Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential benefits of delaying the second mRNA COVID-19 vaccine dose

Vaccination against COVID-19 with the recently approved mRNA vaccines BNT162b2 (BioNTech/Pfizer) and mRNA-1273 (Moderna) is currently underway in a large number of countries. However, high incidence rates and rapidly spreading SARS-CoV-2 variants are concerning. In combination with acute supply deficits in Europe in early 2021, the question arises of whether stretching the vaccine, for instance by delaying the second dose, can make a significant contribution to preventing deaths, despite associated risks such as lower vaccine efficacy, the potential emergence of escape mutants, enhancement, waning immunity, reduced social acceptance of off-label vaccination, and liability shifts. A quantitative epidemiological assessment of risks and benefits of non-standard vaccination protocols remains elusive. To clarify the situation and to provide a quantitative epidemiological foundation we develop a stochastic epidemiological model that integrates specific vaccine rollout protocols into a risk-group structured infectious disease dynamical model. Using the situation and conditions in Germany as a reference system, we show that delaying the second vaccine dose is expected to prevent deaths in the four to five digit range, should the incidence resurge. We show that this considerable public health benefit relies on the fact that both mRNA vaccines provide substantial protection against severe COVID-19 and death beginning 12 to 14 days after the first dose. The benefits of protocol change are attenuated should vaccine compliance decrease substantially. To quantify the impact of protocol change on vaccination adherence we performed a large-scale online survey. We find that, in Germany, changing vaccination protocols may lead to small reductions in vaccination intention. In sum, we therefore expect the benefits of a strategy change to remain substantial and stable.
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynamics modeling and the subsequent planning algorithm, and as a result, they often possess tens of hyperparameters and architectural choices. For this reason, MBRL typically requires significant human expertise before it can be applied to new problems and domains. To alleviate this problem, we propose to use automatic hyperparameter optimization (HPO). We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts. In addition, we show that tuning of several MBRL hyperparameters dynamically, i.e. during the training itself, further improves the performance compared to using static hyperparameters which are kept fixed for the whole training. Finally, our experiments provide valuable insights into the effects of several hyperparameters, such as plan horizon or learning rate and their influence on the stability of training and resulting rewards.
Zoetrope genetic programming for regression

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.
Learning with invariances in random features and kernel models

A number of machine learning tasks entail a high degree of invariance: the data distribution does not change if we act on the data with a certain group of transformations. For instance, labels of images are invariant under translations of the images. Certain neural network architectures —for instance, convolutional networks—are believed to owe their success to the fact that they exploit such invariance properties. With the objective of quantifying the gain achieved by invariant architectures, we introduce two classes of models: invariant random features and invariant kernel methods. The latter includes, as a special case, the neural tangent kernel for convolutional networks with global average pooling. We consider uniform covariates distributions on the sphere and hypercube and a general invariant target function. We characterize the test error of invariant methods in a high-dimensional regime in which the sample size and number of hidden units scale as polynomials in the dimension, for a class of groups that we call ‘degeneracy α’, with α ≤ 1. We show that exploiting invariance in the architecture saves a d factor (d stands for the dimension) in sample size and number of hidden units to achieve the same test error as for unstructured architectures. Finally, we show that output symmetrization of an unstructured kernel estimator does not give a significant statistical improvement; on the other hand, data augmentation with an unstructured kernel estimator is equivalent to an invariant kernel estimator and enjoys the same improvement in statistical efficiency.
Revelation gap for pricing from samples

