Miracle-dz/UniFAR-FTUs huggingface dataset:
The Making of Catalogues of Very-High-Energy {\gamma}-ray Sources

Thirty years after the discovery of the first very-high-energy γ-ray source by the Whipple telescope, the field experienced a revolution mainly driven by the third generation of imaging atmospheric Cherenkov telescopes (IACTs). The combined use of large mirrors and the invention of the imaging technique at the Whipple telescope, stereoscopic observations, developed by the HEGRA array and the fine-grained camera, pioneered by the CAT telescope, led to a jump by a factor of more than ten in sensitivity. The advent of advanced analysis techniques led to a vast improvement in background rejection, as well as in angular and energy resolutions. Recent instruments already have to deal with a very large amount of data (petabytes), containing a large number of sources often very extended (at least within the Galactic plane) and overlapping each other, and the situation will become even more dramatic with future instruments. The first large catalogues of sources have emerged during the last decade, which required numerous, dedicated observations and developments, but also made the first population studies possible. This paper is an attempt to summarize the evolution of the field towards the building up of the source catalogues, to describe the first population studies already made possible, and to give some perspectives in the context of the upcoming, new generation of instruments.
A Riemann-Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory

We establish a new perturbation theory for orthogonal polynomials using a Riemann–Hilbert approach and consider applications in numerical linear algebra and random matrix theory. This new approach shows that the orthogonal polynomials with respect to two measures can be effectively compared using the difference of their Stieltjes transforms on a suitably chosen contour. Moreover, when two measures are close and satisfy some regularity conditions, we use the theta functions of a hyperelliptic Riemann surface to derive explicit and accurate expansion formulae for the perturbed orthogonal polynomials. In contrast to other approaches, a key strength of the methodology is that estimates can remain valid as the degree of the polynomial grows. The results are applied to analyze several numerical algorithms from linear algebra, including the Lanczos tridiagonalization procedure, the Cholesky factorization, and the conjugate gradient algorithm. As a case study, we investigate these algorithms applied to a general spiked sample covariance matrix model by considering the eigenvector empirical spectral distribution and its limits. For the first time, we give precise estimates on the output of the algorithms, applied to this wide class of random matrices, as the number of iterations diverges. In this setting, beyond the first order expansion, we also derive a new mesoscopic central limit theorem for the associated orthogonal polynomials and other quantities relevant to numerical algorithms.
A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization

Batch normalization is an essential component of all state-of-the-art neural networks architectures. However, since it introduces many practical issues, much recent research has been devoted to designing normalization-free architectures. In this brief, we show that weights initialization is key to train ResNet-like normalization-free networks. In particular, we propose a slight modification to the summation operation of a block output to the skip-connection branch, so that the whole network is correctly initialized. We show that this modified architecture achieves competitive results on CIFAR-10, CIFAR-100 and ImageNet without further regularization nor algorithmic modifications.
Manifold Learning Benefits GANs

In this paper11Code: https://qithub.com/MaxwellYaoNi/LCSAGAN. , we improve Generative Adversarial Net-works by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds22The coding spaces considered in this paper are loosely termed man-ifolds. In most cases they are not manifolds in the strict mathematical sense, but rather topological spaces such as varieties, or simplicial com-plexes. The word will be used only in an informal sense. , and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discrimina-tor, with the goal of attracting intermediate feature repre-sentations onto manifolds. We adaptively balance the dis-crepancy between feature representations and their mani-fold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear mani-folds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines.
Hessian geometric structure of chemical thermodynamic systems with stoichiometric constraints

We establish a Hessian geometric structure in chemical thermodynamics which describes chemical reaction networks (CRNs) with equilibrium states. In our setup, the ideal gas assumption and mass action kinetics are not required. The existence and uniqueness condition of the equilibrium state is derived by using the Legendre duality inherent to the Hessian structure. The entropy production during a relaxation to the equilibrium state can be evaluated by the Bregman divergence. Furthermore, the equilibrium state is characterized by four distinct minimization problems of the divergence, which are obtained from the generalized Pythagorean theorem originating in the dual flatness. For the ideal gas case, we confirm that our existence and uniqueness condition implies Birch's theorem, and that the entropy production represented by the divergence coincides with the generalized Kullback-Leibler divergence. In addition, under mass action kinetics, our general framework reproduces the local detailed balance condition.
Universal large-order asymptotic behavior of the strong-coupling and high-temperature series expansions

