detected (p = 0.427). Ninety-four percent of all injuries occurred in the lower extremity, with the thigh (25.8 %), ankle (19.3 %) and knee (13.3 %) being the major locations of injuries. Muscle tendon injuries were the most common non-contact injuries (78.9 %). Conclusion Lower extremity injuries are the most common injury in Austrian football. The incidence of muscle injuries is high, especially injuries of the posterior thigh. There are promising preventive strategies for the most common injury types. The implementation of these strategies is essential in order to reduce the incidence or recurrence of these football injuries.
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading

In the last few years, machine learning has become a very popular tool for analyzing ﬁnancial text data, with many promising results in stock price forecasting from ﬁnancial news, a development with implications for the E � - cient Markets Hypothesis (EMH) that underpins much economic theory. In this work, we explore recurrent neural networks with character-level language model pre-training for both in-traday and interday stock market forecasting. In terms of predicting directional changes in the Standard & Poor’s 500 index, both for individual companies and the overall index, we show that this technique is competitive with other state-of-the-art approaches.
Discriminability Objective for Training Descriptive Captions

One property that remains lacking in image captions generated by contemporary methods is discriminability: being able to tell two images apart given the caption for one of them. We propose a way to improve this aspect of caption generation. By incorporating into the captioning training objective a loss component directly related to ability (by a machine) to disambiguate image/caption matches, we obtain systems that produce much more discriminative caption, according to human evaluation. Remarkably, our approach leads to improvement in other aspects of generated captions, reflected by a battery of standard scores such as BLEU, SPICE etc. Our approach is modular and can be applied to a variety of model/loss combinations commonly proposed for image captioning.
Neural Responses to Smoking Stimuli Are Influenced by Smokers' Attitudes towards Their Own Smoking Behaviour

An important feature of addiction is the high drug craving that may promote the continuation of consumption. Environmental stimuli classically conditioned to drug-intake have a strong motivational power for addicts and can elicit craving. However, addicts differ in the attitudes towards their own consumption behavior: some are content with drug taking (consonant users) whereas others are discontent (dissonant users). Such differences may be important for clinical practice because the experience of dissonance might enhance the likelihood to consider treatment. This fMRI study investigated in smokers whether these different attitudes influence subjective and neural responses to smoking stimuli. Based on self-characterization, smokers were divided into consonant and dissonant smokers. These two groups were presented smoking stimuli and neutral stimuli. Former studies have suggested differences in the impact of smoking stimuli depending on the temporal stage of the smoking ritual they are associated with. Therefore, we used stimuli associated with the beginning (BEGIN-smoking-stimuli) and stimuli associated with the terminal stage (END-smoking-stimuli) of the smoking ritual as distinct stimulus categories. Stimulus ratings did not differ between both groups. Brain data showed that BEGIN-smoking-stimuli led to enhanced mesolimbic responses (amygdala, hippocampus, insula) in dissonant compared to consonant smokers. In response to END-smoking-stimuli, dissonant smokers showed reduced mesocortical responses (orbitofrontal cortex, subcallosal cortex) compared to consonant smokers. These results suggest that smoking stimuli with a high incentive value (BEGIN-smoking-stimuli) are more appetitive for dissonant than consonant smokers at least on the neural level. To the contrary, smoking stimuli with low incentive value (END-smoking-stimuli) seem to be less appetitive for dissonant smokers than consonant smokers. These differences might be one reason why dissonant smokers experience difficulties in translating their attitudes into an actual behavior change.
Extracellular matrix remodeling and TGF‐β1/Smad signaling in diabetic colon mucosa

Diabetes is associated with metabolic and functional alterations in the gut. Using an experimental model of streptozotocin (STZ)‐induced diabetes in rodents, we analyzed the extracellular matrix (ECM) and TGF‐β/Smad signaling in the colon mucosa. Male rats were divided into normal control, diabetic and insulin treated diabetic groups during 4 and 9 weeks. Sirius red staining showed marked increase in the extracellular matrix deposition in diabetic mucosa. High levels of fibrillar collagen (I and III) and fibronectin mRNAs were also detected with an imbalance between MMPs/TIMPs activities. Moreover, an increased mesenchymal cell proliferation together with an enhanced expression of myofibroblasts markers vimentin and α‐SMA were observed. TGF‐β/Smad signaling‐related genes were determined using RT‐PCR, Western blotting, and immunohistochemistry. Diabetic rats showed a significant up‐regulation of TGF‐β1, TGF‐β receptors and the effectors p‐Smad2/3 in the mucosa compared with control rats. Insulin treatment attenuated the stimulating effect of diabetes on colon ECM deposition and TGF‐β/Smad signaling. In conclusion, the overall results showed a deregulation of the TGFβ1 pathway associated with the appearance of myofibroblasts and the accumulation of ECM in the mucosa of diabetic colon. These data provide the first in vivo evidence that TGF‐β1/Smad is a key component of intestinal tissue remodeling in diabetes.
Axial Compression Behavior of Fire-Damaged Concrete Cylinders Confined with CFRP Sheets

AbstractThis paper presents the axial compression behavior of standard O150×300 mm concrete cylinders after exposure to elevated temperatures of 300, 500, and 700°C for heating periods of 2 h (for all temperatures) and 3 h (only for 700°C). A total of 192 specimens were tested in static compression to investigate the stress-strain relationships and failure modes of fire-damaged concrete before and after strengthening with carbon fiber–reinforced polymer (CFRP) wraps. The test results showed that the exposure temperature and duration, unconfined concrete strength, and cooling method (air or water cooling) influence the postfire compressive strength, strain at compressive strength, and modulus of elasticity of concrete. Low-strength concrete is more susceptible to the loss in residual properties caused by fire than high-strength concrete. The CFRP wrapping can significantly enhance both strength and ductility of concrete after exposure to elevated temperatures. The level of strength enhancement by CFRP con.
KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.
Strain monitoring of a single-lap joint with embedded fiber-optic distributed sensors

We have developed a fiber-optic distributed sensor which can measure strain distributions along fiber Bragg grating (FBG) with the high spatial resolution. This sensing system is based on optical frequency domain reflectometry and a long-length FBG whose length is about 100 mm can be used. We can identify the longitudinal strain at an arbitrary position along the FBG using signal processing technology. In this study, long-length FBGs were embedded into the adhesive layers of the two single-lap joints and we could successfully measure the strain distributions inside the adhesives. In one single-lap joint, the adherends were carbon fiber reinforced plastics and in another one, they were aluminum. Theadhesive was epoxy in both cases. The measured results were compared with the calculated ones by nonlinear finite element (FE) analysis in which the large displacement and the elasto–plastic response of the adherend or adhesive material were account for. We found that in most of the applied loads, the agreement between the measured results and the calculated ones obtained from an intact FE model is excellent. While the measured strain distributions inside the adhesive layer of the aluminum single-lap joint were varied at the end of the overlap in the higher applied loads and they were much different from those of the intact model, an FE model with debonding was made and it could represent such variations. We could also monitor the strain distributions inside the adhesive during the manufacturing process and we observed the perturbation in residual strain distributions after curing. Consequently, we can say that the fiber-optic distributed sensor with the high spatial resolution is very useful not only to assess the structural integrity of adhesive joints but also to improve numerical analysis techniques and manufacturing processes for them.