This paper considers prior-independent mechanism design, in which a single mechanism is designed to achieve approximately optimal performance on every prior distribution from a given class. Most results in this literature focus on mechanisms with truthtelling equilibria, a.k. a. , truthful mechanisms. Feng and Hartline [FOCS 2018] introduce the revelation gap to quantify the loss of the restriction to truthful mechanisms. We solve a main open question left in Feng and Hartline [FOCS 2018]; namely, we identify a non-trivial revelation gap for revenue maximization. Our analysis focuses on the canonical problem of selling a single item to a single agent with only access to a single sample from the agent's valuation distribution. We identify the sample-bid mechanism (a simple non-truthful mechanism) and upper-bound its prior-independent approximation ratio by 1.835 (resp. 1.296) for regular (resp. MHR) distributions. We further prove that no truthful mechanism can achieve prior-independent approximation ratio better than 1.957 (resp. 1.543) for regular (resp. MHR) distributions. Thus, a non-trivial revelation gap is shown as the sample-bid mechanism outperforms the optimal prior-independent truthful mechanism. On the hardness side, we prove that no (possibly non-truthful) mechanism can achieve prior-independent approximation ratio better than 1.073 even for uniform distributions.
Are Gross Substitutes a Substitute for Submodular Valuations?

The class of gross substitutes (GS) set functions plays a central role in Economics and Computer Science. GS belongs to the hierarchy of complement free valuations introduced by Lehmann, Lehmann and Nisan, along with other prominent classes: GS ⊊ Submodular ⊊ XOS ⊊ Subadditive$. The GS class has always been more enigmatic than its counterpart classes, both in its definition and in its relation to the other classes. For example, while it is well understood how closely the Submodular, XOS and Subadditive classes (point-wise) approximate one another, approximability of these classes by GS remained wide open. In particular, the largest gap known between Submodular and GS valuations was some constant ratio smaller than 2. Our main result is the existence of a submodular valuation (one that is also budget additive) that cannot be approximated by GS within a ratio better than $Ømega(łog m/łogłog m), where m is the number of items. En route, we uncover a new symmetrization operation that preserves GS, which may be of independent interest. We show that our main result is tight with respect to budget additive valuations. However, whether GS approximates general submodular valuations within a poly-logarithmic factor remains open, even in the special case of concave of GS valuations (a subclass of Submodular containing budget additive). For concave of Rado valuations (Rado is a significant subclass of GS, containing, e.g., weighted matroid rank functions and OXS), we show approximability by GS within an O(łog2m) factor.
Learning to make compiler optimizations more effective

Because loops execute their body many times, compiler developers place much emphasis on their optimization. Nevertheless, in view of highly diverse source code and hardware, compilers still struggle to produce optimal target code. The sheer number of possible loop optimizations, including their combinations, exacerbates the problem further. Today's compilers use hard-coded heuristics to decide when, whether, and which of a limited set of optimizations to apply. Often, this leads to highly unstable behavior, making the success of compiler optimizations dependent on the precise way a loop has been written. This paper presents LoopLearner, which addresses the problem of compiler instability by predicting which way of writing a loop will lead to efficient compiled code. To this end, we train a neural network to find semantically invariant source-level transformations for loops that help the compiler generate more efficient code. Our model learns to extract useful features from the raw source code and predicts the speedup that a given transformation is likely to yield. We evaluate LoopLearner with 1,895 loops from various performance-relevant benchmarks. Applying the transformations that our model deems most favorable prior to compilation yields an average speedup of 1.14x. When trying the top-3 suggested transformations, the average speedup even increases to 1.29x. Comparing the approach with an exhaustive search through all available code transformations shows that LoopLearner helps to identify the most beneficial transformations in several orders of magnitude less time.
Average Rate and Error Probability Analysis in Short Packet Communications Over RIS-Aided URLLC Systems

In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels’ path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error.
Why did the distribution change?

We describe a formal approach based on graphical causal models to identify the"root causes"of the change in the probability distribution of variables. After factorizing the joint distribution into conditional distributions of each variable, given its parents (the"causal mechanisms"), we attribute the change to changes of these causal mechanisms. This attribution analysis accounts for the fact that mechanisms often change independently and sometimes only some of them change. Through simulations, we study the performance of our distribution change attribution method. We then present a real-world case study identifying the drivers of the difference in the income distribution between men and women.
Markov chain Monte Carlo analyses of the flux ratios of B, Be and Li with the DRAGON2 code

