Miracle-dz/UniFAR-FTUs huggingface dataset:
Scene Text Recognition with Permuted Autoregressive Sequence Models

Context-aware STR methods typically use internal autoregressive (AR) language models (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM. The conditional independence of the external LM on the input image may cause it to erroneously rectify correct predictions, leading to significant inefficiencies. Our method, PARSeq, learns an ensemble of internal AR LMs with shared weights using Permutation Language Modeling. It unifies context-free non-AR and context-aware AR inference, and iterative refinement using bidirectional context. Using synthetic training data, PARSeq achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and more challenging datasets. It establishes new SOTA results (96.0% accuracy) when trained on real data. PARSeq is optimal on accuracy vs parameter count, FLOPS, and latency because of its simple, unified structure and parallel token processing. Due to its extensive use of attention, it is robust on arbitrarily-oriented text which is common in real-world images. Code, pretrained weights, and data are available at: https://github.com/baudm/parseq.
Prospective Evaluation of Hemostatic Predictors of Subacute Stent Thrombosis After Coronary Palmaz-Schatz Stenting

Objectives. This study sought to investigate hemostatic predictors of subacute occlusive coronary stent thrombosis. Background. Better hemostatic monitoring may improve antithrombotic therapy after stenting. Methods. In 140 consecutive patients undergoing PalmazSchatz stent implantation for suboptimal angioplasty results, we obtained serial blood samples immediately before and daily for 12 days after stenting. We prospectively tested the hypothesis that subacute stent thrombosis was more frequent if the surface expression of the inducible fibrinogen receptor on platelets (flow cytometry) or the concentration of plasma fibrinogen or that of the prothrombin fragments Fl+ 2 before stent implantation exceeded the 75th percentile of the entire study cohort. Results. All five stent occlusions encountered during the study occurred in patients with platelet fibrinogen receptor expression above the 75th percentile. Thus, the rate of stent occlusion differed significantly between the groups defined by platelet fibrinogen receptor expression (14.3% vs. 0%, p = 0.0008). In both the group with fibrinogen concentration and that with F1+ 2 concentration above the 75th percentile, three stent occlusions occurred. Between the groups defined by these variables, the rate of stent occlusion did not differ significantly (8.6% vs. 1.9%, p = 0°10). Logistic regression analysis, including angiographic and hemostatic variables, confirmed platelet fibrinogen receptor expression as an independent predictor of stent occlusion (p = 0.020). Stent occlusion could not be predicted by the time course of any of the hemostatic variables. Conclusions. Platelet fibrinogen receptor expression is an independent predictor of subacute stent occlusion. However, fibrinogen and F 1 +z concentrations do not show a strong relation to the risk of stent occlusion. (JAm Coil Cardiol 1996;27:15-21)
Optimal Activation of Intrusion Detection Agents for Wireless Sensor Networks

—Recent technological advancements and low price of deployment and maintenance of wireless sensor networks (WSNs) allow their use in numerous applications in industry, research, and commerce, in order to gather environmental data in an unattended manner. Since WSNs usually function in open environments, they may become a target of attacks or malicious activities aiming to gain access to data, manipulate aggregation result, or disrupt the network service. Therefore, intrusion detection becomes crucial for WSNs as a second line of defense. In order to detect “smart” attacks of colluding devices, active monitoring of behaviors of neighboring nodes was proposed, but it is too energy expensive for resource-constrained WSN nodes, making an adaptive technique for activation of intrusion detection system (IDS) agents extremely important. This paper proposed a model for optimal activation of IDS agents for WSNs on the basis of the Ising model.
GreyConE: Greybox Fuzzing + Concolic Execution Guided Test Generation for High Level Designs

Exhaustive testing of high-level designs poses an arduous challenge due to complex branching conditions, loop structures, and the inherent concurrency of hardware designs. Test engineers aim to generate quality test cases satisfying various code coverage metrics to ensure minimal presence of bugs in a design. Prior works in testing SystemC designs are time inefficient which obstructs achieving the desired coverage in a shorter time-span. We interleave greybox fuzzing and concolic execution in a systematic manner and generate quality test cases for accelerating test coverage metrics. Our results outperform state-of-the-art methods in terms of number of test cases and branch-coverage for some of the benchmarks, and runtime for most of them.
Distributed Control for a Multiagent System to Pass Through a Connected Quadrangle Virtual Tube

