Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly compact neutron stars and screening mechanisms: Equilibrium and stability

Modified theories of gravity that offer viable models for dark energy often rely on mechanisms that screen their effects in high density environments. From this perspective, it would appear that, once solar system constraints are satisfied, these theories would predict a trivial phenomenology for (much denser) neutron stars. In this work we explore the fact that in scalar-tensor theories the scalar degree of freedom does not couple to the mass density alone, but to the trace of the energy-momentum tensor - which can increase and eventually change sign as density and pressure build up in the core of neutron stars -, and investigate whether there could be a partial unscreening of the scalar field inside the most compact stars found in Nature. For this purpose, we construct neutron star solutions with realistic equations of state in theories with screening mechanisms and study their stability under radial perturbations. In particular, we find that stable solutions with a unscreened core can exist in chameleon models, while for the environmentally-dependent dilaton model a wealth of new, scalarized equilibrium solutions are found, some of which can be stable.
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming

Most of the literature on neural network quantization requires some training of the quantized model (fine-tuning). However, this training is not always possible in real-world scenarios, as it requires the full dataset. Lately, post-training quantization methods have gained considerable attention, as they are simple to use and require only a small, unlabeled calibration set. Yet, they usually incur significant accuracy degradation when quantized below 8-bits. This paper seeks to address this problem by introducing two pipelines, advanced and light, where the former involves: (i) minimizing the quantization errors of each layer by optimizing its parameters over the calibration set; (ii) using integer programming to optimally allocate the desired bit-width for each layer while constraining accuracy degradation or model compression; and (iii) tuning the mixed-precision model statistics to correct biases introduced during quantization. While the light pipeline which invokes only (ii) and (iii) obtains surprisingly accurate results; the advanced pipeline yields state-of-the-art accuracy-compression ratios for both vision and text models. For instance, on ResNet50, we obtain less than 1% accuracy degradation while compressing the model to 13% of its original size. We open-sourced our code.
Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting

Time series prediction has been a long-standing research topic and an essential application in many domains. Modern time series collected from sensor networks (e.g., energy consumption and traffic flow) are often large-scale and incomplete with considerable corruption and missing values, making it difficult to perform accurate predictions. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data. The key of LATC is to transform the original multivariate time series matrix (e.g., sensor$\times$time point) to a third-order tensor structure (e.g., sensor$\times$time of day$\times$day) by introducing an additional temporal dimension, which allows us to model the inherent rhythms and seasonality of time series as global patterns. With the tensor structure, we can transform the time series prediction and missing data imputation problems into a universal low-rank tensor completion problem. Besides minimizing tensor rank, we also integrate a novel autoregressive norm on the original matrix representation into the objective function. The two components serve different roles. The low-rank structure allows us to effectively capture the global consistency and trends across all the three dimensions (i.e., similarity among sensors, similarity of different days, and current time v.s. the same time of historical days). The autoregressive norm can better model the local temporal trends. Our numerical experiments on three real-world data sets demonstrate the superiority of the integration of global and local trends in LATC in both missing data imputation and rolling prediction tasks.
Nanoflagellate predation on auto- and heterotrophic picoplankton in the oligotrophic Mediterranean Sea

Dynamics of autotrophic and heterotrophic prokaryotes and their consumption by nanoflagellates were studied in the euphotic zone at nine stations located from the Levantine Basin (34°E) to the Balearic sea (5°E) in June 1999. Bacterial biomass constituted the largest proportion of living biomass at all stations. Integrated bacterial production at the furthest east station, was sixfold lower than integrated bacterial production at the furthest west (13 and 75 mg C m -2 d -1 respectively). Estimated heterotrophic nanoflagellate bacterivory accounted for 45-87% of bacterial production. Small protists (<3 μm) dominated the bacterivore assemblage and accounted for more than 90% of the heterotrophic bacterial consumption. Our results indicated that there was no negative selection against Synechococcus and that both picoplankton groups were grazed according to their standing stocks. An estimated consumption of Synechococcus derived from food vacuole content analysis of nanoflagellates revealed that they consumed from 0.5 to 45% (mean 13%) of Synechococcus stock per day. These data are among the first documenting the relative grazing impact of heterotrophic nanoflagellates on bacteria and Synechococcus in situ. Assuming that there was no selection for or against Prochlorococcus, heterotrophic nanoflagellates could ingest from 1.4 to 21% (mean 6%) of Prochlorococcus stock per day. The amount of organic carbon obtained by heterotrophic nanoflagellates from photosynthetic prokaryotes represented 27% of the total amount of carbon obtained from total prokaryotes.
Intense uplift of the Qinghai-Tibetan Plateau triggered rapid diversification of Phyllolobium (Leguminosae) in the Late Cenozoic