Recent cosmic-ray measurements are challenging our models of propagation in the Galaxy. A good characterization of the secondary cosmic rays (B, Be, Li and sub-iron species) is crucial to constrain these models and exploit the precision of modern CR experiments. In this work, a Markov chain Monte Carlo analysis has been implemented to fit the experimental flux ratios between B, Be and Li and their flux ratios to the primary cosmic-ray nuclei C and O. We have fitted the data using two different parametrizations for the spallation cross sections. The uncertainties in the evaluation of the spectra of these secondary cosmic rays, due to spallation cross sections, have been taken into account by introducing scale factors as nuisance parameters in the fits, assuming that this uncertainty is mostly due to the normalization of the cross sections parametrizations. We have also tested two different formulations for the diffusion coefficient, which differ in the origin of the high energy hardening (∼ 200 GeV/n) of cosmic rays. Additionally, two different approaches are used to scale the cross sections, one based on a combined analysis of all the species (“combined” analysis) and the other reproducing the high energy spectra of the secondary-to-secondary flux ratios of Be/B, Li/B, Li/Be (“scaled” analysis). This allows us to make a better comparison between the propagation parameters inferred from the cross sections parametrizations tested in this work. This novel analysis has been successfully implemented using the numerical code DRAGON2 dedicated to cosmic-ray propagation to reproduce the cosmic-ray nuclei data up to Z=14 from the AMS-02 experiment. In general, it is found that the ratios of Li favor a harder spectral index of the diffusion coefficient, but compatible with the other ratios inside the observed 2σ uncertainties. In addition, it is shown that, including these scale factors, the secondary-to-primary flux ratios can be simultaneously reproduced, obtaining that the scale factor associated to the cross sections of boron production is the lowest one, whereas that associated to Li production is the largest one.
Double-IRS Aided MIMO Communication Under LoS Channels: Capacity Maximization and Scaling

Intelligent reflecting surface (IRS) is a promising technology to extend the wireless signal coverage and support the high performance communication. By intelligently adjusting the reflection coefficients of a large number of passive reflecting elements, the IRS can modify the wireless propagation environment in favour of signal transmission. Different from most of the prior works which did not consider any cooperation between IRSs, in this work we propose and study a cooperative double-IRS aided multiple-input multiple-output (MIMO) communication system under the line-of-sight (LoS) propagation channels. We investigate the capacity maximization problem by jointly optimizing the transmit covariance matrix and the passive beamforming matrices of the two cooperative IRSs. Although the above problem is non-convex and difficult to solve, we transform and simplify the original problem by exploiting a tractable characterization of the LoS channels. Then we develop a novel low-complexity algorithm whose complexity is independent of the number of IRS elements. Moreover, we analyze the capacity scaling orders of the double-IRS aided MIMO system with respect to an asymptotically large number of IRS elements or transmit power, which significantly outperform those of the conventional single-IRS aided MIMO system, thanks to the cooperative power gain brought by the double-reflection link and the spatial multiplexing gain harvested from the two single-reflection links. Extensive numerical results are provided to show that by exploiting the LoS channel properties, our proposed algorithm can achieve a desirable performance with low computational time. Also, our capacity scaling analysis is validated, and the double-IRS system is shown to achieve a much higher rate than its single-IRS counterpart as long as the number of IRS elements or the transmit power is not small.
Improved Nutrient Uptake and Growth of Maize in Response to Inoculation with Thiobacillus and Mycorrhiza on an Alkaline Soil

Most plant nutrients are optimally available when soil pH is close to neutral. In this experiment the effects of Thiobacillus and Mycorrhiza on nutrient uptake and grain yield of maize were studied on an alkaline soil as a factorial experiment with randomized complete blocks design. Treatments consisted of Mycorrhiza fungi (M): inoculated (m1) and noninoculated (m0), Thiobacillus (T): inoculated (t1) and noninoculated (t0), and sulfur (S) (S0, S1: 250, and S2: 500 kg ha−1). Inoculation of Mycorrhiza, Thiobacillus, and S application decreased soil pH and increased grain yield and seed oil content. The lowest soil pH and the greatest S content were obtained from the combination of Thiobacillus and 500 kg ha−1 S. Inoculation of Thiobacillus and S application significantly decreased root colonization. The greatest iron (Fe) content was in the combination of Mycorrhiza inoculation and 500 kg ha−1 S. Grain P content significantly increased with Mycorrhiza inoculation and S application. The greatest grain yield obtained from combination of Thiobacillus with 500 kg ha−1 S.
MazF toxin causes alterations in Staphylococcus aureus transcriptome, translatome and proteome that underlie bacterial dormancy

