rac-1-(Furan-2-ylmeth­yl)-N-nitro-5-(oxolan-2-ylmeth­yl)-1,3,5-triazinan-2-imine

In the title compound C13H19N5O4, which belongs to the insecticidally active neonicotinoid group of compounds, the triazane ring exhibits a half-chair conformation. The large discrepancy between the two nitro O—N—N bond angles [116.1 (2) and 123.98 (19)°] may be attributed to intra­molecular N—H⋯O hydrogen bonding involving one of the nitro O atoms as the acceptor. The delocalization of the electrons extends as far as the nitro group, forming coplanar π-electron networks. In the crystal, inversion dimers lined by pairs of N—H⋯O hydrogen bonds occur

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis

$\textit{Implicit neural representations}$ (INRs) aim to learn a $\textit{continuous function}$ (i.e., a neural network) to represent an image, where the input and output of the function are pixel coordinates and RGB/Gray values, respectively. However, images tend to consist of many objects whose colors are not perfectly consistent, resulting in the challenge that image is actually a $\textit{discontinuous piecewise function}$ and cannot be well estimated by a continuous function. In this paper, we empirically investigate that if a neural network is enforced to fit a discontinuous piecewise function to reach a fixed small error, the time costs will increase exponentially with respect to the boundaries in the spatial domain of the target signal. We name this phenomenon the $\textit{exponential-increase}$ hypothesis. Under the $\textit{exponential-increase}$ hypothesis, learning INRs for images with many objects will converge very slowly. To address this issue, we first prove that partitioning a complex signal into several sub-regions and utilizing piecewise INRs to fit that signal can significantly speed up the convergence. Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs. In both cases, we partition an image into different sub-regions and dedicate smaller networks for each part. In addition, we further propose two partition rules based on regular grids and semantic segmentation maps, respectively. Extensive experiments validate the effectiveness of the proposed partitioning methods in terms of learning INR for a single image (ordinary learning framework) and the learning-to-learn framework.Comment: Proceedings of the IEEE/CVF International Conference on Computer Vision. 202

The impact of intraarterial, intravenous, and combined tirofiban on endovascular treatment for acute intracranial atherosclerotic occlusion

Background and purposeAdjunctive tirofiban administration in patients undergoing endovascular treatment (EVT) for acute large vessel occlusion (LVO) has been investigated in several studies. However, the findings are conflict. This study aimed to compare the effect of different administration pathways of tirofiban on patients undergoing EVT for acute LVO with intracranial atherosclerotic disease (ICAD).MethodsPatients were selected from the ANGEL-ACT Registry (Endovascular Treatment Key Technique and Emergency Workflow Improvement of Acute Ischemic Stroke: A Prospective Multicenter Registry Study) and divided into four groups: intra-arterial (IA), intravenous (IV), and intra-arterial plus intravenous (IA+IV) and non-tirofiban. The primary outcome was 90-day ordinal modified Rankin Scale (mRS) score, and the secondary outcomes included the rates of mRS 0–1, 0–2, and 0–3 at 90-day, successful recanalization. The safety outcomes were symptomatic intracranial hemorrhage (sICH) and other safety endpoints. The multivariable logistic regression models adjusting for potential baseline confounders were performed to compare the outcomes. A propensity score matching (PSM) with a 1:1:1:1 ratio was conducted among four groups, and the outcomes were then compared in the post-matched population.ResultsA total of 502 patients were included, 80 of which were in the IA-tirofiban group, 73 in IV-tirofiban, 181 in (IA+IV)-tirofiban group, and 168 in the non-tirofiban group. The median (IQR) 90-day mRS score in the four groups of IA, IV, IA+IV, and non-tirofiban was, respectively 3(0–5) vs. 1(0–4) vs. 1(0–4) vs. 3(0–5). The adjusted common odds ratio (OR) for 90-day ordinal modified Rankin Scale distribution with IA-tirofiban vs. non-tirofiban was 0.77 (95% CI, 0.45–1.30, P = 0.330), with IV-tirofiban vs. non-tirofiban was 1.36 (95% CI, 0.78–2.36, P = 0.276), and with (IA+IV)-tirofiban vs. non-tirofiban was 1.03 (95% CI, 0.64–1.64, P = 0.912). The adjusted OR for mRS 0–1 and mRS 0–2 at 90-day with IA-tirofiban vs. non-tirofiban was, respectively 0.51 (95% CI, 0.27–0.98, P = 0.042) and 0.50 (95% CI, 0.26–0.94, P = 0.033). The other outcomes of each group were similar with non-tirofiban group, all P was >0.05. After PSM, the common odds ratio (OR) for 90-day ordinal modified Rankin Scale distribution with IA-tirofiban vs. non-tirofiban was 0.41 (95% CI, 0.18–0.94, P = 0.036), and the OR for mRS 0–1 and mRS 0–2 at 90-day with IA-tirofiban vs. non-tirofiban was, respectively 0.28 (95% CI, 0.11–0.74, P = 0.011) and 0.25 (95% CI, 0.09–0.67, P = 0.006).ConclusionsIntra-arterial administration of tirofiban was associated with worse outcome than non-tirofiban, which suggested that intra-arterial tirofiban had a harmful effect on patients undergoing EVT for ICAD-LVO.Clinical trial registrationhttp://www.clinicaltrials.gov, Unique identifier: NCT03370939