For theories that exhibit second order phase transition, we conjecture that the large-order asymptotic behavior of the strong-coupling ( High-Temperature) series expansion takes the form σnnb where b is a universal parameter. The associated critical exponent is then given by b+1. The series itself can be approximated by the hypergeometric approximants pFp−1 which can mimic the same large-order behavior of the given series. Near the tip of the branch cut, the hypergeometric function pFp−1 has a power-law behavior from which the critical exponent and critical coupling can be extracted. The conjecture has been tested in this work for the perturbation series of the ground state energy of the Yang-Lee model as a strong-coupling form of the PT -symmetric iφ3 theory and the High-Temperature expansion within the Ising model. From the known b parameter for the Yang-Lee model, we obtained the exact critical exponents which reflects the universality of b. Very accurate prediction for b has been obtained from the many orders available for the High-Temperature series expansion of the Ising model which in turn predicts accurate critical exponent. Apart from critical exponents, the hypergeometric approximants for the Yang-Lee model show almost exact predictions for the ground state energy from low orders of perturbation series as input.
Unsupervised Data Selection for Data-Centric Semi-Supervised Learning

We study unsupervised data selection for semi-supervised learning (SSL), where a large-scale unlabeled dataset is available and a small subset of data is bud-geted for label acquisition. Existing SSL methods focus on learning a model that effectively integrates information from given small labeled data and large unlabeled data, whereas we focus on selecting the right data to annotate for SSL without requiring any label or task information. Intuitively, instances to be labeled shall collectively have maximum diversity and coverage for downstream tasks, and individually have maximum information propagation utility for SSL. We formalize these concepts in a three-step data-centric SSL method that improves FixMatch in stability and accuracy by 8% on CIFAR-10 (0.08% labeled) and 14% on ImageNet-1K (0.2% labeled). It is also a universal framework that works with various SSL methods, delivering consistent performance gains. Our work demonstrates that small computation spent on carefully selecting data for annotation brings big annotation efﬁciency and model performance gain without changing the learning pipeline. Our completely unsupervised data selection can be easily extended to other weakly supervised learning settings.
Unsteady Flow Within Centrifugal Compressor Channels Under Rotating Stall and Surge

Unsteady flow patterns throughout a centrifugal compressor system during the rotating stall and the surge were measured experimentally. Various kinds of unsteady behaviors of the flow appeared both continuously and suddenly as the flow rate decreased. The part-span stall, the full-span stall, the mild and the deep stalls and the deep surge appeared clearly. The fluctuations caused by the full-span stall were seen even during the surge and affected the flow within the scroll through the vaneless diffuser. The experimental results were compared with those computed by the lumped parameter theory. The good agreements between them were obtained when the appropriate values were selected for the lumped parameters.Copyright © 1991 by ASME
Sub-Character Tokenization for Chinese Pretrained Language Models

Tokenization is fundamental to pretrained language models (PLMs). Existing tokenization methods for Chinese PLMs typically treat each character as an indivisible token. However, they ignore the unique feature of the Chinese writing system where additional linguistic information exists below the character level, i.e., at the sub-character level. To utilize such information, we propose sub-character (SubChar for short) tokenization. Specifically, we first encode the input text by converting each Chinese character into a short sequence based on its glyph or pronunciation, and then construct the vocabulary based on the encoded text with sub-word segmentation. Experimental results show that SubChar tokenizers have two main advantages over existing tokenizers: 1) They can tokenize inputs into much shorter sequences, thus improving the computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode Chinese homophones into the same transliteration sequences and produce the same tokenization output, hence being robust to homophone typos. At the same time, models trained with SubChar tokenizers perform competitively on downstream tasks. We release our code and models at https://github.com/thunlp/SubCharTokenization to facilitate future work.
The growth mechanism of CuO nanowires synthesized by the thermal oxidation method

Large-scale CuO nanowires are commonly synthesized by the thermal oxidation method. However, the growth mechanism remains controversial between diffusion growth mechanism and vapor-solid (VS) growth mechanism. In this study, we investigated the morphology and atomic structure of the CuO nanowires by electron microscopes to clarify the growth mechanism. We found that they were grown with the annealing ambient during the cooling process and there were screw dislocations in the nanowires. These features indicate that the VS growth mechanism is applicable to explain the growth of the CuO nanowires. In addition, the kinetic consideration also supports the VS growth mechanism.
Time and Memory Efficient Parallel Algorithm for Structural Graph Summaries and two Extensions to Incremental Summarization and $k$-Bisimulation for Long $k$-Chaining