In order to guide the multiagent system in a cluttered environment, a connected quadrangle virtual tube is designed for all agents to keep moving within it, whose basis is called the single trapezoid virtual tube. There is no obstacle inside the tube, namely, the area inside the tube can be seen as a safety zone. Then, a distributed swarm controller is proposed for the single trapezoid virtual tube passing-through control problem. This issue is resolved by a gradient vector field method with no local minima. Formal analyses and proofs are made to show that all agents are able to pass through the single trapezoid virtual tube. Finally, a modified controller is put forward for convenience in practical use. For the connected quadrangle virtual tube, a modified switching logic is proposed to avoid the deadlock and prevent agents from moving outside the virtual tube. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning techniques allow uncertainty estimation, training them with large-scale datasets is an expensive process that does not always yield models competitive with non-Bayesian counterparts. Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates. To address these issues, we propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation. BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset, enhancing pretrained non-Bayesian computer vision models by providing calibrated uncertainty estimates for the predictions without (i) hampering the performance of the model and (ii) the need for expensive retraining the model from scratch. The proposed method is agnostic to various architectures and tasks. We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures, including image super-resolution, deblurring, inpainting, and crucial application such as medical image translation. Moreover, we apply the derived uncertainty estimates to detect out-of-distribution samples in critical scenarios like depth estimation in autonomous driving. Code is available at https://github.com/ExplainableML/BayesCap . We first provide additional baselines for the ablation study showing the im-portance of the identity mapping. Then, we discuss the experimental details for the out-of-distribution analysis presented in the main paper, The code for the BayesCap is available at https://github.com/ExplainableML/BayesCap .
Machine learning for galactic archaeology: A chemistry-based neural network method for identification of accreted disc stars

We develop a method (’Galactic Archaeology Neural Network’, GANN) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the Auriga Project. We train the network on the target galaxy’s own local environment defined by the stellar halo and the surviving satellites. We demonstrate that this approach allows the detection of accreted stars that are spatially mixed into the disc. Two performance measures are defined —recovery fraction of accreted stars, frecov and the probability that a star with a positive (accreted) classification is a true-positive result, P(TP). As the NNM output is akin to an assigned probability (Pa), we are able to determine positivity based on flexible threshold values that can be adjusted easily to refine the selection of presumed-accreted stars. We find that GANN identifies accreted disc stars within simulated galaxies, with high frecov and/or high P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are over 50 per cent recovered by our NNMs in the majority (18/24) of cases. Additionally, nearly every individual source of accreted stars is detected at 10 per cent or more of its peak stellar mass in the disc. We also demonstrate that a conglomerated NNM, trained on the halo and satellite stars from all of the Auriga galaxies provides the most consistent results, and could prove to be an intriguing future approach as our observational capabilities expand.
Point-to-Box Network for Accurate Object Detection via Single Point Supervision

Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large. In this paper, we attribute such a large performance gap to the failure of generating high-quality proposal bags which are crucial for multiple instance learning (MIL). To address this problem, we introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box Network (P2BNet), which can construct an inter-objects balanced proposal bag by generating proposals in an anchor-like way. By fully investigating the accurate position information, P2BNet further constructs an instance-level bag, avoiding the mixture of multiple objects. Finally, a coarse-to-fine policy in a cascade fashion is utilized to improve the IoU between proposals and ground-truth (GT). Benefiting from these strategies, P2BNet is able to produce high-quality instance-level bags for object detection. P2BNet improves the mean average precision (AP) by more than 50% relative to the previous best PSOD method on the MS COCO dataset. It also demonstrates the great potential to bridge the performance gap between point supervised and bounding-box supervised detectors. The code will be released at github.com/ucas-vg/P2BNet.
FFTc: An MLIR Dialect for Developing HPC Fast Fourier Transform Libraries

Discrete Fourier Transform (DFT) libraries are one of the most critical software components for scientific computing. Inspired by FFTW, a widely used library for DFT HPC calculations, we apply compiler technologies for the development of HPC Fourier transform libraries. In this work, we introduce FFTc, a domain-specific language, based on Multi-Level Intermediate Representation (MLIR), for expressing Fourier Transform algorithms. We present the initial design, implementation, and preliminary results of FFTc.
Unscrambling the Lensed Galaxies in JWST Images behind SMACS 0723

The first deep field images from the James Webb Space Telescope (JWST) of the galaxy cluster SMACS J0723.3-7327 reveal a wealth of new lensed images at uncharted infrared wavelengths, with unprecedented depth and resolution. Here we securely identify 14 new sets of multiply imaged galaxies totaling 42 images, adding to the five sets of bright and multiply imaged galaxies already known from Hubble Space Telescope data. We find examples of arcs crossing critical curves, allowing detailed community follow-up, such as JWST spectroscopy for precise redshift determinations, and measurements of the chemical abundances and of the detailed internal gas dynamics of very distant, young galaxies. One such arc contains a pair of compact knots that are magnified by a factor of hundreds, and features a microlensed transient. We also detect an Einstein cross candidate only visible thanks to JWST’s superb resolution. Our parametric lens model is available through the following link (https://www.dropbox.com/sh/gwup2lvks0jsqe5/AAC2RRSKce0aX-lIFCc9vhBXa? dl=0) and will be regularly updated using additional spectroscopic redshifts. The model is constrained by 16 of these sets of multiply imaged galaxies, three of which have spectroscopic redshifts, and reproduces the multiple images to better than an rms of 0. ″5, allowing for accurate magnification estimates of high-redshift galaxies. The intracluster light extends beyond the cluster members, exhibiting large-scale features that suggest a significant past dynamical disturbance. This work represents a first taste of the enhanced power JWST will have for lensing-related science.
Conservation Laws and Quantum Error Correction: Toward a Generalized Matching Decoder

