Miracle-dz/UniFAR-FTUs huggingface dataset:
Observing Gravitational Waves from Core-Collapse Supernovae in the Advanced Detector Era

The next galactic core-collapse supernova (CCSN) has already exploded, and its electromagnetic (EM) waves, neutrinos, and gravitational waves (GWs) may arrive at any moment. We present an extensive study on the potential sensitivity of prospective detection scenarios for GWs from CCSNe within 5 Mpc, using realistic noise at the predicted sensitivity of the Advanced LIGO and Advanced Virgo detectors for 2015, 2017, and 2019. We quantify the detectability of GWs from CCSNe within the Milky Way and Large Magellanic Cloud, for which there will be an observed neutrino burst. We also consider extreme GW emission scenarios for more distant CCSNe with an associated EM signature. We find that a three-detector network at design sensitivity will be able to detect neutrino-driven CCSN explosions out to ∼5.5 kpc, while rapidly rotating core collapse will be detectable out to the Large Magellanic Cloud at 50 kpc. Of the phenomenological models for extreme GW emission scenarios considered in this study, such as long-lived bar-mode instabilities and disk fragmentation instabilities, all models considered will be detectable out to M31 at 0.77 Mpc, while the most extreme models will be detectable out to M82 at 3.52 Mpc and beyond.
Embodied artificial intelligence through distributed adaptive control: An integrated framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances in the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building on this analysis, we first propose an embodied cognitive architecture integrating heterogeneous subfields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.
Interactions between Plasma Membrane Aquaporins Modulate Their Water Channel Activity Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.017194.

Plant plasma membrane intrinsic proteins (PIPs) cluster in two evolutionary subgroups, PIP1 and PIP2, with different aquaporin activities when expressed in Xenopus oocytes. Maize ZmPIP1;1 and ZmPIP1;2 do not increase the osmotic water permeability coefficient (Pf), whereas ZmPIP2;1, ZmPIP2;4, and ZmPIP2;5 do. Here, we show that coexpression of the nonfunctional ZmPIP1;2 and the functional ZmPIP2;1, ZmPIP2;4, or ZmPIP2;5 resulted in an increase in Pf that was dependent on the amount of injected ZmPIP1;2 complementary RNA. Confocal analysis of oocytes expressing ZmPIP1;2–green fluorescent protein (GFP) alone or ZmPIP1;2-GFP plus ZmPIP2;5 showed that the amount of ZmPIP1;2-GFP present in the plasma membrane was significantly greater in coexpressing cells. Nickel affinity chromatography purification of ZmPIP2;1 fused to a His tag coeluted with ZmPIP1;2-GFP demonstrated physical interaction and heteromerization of both isoforms. Interestingly, coexpression of ZmPIP1;1 and ZmPIP2;5 did not result in a greater increase in Pf than did the expression of ZmPIP2;5 alone, but coexpression of the ZmPIP1;1 and ZmPIP1;2 isoforms induced a Pf increase, indicating that PIP1 isoform heteromerization is required for both of them to act as functional water channels. Mutational analysis demonstrated the important role of the C-terminal part of loop E in PIP interaction and water channel activity induction. This study has revealed a new mechanism of plant aquaporin regulation that might be important in plant water relations.
Point processes for bearing fault detection under non-stationary operating conditions

Bearing faults represent the most frequent mechanical faults in rotational machines. They are characterized by repetitive impacts between the rolling elements and the damaged surface. The time intervals between two impacts are directly related with the type and location of the surface fault. These time intervals can be elegantly analyzed within the framework of renewal point processes. With such an approach the fault detection and identification can be performed irrespective of the variability of rotational speed. Furthermore, we show that by analyzing the entropy of the underlying counting process by means of wavelet transform, one can perform fault detection and identification without any information about the operating conditions. The effectiveness of the approach is shown on a data-set acquired from a two–stage gearbox with various bearing faults operating under different rotational speeds and loads.
A 17ps time-to-digital converter implemented in 65nm FPGA technology

This paper presents a new architecture for time-to-digital conversion enabling a time resolution of 17ps over a range of 50ns with a conversion rate of 20MS/s. The proposed architecture, implemented in a 65nm FPGA system, consists of a pipelined interpolating time-to-digital converter (TDC). The TDC comprises a coarse time discriminator and a fine delay line, capable of sustained operation at a clock frequency of 300MHz. A Turbo version of the circuit implements a pipelined interpolating TDC with suppressed dead time to reach a conversion rate of 300MS/s at the expense of a systematic asymmetry that requires fast error correction. The TDCs proposed in this paper can be compensated for process, voltage, and temperature (PVT) variations using a conventional charge pump based feedback or a digital calibration technique. Results demonstrate the suitability of the approach for a variety of applications involving high-precision ultra-fast time discrimination, such as optical lifetime sensing, time-of-flight cameras, high throughput comlinks, RADARs, etc.
Social and ecological factors influencing offspring survival in wild macaques