Most graph summarization algorithms are tailored to a specific graph summary model and were designed for a one-time batch computation of a summary. We developed a flexible parallel algorithm for graph summarization based on vertex-centric programming and parameterized message passing. The base algorithm supports infinitely many structural graph summary models defined in a formal language. An extension of the parallel base algorithm allows incremental graph summarization, i.e., updating the summary as the graph evolves. 1 In this paper, we prove that the incremental algorithm is correct and show that updates are performed in time O( Δ · 𝑑 𝑘 ) , where Δ is the number of additions, deletions, and modifications to the input graph, 𝑑 the maximum degree, and 𝑘 is the maximum distance in the subgraphs considered. Although the iterative algorithm supports values of 𝑘 > 1, it requires nested data structures for the message passing that are memory-inefficient. Thus, we extended the base summarization algorithm by a hash-based messaging mechanism to support a scalable iterative computation of graph summarizations based on 𝑘 -bisimulation for arbitrary 𝑘 . We empirically evaluate the performance of our algorithms using benchmark and real-world datasets. The incremental algorithm almost always outperforms the batch computation. We observe in our experiments that the incremental algorithm is faster even in cases when 50% of the graph database changes from one version to the next. The incremental computation requires a three-layered hash index, which has a low memory overhead of only 8% ( ± 1%) compared to its batch-based version. Finally, the incremental summarization algorithm outperforms the batch algorithm even when fewer cores are available. The iterative parallel 𝑘 -bisimulation algorithm computes summaries on graphs with over 10M edges within seconds. We show that the algorithm scales and processes graphs of 100 + M edges within a few minutes while having a moderate memory consumption below of 150 GB. For the largest BSBM1B dataset with 1 billion edges, it computes 𝑘 = 10 bisimulation in under an hour, i.e., we need only four to five minutes per iteration.
Discretionary Effort on Green Technology Innovation: How Chinese Enterprises Act when Facing Financing Constraints

Firm’s effort on Green technology innovation (hereafter, called G-innovation) is affected by financing constraints, and firm will make a discretionary choice according to its own situation, to achieve the maximization of self-interests. Based on the data of Chinese micro enterprises, firstly, we empirically analyze firms’ decision-making towards G-innovation when faced with financing constraints. It supports the view that financing constraints can hinder enterprise technological innovation. And we also make an explanation that the social benefits of green technology innovation are greater than personal benefits, which makes enterprises tend to reduce green technology innovation when facing financing constraints. Then we examine firms’ heterogonous behaviors under different internal attributes and external environments. The results reveal that: First, firms are reluctant to pay more efforts to G-innovation when faced with increased financing constraints. Second, firms with different attributes exhibit heterogeneous G-innovation. Political connections will change firms’ willingness to innovate, while the structure of property rights and the pollution degree will not. Third, firms under different external environment also exhibit heterogeneous G-innovation. When economic policy uncertainty increases, firms’ willingness to innovate weakens. The development of shadow banks fail to improve firm’s willingness to innovate.
Micro-component survey of residential water consumption in Hanoi.

Daily total water consumption per capita has been used as a basic unit for the future planning of water supply for domestic use. However, for innovative water utilization designs that consider various scenarios, including the effects of policy direction and global warming, and more strategic and efficient water use, it is absolutely essential to consider water usage divided by residential activities, such as toilet flushing, cooking, clothes washing, and bathing. We collected micro-component data by direct measurement from each household outlet, and developed small accumulative meters. Measurements were conducted at 56 households for 2 months in Hanoi, Vietnam, and the average consumption was 18.6 L/p/d for toilet, 16.2 L/p/d for laundry, 10.4 L/p/d for bath, and 15.7 L/p/d for kitchen. We then analyzed the representative values and the distribution of water consumption for every usage from social and economic perspectives. In addition, we compared the results in Hanoi with those in Chiang Mai, Thailand, where we investigated water consumption a few years ago, and their value seemed similar except for bathroom use, but the substance was different. One distinct outcome of our investigation was the recognition of the cultural and methodological challenges to end-use assessment of water consumption in modernizing Asian communities.
Nonadherence in diabetes care among US adults with diabetes by stroke status

Objective Effects of stroke (i.e., memory loss, paralysis) may make effective diabetes care difficult which can in turn contribute to additional diabetes related complications and hospitalization. However, little is known about US post-stroke diabetes care levels. This study sought to examine diabetes care levels among US adults with diabetes by stroke status. Methods Using 2015–2018 Behavioral Risk Factor Surveillance System surveys, the prevalence of nonadherence with the American Diabetes Association’s diabetes care measures (<1 eye exam annually, <1 foot exam annually, <1 blood glucose check daily, <2 A1C tests annually, no receipt of annual flu vaccination) was ascertained in people with diabetes by stroke status. A separate logistic regression model was run for each diabetes care measure to determine if nonadherence patterns differed by stroke status after adjustment for stroke and diabetes associated factors. Results Our study included 72,630 individuals, with 9.8% having had a stroke. Nonadherence levels varied for each diabetes care measure ranging from 20.4–42.2% for stroke survivors and 22.8–44.0% for those who had never had stroke. By stroke status, nonadherence with diabetes management measures was comparable except for stroke survivors having both a lower prevalence (30.2% versus 40.1%) and odds of nonadherence (OR: 0.73, 95% CI: 0.65, 0.82) with daily blood glucose check than those who had never had stroke. Conclusion While nonadherence with diabetes management does not vary by stroke status, considerable nonadherence still exists among stroke survivors with diabetes. Additional interventions to improve diabetes care may help to reduce risk of further diabetes complications in this population.
Intraspecific Variation in the Rates of Mutations Causing Structural Variation in Daphnia magna