Decoding algorithms are essential to fault-tolerant quantum-computing architectures. In this perspective we explore decoding algorithms for the surface code; a prototypical quantum low-density parity-check code that underlies many of the leading efforts to demonstrate scalable quantum computing. Central to our discussion is the minimum-weight perfect-matching decoder. The decoder works by exploiting underlying structure that arises due to materialized symmetries among surface-code stabilizer elements. By concentrating on these symmetries, we begin to address the question of how a minimum-weight perfect-matching decoder might be generalized for other families of codes. We approach this question first by investigating examples of matching decoders for other codes. These include decoding algorithms that have been specialized to correct for noise models that demonstrate a particular structure or bias with respect to certain codes. In addition to this, we propose a systematic way of constructing a minimum-weight perfect-matching decoder for codes with certain characteristic properties. The properties we make use of are common among topological codes. We discuss the broader applicability of the proposal, and we suggest some questions we can address that may show us how to design a generalized matching decoder for arbitrary stabilizer codes.
Thermal stability and optoelectronic behavior of polyaniline/GNP (graphene nanoplatelets) nanocomposites

Polyaniline and graphene nanoplatelets (PANI-GNP) nanocomposites are synthesized by in situ oxidative polymerization of polyaniline using an oxidizing agent, ammonium peroxy-disulphate (APS). The mass of GNP in the nanocomposites varied by 5, 10, and 15 wt% compared to PANI. The synthesized polyaniline nanocomposites are chemically characterized and using Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, Scanning electron microscopy (SEM), UV-Vis spectroscopy, and X-ray diffraction analysis (XRD). FTIR and Raman spectroscopy analysis confirmed the uniform coating of polyaniline on GNP. The SEM micrograph and XRD pattern demonstrate the polymerization quality and crystallization degree of samples. UV-Vis analysis showed a decrease in the bandgap of polyaniline, which confirms that nanocomposites are more suitable for optoelectronic application because of variation in the bandgap. TGA analysis showed the thermal stability of PANI is increased with the increased mass of GNP. This study suggests the potential of GNP as a filler for efficient modification in the morphological, electrical, optical, and thermal properties of PANI.
Association Between IL-4 Polymorphisms and Risk of Liver Disease An Updated Meta-Analysis

: Interleukin-4 (IL-4) polymorphisms have been reported to inﬂuence an individual’s susceptibility to liver disease as it is a central anti-inﬂammatory Th2 cytokine; however, these results remain controversial. A comprehensive meta-analysis of the relevant literature was thus performed to better estimate the relationship between IL-4 polymorph- isms and liver disease. Systematic searches of various databases (PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure) for studies published before July 5, 2015 were performed. Odds ratios (ORs) with 95% conﬁdence intervals (CIs) calculated in ﬁxed or random-effects models were used to estimate the strength of the association. Subgroup analyses, meta-regression, Galbraith plots, and sensitivity analyses were also performed. A total of 16 case–control studies, of which 15 involved the -590C/T polymorphismand3involvedthe-33T/Cpolymorphism,wereincludedin the study. With respect to the -590C/T polymorphism, a signiﬁcantly increased risk of liver diseases was found in the overall population (TT þ CT vs CC: OR ¼ 1.25, 95% CI ¼ 1.06–1.49, P ¼ 0.009 and CT vs CC: OR ¼ 1.22, 95% CI ¼ 1.00–1.48, P ¼ 0.048) and the Asian popu- lation (TT þ CT vs CC: OR ¼ 1.28, 95% CI ¼ 1.04–1.57, P ¼ 0.020). Further subgroup analyses also showed signiﬁcant associations between the -590C > T polymorphism and the risk of hepatitis C infection and hepatocellularcarcinoma.However,noassociationwasfoundbetweenthe -33T/Cpolymorphism and risk of liverdiseasesin all comparison models. This meta-analysis suggested that the IL-4 -590C > T polymorphism is associated with an increased risk of hepatitis C infection and hepatocellular carcinoma, especially among the Asian population.
Title Consumer Moments of Truth in the Digital Context : How " Search " and " E-Word of Mouth " Can Fuel Consumer Decision-Making