Abstract Antibiotic resistance is a serious problem which may be caused by bacterial dormancy. It has been suggested that bacterial toxin–antitoxin systems induce dormancy. We analyzed the genome-wide role of Staphylococcus aureus endoribonuclease toxin MazF using RNA-Seq, Ribo-Seq and quantitative proteomics. We characterized changes in transcriptome, translatome and proteome caused by MazF, and proposed that MazF decreases translation directly by cleaving mRNAs, and indirectly, by decreasing translation factors and by promoting ribosome hibernation. Important pathways affected during the early stage of MazF induction were identified: MazF increases cell wall thickness and decreases cell division; MazF activates SsrA-system which rescues stalled ribosomes, appearing as a result of MazF mRNA cleavage. These pathways may be promising targets for new antibacterial drugs that prevent bacteria dormancy. Finally, we described the overall impact of MazF on S. aureus cell physiology, and propose one of the mechanisms by which MazF might regulate cellular changes leading to dormancy.
A 3D organopolymolybdate polymer with unusual topology functionalized by 1,4-bis(1,2,4-triazol-1-yl)butane through Mo–N bond

An unusual (4,6,6)-connected high dimensional organopolymolybdate polymer by 1,4-bis(1,2,4-triazol-1-yl)butane coordinating directly to a molybdenum atom has been obtained under hydrothermal condition, which exhibits a self-penetrating framework with (44·5·6)(45·54·66)(48·52·65) topology assembled from 2D self-threading skeleton.
Influence of irrigation and growth stage on element concentrations of soybean plant parts

Abstract A three‐year field study was conducted to determine the influence of irrigation on the nutrition of the “Lee 74” cultivar, a determinate soybean (Glycine max (L.) Merr. ). The objectives were to measure seasonal concentrations of N, K, Ca, and Mg in steins, leaves, and pods for soybeans grown on a Crowley silt loam (Typic Albaqualf) and a Kobel clay (Vertic Fluvaquent) under irrigated and nonirrigated moisture regimes. Fluctuations in element concentrations in relation to plant growth stage for irrigated and nonirrigated leaves and stems were modeled using multivariate regression. Elements in the pods as effected by irrigation and growth stage were also compared. During 1974, a wet year, element concentrations of the leaves, stems, and pods were unaffected by irrigation. In the dry years of 1975 and 1976, significant differences in the concentration of N, K, Ca, and Mg in the plant component parts were found due to irrigation. Generally, element concentrations of stems, leaves, and pods of irrigat.
Clinical features and prognostic factors of elderly patients with metastatic pancreatic cancer: a population-based study

The aim of this study was to evaluate the clinical features and prognostic factors of elderly patients with metastatic pancreatic cancer. Patients diagnosed with metastatic pancreatic cancer between 2004 and 2014 were identified from the Surveillance Epidemiology and End Results database. Clinical characteristics and prognostic factors in elderly patients with metastatic pancreatic cancer were examined. A total of 10784 metastatic pancreatic cancer patients between 65 and 80 years old were included and divided into three age groups. Elderly metastatic pancreatic cancer patients differed from younger patients in many aspects, including marital status, race, sex, T stage, N stage, treatment regimen, prognosis, cause of death, and metastatic characteristics (P<0.001). An analysis of prognostic factors showed that chemotherapy, as the main treatment for elderly patients, can significantly improve prognosis, while surgery can improve the prognosis of patients between 65 and 80 years old. Other factors, including sex, marital status, T stage, and site of metastasis, had different effects on patients in different age groups. Elderly patients with metastatic pancreatic cancer are a special group of individuals whose clinical characteristics and prognostic factors are different from those of younger patients, and these patients require special treatment and attention.