Lay summary Better to live in a big group if you want your offspring to survive! Using a multivariate approach, we show how the interplay of ecological and social factors influences fetus and infant survival in wild crested macaques. Offspring are more likely to survive in bigger groups, but seasonality also influences their survival. Fetus survival is higher for higher ranking mothers, whereas the main determinant of infants’ death is an alpha-male takeover by an immigrant male.
Dynamic Pricing Competition with Strategic Customers Under Vertical Product Differentiation

We consider dynamic pricing competition between two firms offering vertically differentiated products to strategic customers who are intertemporal utility maximizers. We show that price skimming arises as the unique pure-strategy Markov perfect equilibrium in the game under a simple condition. Our results highlight the asymmetric effect of strategic customer behavior on quality-differentiated firms. Even though the profit of either firm decreases as customers become more strategic, the low-quality firm suffers substantially more than the high-quality firm. Furthermore, we show that unilateral commitment to static pricing by either firm generally improves profits of both firms. Interestingly, both firms enjoy higher profit lifts when the high-quality firm commits rather than when the low-quality firm commits. This paper was accepted by Yossi Aviv, operations management.
Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting

Frontal-midline (fm) theta oscillations as measured via the electroencephalogram (EEG) have been suggested as neural “working language” of executive functioning. Their power has been shown to increase when cognitive processing or task performance is enhanced. Thus, the question arises whether learning to increase fm-theta amplitudes would functionally impact the behavioral performance in tasks probing executive functions (EFs). Here, the effects of neurofeedback (NF), a learning method to self-up-regulate fm-theta over fm electrodes, on the four most representative EFs, memory updating, set shifting, conflict monitoring, and motor inhibition are presented. Before beginning and after completing an individualized, eight-session gap-spaced NF intervention, the three-back, letter/number task-switching, Stroop, and stop-signal tasks were tested while measuring the EEG. Self-determined up-regulation of fm-theta and its putative role for executive functioning were compared to an active control group, the so-called pseudo-neurofeedback group. Task-related fm-theta activity after training differed significantly between groups. More importantly, though, after NF significantly enhanced behavioral performance was observed. The training group showed higher accuracy scores in the three-back task and reduced mixing and shifting costs in letter/number task-switching. However, this specific protocol type did not affect performance in tasks probing conflict monitoring and motor inhibition. Thus, our results suggest a modulation of proactive but not reactive mechanisms of cognitive control. Furthermore, task-related EEG changes show a distinct pattern for fm-theta after training between the NF and the pseudo-neurofeedback group, which indicates that NF training indeed tackles EFs-networks. In sum, the modulation of fm-theta via NF may serve as potent treatment approach for executive dysfunctions.
Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests

This paper presents the first semi-supervised transductive algorithm for real-time articulated hand pose estimation. Noisy data and occlusions are the major challenges of articulated hand pose estimation. In addition, the discrepancies among realistic and synthetic pose data undermine the performances of existing approaches that use synthetic data extensively in training. We therefore propose the Semi-supervised Transductive Regression (STR) forest which learns the relationship between a small, sparsely labelled realistic dataset and a large synthetic dataset. We also design a novel data-driven, pseudo-kinematic technique to refine noisy or occluded joints. Our contributions include: (i) capturing the benefits of both realistic and synthetic data via transductive learning, (ii) showing accuracies can be improved by considering unlabelled data, and (iii) introducing a pseudo-kinematic technique to refine articulations efficiently. Experimental results show not only the promising performance of our method with respect to noise and occlusions, but also its superiority over state-of-the-arts in accuracy, robustness and speed.
A new mobile‐immobile model for reactive solute transport with scale‐dependent dispersion

This study proposed a new mobile‐immobile model (MIM) to describe reactive solute transport with scale‐dependent dispersion in heterogeneous porous media. The model was derived from the conventional MIM but assumed the dispersivity to be a linear or exponential function of travel distance. The linear adsorption and the first‐order degradation of solute were also considered in the model. The Laplace transform technique and the de Hoog numerical Laplace inversion method were applied to solve the developed model. Solute breakthrough curves (BTCs) obtained from MIM with scale‐dependent and constant dispersions were compared, and a constant effective dispersivity was provided to reflect the lumped scale‐dependent dispersion effect. The effective dispersivity was calculated by arithmetically averaging the distance‐dependent dispersivity. With this effective dispersivity, MIM could produce similar BTC as that from MIM with scale‐dependent dispersion in porous media with moderate heterogeneity. The applicability of the proposed new model was tested with concentration data from a 1,250‐cm long and highly heterogeneous soil column. The simulation results indicated that MIM with constant and linear distance‐dependent dispersivities were unable to adequately describe the measured BTCs in the column, while MIM with exponential distance‐dependent dispersivity satisfactorily captured the evolution of BTCs.
DSIbin: Identifying dynamic data structures in C/C++ binaries