The consumer purchasing journey has evolved. The current paper revisits the practitioner-led “Moments of Truth” model used by a number of successful multinationals (initially Procter & Gamble and subsequently Google). “Moments of Truth” (MOT) describe key instances of contact between a potential customer and a brand. The new model integrates variables such as shared brand experience and searchable electronic word of mouth (e-WOM). This short paper establishes the cycle of e-WOM influence as recommendations are shared and searched among digitally connected consumers and explains how marketers can successfully manage these MOTs.
Denopamine, a b 1 -adrenergic agonist, increases alveolar ﬂuid clearance in ex vivo rat and guinea pig lungs

Denopamine, a b 1 -adren-ergic agonist, increases alveolar ﬂuid clearance in ex vivo rat and guinea pig lungs. J Appl Physiol 90: 10–16, 2001. —The effect of denopamine, a selective b 1 -adrenergic agonist, on alveolar ﬂuid clearance was determined in both ex vivo rat and guinea pig lungs. Alveolar ﬂuid clearance was measured by the progressive increase in the concentration of Evans blue-labeled albumin ove r 1 h at37°C. Denopamine (10 2 6 to 10 2 3 M) increased alveolar ﬂuid clearance in a dose-depen-dent manner in ex vivo rat lungs. Denopamine also stimulated alveolar ﬂuid clearance in guinea pig lungs. Atenolol, a selective b 1 -adrenergic antagonist, and amiloride, a sodium channel inhibitor, inhibited denopamine-stimulated alveolar ﬂuid clearance. The potency of denopamine was similar to that of similar doses of isoproterenol or terbutaline. Short-term hypoxia (100% nitrogen for 1–2 h) did not alter the stimulatory effect of denopamine. Denopamine (10 2 4 , 10 2 3 M) increased intracellular adenosine 3 9 ,5 9 -cyclic monophos-phate levels in cultured rat alveolar type II cells. In sum-mary, denopamine, a selective b 1 -adrenergic agonist, stimulates alveolar ﬂuid clearance in both ex vivo rat and guinea pig lungs.
Finite temperature contributions to cosmological constant

We reexamine the cosmological constant problem in a ﬁnite temperature setup and propose an intriguing possibility of carrying out perturbative analysis by employing a renormalization scheme in which the renormalized Higgs mass (or resummed mass, to be more precise) is taken to be on the order of the CMB temperature. Our proposal hinges on the fact that although the physical value of the cosmological constant does not depend on one’s renormalization scheme, whether or not a ﬁne tuning is involved does. The cosmological constant problem is avoided in the sense that the renormalization process no longer requires ﬁnetuning. This is achieved essentially by renormalization scheme-independence of a physical quantity, which in turn is assured by bare perturbation theory. The proposal shifts the cosmological constant problem to a peculiarity of the consequent perturbation series for the Higgs mass (and other massive sectors of the Standard Model); the peculiarity is interpreted as an indicator of new physics after the expected mathematical structure of the series is scrutinized. Finite-temperature-induced complexiﬁcation of the eﬀective potential is observed and its interpretation is given. A consistency check in the cosmology context is suggested.
Disrupting Education Through Blockchain-Based Education Technology?

This study is the first to systematically analyze the use of blockchain technology in education. In particular, we analyze the status quo of blockchain-based education technologies (N = 62; i.e., a full analysis). We performed a qualitative content analysis of providers’ websites to analyze the characteristics of their technologies. The analysis reveals that existing blockchain-based education technologies are diverse and offer important advantages for education (e.g., trust and equal opportunities). Employers seem to profit from these technologies (e.g., trust in applicants), but only some technologies contribute to the individualization of education. Current blockchain-based education technologies were primarily made for the general public or for job seekers. We conclude that blockchain technology might disrupt education but that this process of change is only in its infancy. Given the high relevance of this topic, we conclude by developing an agenda for future research.
Evaluation of Accounting Information System Quality of Construction Firms Listed on the Vietnamese Stock Exchange

My study aims to analyze and assess accounting information system quality of construction firms listed on the Vietnamese stock exchange, to bring comprehensive insight about the accounting information system of construction firms. The evaluation was carried out by applying qualitative methodology and quantitative methodology to the research results and based on survey results of accountant staffs and IT staffs of construction firms. Overall, the study has identified and measured nine (9) attributes of accounting information system quality of construction firms that have great effects on accountants. There is not, statistically, significant difference in the level of accounting information system quality of construction firms listed on the Vietnamese stock exchange from these different job descriptions and different work experiences. Based on the research findings, there are recommendations regarding the construction firms. The findings contribute to the further development of accounting information system quality research and the construction industry.