Proton-Boron Fusion Yield Increased by Orders of Magnitude with Foam Targets

A novel intense beam-driven scheme for high yield of the tri-alpha reaction 11B(p,{\alpha})2{\alpha} was investigated. We used a foam target made of cellulose triacetate (TAC, C_9H_{16}O_8) doped with boron. It was then heated volumetrically by soft X-ray radiation from a laser heated hohlraum and turned into a homogenous, and long living plasma. We employed a picosecond laser pulse to generate a high-intensity energetic proton beam via the well-known Target Normal Sheath Acceleration (TNSA) mechanism. We observed up to 10^{10}/sr {\alpha} particles per laser shot. This constitutes presently the highest yield value normalized to the laser energy on target. The measured fusion yield per proton exceeds the classical expectation of beam-target reactions by up to four orders of magnitude under high proton intensities. This enhancement is attributed to the strong electric fields and nonequilibrium thermonuclear fusion reactions as a result of the new method. Our approach shows opportunities to pursue ignition of aneutronic fusion

Effects of a parental program for preventing underage drinking - The NGO program strong and clear

<p>Abstract</p> <p>Background</p> <p>The present study is an evaluation of a 3-year parental program aiming to prevent underage drinking. The intervention was implemented by a non-governmental organization and targeted parents with children aged 13-16 years old and included recurrent activities during the entire period of secondary school. The program consisted of four different types of group and self-administered activities: parent meetings, family dialogues, friend meetings, and family meetings.</p> <p>Methods</p> <p>A quasi-experimental design was used following parents and children with questionnaires during the three years of secondary school. The analytic sample consisted of 509 dyads of parents and children. Measures of parental attitudes and behaviour concerning underage drinking and adolescents' lifetime alcohol consumption and drunkenness were used. Three socio-demographic factors were included: parental education, school, and gender of the child. A Latent Growth Modelling (LGM) approach was used to examine changes in parental behaviour regarding youth drinking and in young people's drinking behaviour. To test for the pre-post test differences in parental attitudes repeated measures ANOVA were used.</p> <p>Results</p> <p>The results showed that parents in the program maintained their restrictive attitude toward underage drinking to a higher degree than non-participating parents. Adolescents of participants were on average one year older than adolescents with non-participating parents when they made their alcohol debut. They were also less likely to have ever been drunk in school year 9.</p> <p>Conclusion</p> <p>The results of the study suggested that Strong and Clear contributed to maintaining parents' restrictive attitude toward underage drinking during secondary school, postponing alcohol debut among the adolescents, and significantly reducing their drunkenness.</p

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001