Abstract Mutations that cause structural variation are important sources of genetic variation upon which other evolutionary forces can act, however, they are difficult to observe and therefore few direct estimates of their rate and spectrum are available. Understanding mutation rate evolution, however, requires adding to the limited number of species for which direct estimates are available, quantifying levels of intraspecific variation in mutation rates, and assessing whether rate estimates co-vary across types of mutation. Here, we report structural variation-causing mutation rates (svcMRs) for six categories of mutations (short insertions and deletions, long deletions and duplications, and deletions and duplications at copy number variable sites) from nine genotypes of Daphnia magna collected from three populations in Finland, Germany, and Israel using a mutation accumulation approach. Based on whole-genome sequence data and validated using simulations, we find svcMRs are high (two orders of magnitude higher than base substitution mutation rates measured in the same lineages), highly variable among populations, and uncorrelated across categories of mutation. Furthermore, to assess the impact of scvMRs on the genome, we calculated rates while adjusting for the lengths of events and ran simulations to determine if the mutations occur in genic regions more or less frequently than expected by chance. Our results pose a challenge to most prevailing theories aimed at explaining the evolution of the mutation rate, underscoring the importance of obtaining additional mutation rate estimates in more genotypes, for more types of mutation, in more species, in order to improve our future understanding of mutation rates, their variation, and their evolution.
National attitudes of medical students towards mandating the COVID-19 vaccine and its association with knowledge of the vaccine

Background With the introduction of the novel COVID-19 vaccine, public hesitancy is being experienced with many turning to healthcare professionals for advice. As future physicians, medical students play a critical role in the public’s view of the vaccine. Objectives To determine the attitude of U.S. medical students toward mandating the COVID-19 vaccine to healthcare workers and patients, as well as whether their knowledge of the vaccine plays a role in their view. Methods The authors emailed a survey link to all U.S. medical schools with request to distribute it to their medical students. The survey remained open from 02/09/2021 to 03/15/2021 and included questions to determine the attitude of the medical students toward recommending the COVID-19 vaccine, and general knowledge questions about the vaccine. Chi square, Fisher’s exact test, and linear regression were conducted to determine associations between willingness to recommend the COVID-19 vaccine and general knowledge of the vaccine. Results Among the 1,899 responses from medical students representing 151 U.S. medical schools, 57.82% approved of making the COVID-19 vaccine mandatory to healthcare workers, and 16.27% approved of making it mandatory to patients. Additionally, those who tested most knowledgeable of the vaccine were less likely to approve of making the vaccine mandatory for patients (66.67% vs. 72.70). Those that tested most knowledgeable were also more likely to personally receive the vaccine (72.35% vs 62.99%) as opposed to those that tested the least knowledgeable who were less willing to personally receive the vaccine (4.12% vs 14.17%). Conclusions The data revealed that a slight majority of medical students support a vaccine mandate toward healthcare workers while a minority of medical students support a vaccine mandate toward healthcare workers. Additionally, medical students that had relatively high knowledge of the vaccine correlated with not approving of making the vaccine mandatory for patients. However increased knowledge of the vaccine correlated with increased willingness to personally receive the vaccine.
Development of a natural gas spark ignition engine for optimum performance

Abstract A 4 litre displacement, six cylinder, fuel injected petrol engine was modified to natural gas (NG) fuelling. Experimental investigation was carried out with various mixture controls and compression ratios over a wide range of operating conditions. As a strategy for combustion chamber shape modification, the compression ratio was raised with simultaneous enhancement of in-cylinder turbulence through squish motion. A fast burning chamber for the combustion of lean mixtures of natural gas and air was developed. Brake thermal efficiency in excess of 40 per cent and brake torque in excess of the peak base torque with petrol were achieved. The research provides the foundation for the implementation of NG cars that will emit only about 65 per cent of the carbon dioxide (CO2) of their petrol engine counterparts, with the prospect of extending the limited range of NG cars by up to one-third and producing low hydrocarbon (HC) and nitrogen oxide (NOx) emissions.