Reverse engineering binary code is notoriously difficult and, especially, understanding a binary's dynamic data structures. Existing data structure analyzers are limited wrt. program comprehension: they do not detect complex structures such as skip lists, or lists running through nodes of different types such as in the Linux kernel's cyclic doubly-linked list. They also do not reveal complex parent-child relationships between structures. The tool DSI remedies these shortcomings but requires source code, where type information on heap nodes is available. We present DSIbin, a combination of DSI and the type excavator Howard for the inspection of C/C++ binaries. While a naive combination already improves upon related work, its precision is limited because Howard's inferred types are often too coarse. To address this we auto-generate candidates of refined types based on speculative nested-struct detection and type merging; the plausibility of these hypotheses is then validated by DSI. We demonstrate via benchmarking that DSIbin detects data structures with high precision.
Development of a Size Exclusion Chromatography metod for analysis of extraction solutions from urinary catheters

This project focused on developing a Size Exclusion Chromatography (SEC) methodwith Refractive Index (RI) detection for analysis of extraction samples from urinarycatheters to detect compounds that can be extracted from the catheter during use.Mobile phases, extraction fluids and sample concentrations were varied, as well aspore sizes of the columns, to investigate the applicability of this technique forcharacterization of the coating and potential leachables. Analyses of extractionsamples showed that this method can be used for analyses of polyvinylpyrrolidone(PVP), which is the main component in the coating, giving rise to the specialcharacteristics of the coating. No compounds could be detected from extraction ofuncoated catheters.Comparisons were made between different raw catheter materials, PVC and POBE,and differences in molecular weight distribution of the extracted PVP compoundswere seen, in spite of identical coating processes, indicating that the bonding of PVP inthe coating depends on raw catheter material. Furthermore, radiation effects alsodiffered, where a larger decrease in high-molecular weight fractions of PVP was seenwith increasing radiation dose from extracted PVC catheters, compared to thosefrom POBE. Analyses of radiated PVP powder showed opposite radiation effects thanthose from radiated catheters; that is, a steady increase in molecular weight withincreasing radiation dose, indicating cross-linking of PVP when radiated in powderform, and consequently chain-scission of high-molecular PVP fractions when radiatedbonded to the catheter coating.The SEC-RI method was concluded to be a useful tool for qualitative analyses of thedifferences in molecular weight distribution of PVP from the different extractionsamples, but showed low reproducibility in molecular weight calculations and therelative molecular weights calculated from these analyses differed significantly fromtrue molecular weights. This method could therefore not be used to give a goodestimation of true molecular weights.
Internal workload and non-contact injury: a one-season study of five teams from the UEFA Elite Club Injury Study

Background Internal workload (ie, from training and matches) is considered one of the most important injury risk factors for elite European football teams, however there is little published evidence to support this belief. Objective We examined the association and predictive power of internal workload and non-contact injuries. Methods Five elite European teams, 171 players (age: 25.1±4.9 years; height: 181.6±6.7 cm; body mass: 77.5±7.2 kg) participated over one full competitive season. Using the session-rating of perceived exertion (s-RPE) method player’s internal workloads were calculated for acute week, week-to-week changes, cumulated weeks, chronic weeks and acute:chronic ratios and analysed for association with non-contact injury (using generalised estimating equations (GEE)). Associated variables from GEE analysis were categorised into very low to very high workload zones and checked for increased relative risks (RRs). Associated workload variables were also analysed for predictive power (receiver operating characteristics). Results Acute:chronic workload ratios at 1:3 and 1:4 weeks were associated with non-contact injury (P<0.05). Specifically, a greater risk of injury was found for players with an acute:chronic workload at 1:4 weeks of 0.97 to 1.38 (RR 1.68; 95% CI 1.02 to 2.78, likely harmful) and >1.38 (RR 2.13; 95% CI 1.21 to 3.77, very likely harmful) compared with players whose acute:chronic workload was 0.60 to 0.97. An acute:chronic workload 1:3 of >1.42 compared with 0.59 to 0.97 displayed a 1.94 times higher risk of injury (RR 1.90; 95% CI 1.08 to 3.36, very likely harmful). Importantly, acute:chronic workload at both 1:4 and 1:3 showed poor predictive power (area under the curve 0.53 to 0.58) despite previous reports and beliefs that it can predict injury. Conclusions This study provides evidence for the acute:chronic internal workload (measured using s-RPE) as a risk factor for non-contact injury in elite European footballers. However the acute:chronic workload, in isolation, should not be used to predict non-contact injury.
An Improved Multifunctional Active Frequency Selective Surface

An improved dual-polarized multifunctional active frequency selective surface (MAFSS) with parallel-feed configuration is proposed in this paper. The designed structure is composed of two metallic layers separated by a thin dielectric layer. Three independent and controllable functions of electromagnetic switching, polarization selection, and frequency tuning are integrated together into the proposed structure via introducing PIN diodes and varactor diodes. To reduce the negative effects of extra feed lines, frequency selective surface (FSS) elements themselves play a significant role in connecting the active components and allowing the direct currents to flow through. A special biasing configuration is designed to provide parallel bias voltages for the two different types of active devices. Finally, an FSS prototype is fabricated and measured, and the measurement results as well as the simulation results both demonstrate great performance of the MAFSS structure. In addition, the proposed structure also performs well under circumstances of oblique incidence and different polarizations.
Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme

In this paper, we propose an automatic and effective cloud detection algorithm for color aerial photographs. Based on the properties derived from observations and statistical results on a large number of color aerial photographs with cloud layers, we present a novel progressive refinement scheme for detecting clouds in the color aerial photographs. We first construct a significance map which highlights the difference between cloud regions and noncloud regions. Based on the significance map and the proposed optimal threshold setting, we obtain a coarse cloud detection result which classifies the input aerial photograph into the candidate cloud regions and noncloud regions. In order to accurately detect the cloud regions from the candidate cloud regions, we then construct a robust detail map derived from a multiscale bilateral decomposition to guide us in removing noncloud regions from the candidate cloud regions. Finally, we further perform a guided feathering to achieve our final cloud detection result, which detects semitransparent cloud pixels around the boundaries of cloud regions. The proposed method is evaluated in terms of both visual and quantitative comparisons, and the evaluation results show that our proposed method works well for the cloud detection of color aerial photographs.
Study of Mechanical, Tribological and Thermal Stability Properties of TiAIN Coatings with Low Aluminum Content

Ti-Al-N coatings have been widely studied especially those with high aluminum content for different industrial applications because they have high wear resistance and good thermal stability. For the present work TiAlN thin films were prepared by the simultaneous laser ablation of Ti and Al targets in a reactive atmosphere containing nitrogen. The structural characterization of the deposits was carried out using X-ray diffraction and it was found that the films were highly oriented in the (111) direction of TiN. The maximum coating hardness was 43 GPa for an aluminum concentration close to 11 at. %, whilst the coatings commonly used in industry with an aluminum content of about 25 at. % or greater, have hardness values between 25 and 30 GPa. The thermal stability of the films was studied by micro-Raman spectroscopy in the temperature range from 500 to 900 C. This technique was used to detect the formation of the TiO2 phase. The results indicated that the inclusion of relatively low aluminum contents up to 15 at. % led to an improved thermal stability up to 900 C.
The prevalence of activated protein C (APC) resistance and factor V Leiden is significantly higher in patients with retinal vein occlusion without general risk factors

Summary Several small case-control studies have investigated whether factor V Leiden (FVL) is a risk factor for retinal vein occlusion (RVO) and generated conflicting data. To clarify this question we performed a large two-centre case-control study and a meta-analysis of published studies. Two hundred seven consecutive patients with RVO and a control group of 150 subjects were screened between 1996 and 2006. A systematic meta-analysis was done combining our study with further 17 published European case-control studies. APC resistance was detected in 16 out of 207 (7.7%) patients and eight out of 150 (5.3%) controls. The odds ratio (OR) estimated was 1.49 with a (non-significant) 95% confidence interval (CI) of 0.62–3.57. The meta-analysis including 18 studies with a total of 1,748 patients and 2,716 controls showed a significantly higher prevalence of FVL in patients with RVO compared to healthy controls (combined OR 1.66; 95% CI 1.19–2.32). All single studies combined in the meta-analysis were too small to reliably detect the effect individually. This explains the seemingly contradictory data in the literature. In conclusion, the prevalence of APC resistance (and FVL) is increased in patients with RVO compared to controls, but the effect is only moderate. Therefore, there is no indication for general screening of factor V mutation in all patients with RVO. We recommend this test to be performed in patients older than 50 years with an additional history of thromboembolic event and in younger patients without general risk factors like hypertension.