Background: Phyllolobium, a recently established genus from subgenus Pogonophace of Astragalus, contains about 20 species and four sections, mostly endemic to the Qinghai-Tibetan Plateau (QTP). The uplift of the QTP undoubtedly affected organismic evolution in the region, but further molecular dating in a phylogenetic context is required to test whether diversification is linked in particular to the intense uplift 3.6 million years ago (Ma). Aims: Based on molecular dating of Phyllolobium, we attempted to identify a relationship between diversification and speciation of Phyllolobium and the geological event of this intense uplift. Methods: Internal transcribed spacer (ITS) sequence data of Phyllolobium were used for the study. Outgroup genera selected were Astragalus, Oxytropis, Caragana, Calophaca, Hamilodendron, Hedysarum, Sphaerophysa, Swainsona and Colutea, as well as Dalbergia and Pueraria. Both of the latter genera have fossil records, enabling their use in constraint calibrations for dating. Phylogenetic analysis employed maximum parsimony and Bayesian inference, and phylogenetic datings were conducted by using three approaches implemented in the programs r8s, PAML and BEAST. Results: The estimated crown age of Phyllolobium was dated to 3.62 Ma in PL-r8s (3.96 in BEAST, 3.48 in PAML, listed similarly in the following), and sections within the genus had ages ranging between 3.60 and 2.55 Ma. These dates coincide with, or follow soon after, the intense uplift of the QTP in the Late Pliocene. By contrast, diversification of sections Bibracteolati and Oliganthum began at estimated ages of 1.95 Ma (2.24, 1.94) and 1.83 Ma (1.53, 1.62), respectively, and may therefore have been triggered by the ‘Qingzang’ plateau uplift (2.5–1.7 Ma). P. tribulifolium had a dated crown age of 0.63 Ma (0.58, 0.42), suggesting that the ‘Kunhuang’ uplift and/or glaciation (occurring between 1.1/0.8 and 0.6 Ma) facilitated diversification. Conclusions: The estimated crown ages of Phyllolobium and of sections within it indicate that rapid diversification was likely triggered by consecutive phases of QTP uplift in the Late Pliocene and the Early-to-Mid Pleistocene. However, the diversification of at least some Phyllolobium species can be explained by Late Pleistocene glaciation and/or geological events. In sum, these findings characterise Phyllolobium as a recently diversified genus adapted to the cold and dry habitats of the QTP and its environs.
Some useful approximations for calculation of directivities of multibeam power patterns of large planar arrays

In this paper, by adopting the superposition of terms with unequal coefficients, some useful closed-form formulas for the class of large planar arrays are presented for predicting the directivities of radiated beams. Despite the applied simplifying assumptions, the provided formulas may be used for designing a large multiple-beam planar array with a specified power pattern. Several illustrative examples are presented which are numerically demonstrate by the MATLAB software. The applicability of the proposed synthesis method verified by the achieved conformity between the simulations and theoretical predictions. Furthermore, the impact of quantization and array dimensions on the performance of power controlling as well as the limitations on the maximum scan angle are investigated. It is believed that the proposed straightforward approach for array synthesis provides a new opportunity for various applications such as multiple-target radar systems and beamforming array antenna.
HyNet: Local Descriptor with Hybrid Similarity Measure and Triplet Loss

Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature. In this paper, we investigate how L2 normalisation affects the back-propagated descriptor gradients during training. Based on our observations, we propose HyNet, a new local descriptor that leads to state-of-the-art results in matching. HyNet introduces a hybrid similarity measure for triplet margin loss, a regularisation term constraining the descriptor norm, and a new network architecture that performs L2 normalisation of all intermediate feature maps and the output descriptors. HyNet surpasses previous methods by a significant margin on standard benchmarks that include patch matching, verification, and retrieval, as well as outperforming full end-to-end methods on 3D reconstruction tasks.
Extensively Matching for Few-shot Learning Event Detection

Current event detection models under supervised learning settings fail to transfer to new event types. Few-shot learning has not been explored in event detection even though it allows a model to perform well with high generalization on new event types. In this work, we formulate event detection as a few-shot learning problem to enable to extend event detection to new event types. We propose two novel loss factors that matching examples in the support set to provide more training signals to the model. Moreover, these training signals can be applied in many metric-based few-shot learning models. Our extensive experiments on the ACE-2005 dataset (under a few-shot learning setting) show that the proposed method can improve the performance of few-shot learning.
Drought‐induced leaf shedding in walnut: evidence for vulnerability segmentation

Trees of Juglans regia L. shed leaves when subjected to drought. Before shedding (when leaves are yellow), the petioles have lost 87% of their maximum hydraulic conductivity, but stems have lost only 14% of their conductivity. This is caused by the higher vulnerability of petioles than stems to water-stress induced cavitation. These data are discussed in the context of the plant segmentation hypothesis.
Plant Growth Promoting Characteristics in Some Flavobacterium spp. Isolated from Soils of Iran

Plant growth promoting rhizobacteria (PGPR) is referred to a heterogeneous group of beneficial rhizosphere bacteria that could enhance plant yield through one or more mechanisms. Flavobacterium has been noted as PGPR in almost all review articles. However, there are a few studies regarding plant growth promotion imposed by them. Some of Plant growth promoting characteristics such as Phosphate solubilizing capacity, ability to use of 1-Amino Cyclopropan-1-Carboxylate (ACC) as sole nitrogen source and production of auxin, siderophore, salicylic acid, chitinase and hydrogen cyanide were evaluated in forty-four flavobacteria isolated from rhizosphere of wheat. Results showed that none of the isolates were able to produce siderophore, salicylic acid and chitinase and they were not able to use ACC as well. Determining the siderophore showed that none isolates did not grow on Chrome Azurol S (CAS) Agar medium. The results of this part were further analyzed using CAS Agar Diffusion (CASAD) method, but the results were also negative. HCN production was observed in all isolates, but in lowest limit. Thirty-four isolates were capable to solubilize insoluble inorganic Phosphate (P) sources. The average rate of P-solubilization was 3.54 µg Pml -1 , ranging from zero to 37.48 µgPml -1 . There was a significant negative correlation (r = -0.81**) between solubilized P and the final pH of the growth medium. In this study, all the isolates were able to produce auxin, ranging from 0.27 to 12.03 µgml -1 averaged by 2.03. Considering the ability of the isolates to produce auxin and for P-solubilization, it is necessary to evaluate their effect on growth and yield of different crop plants.
A Mechanical Screwing Tool for Parallel Grippers—Design, Optimization, and Manipulation Policies

This article develops a mechanical screwing tool and its manipulation policies for two-finger parallel robotic grippers. The tool is based on a combined scissor-like element (SLE) and double-ratchet mechanism that converts the gripping motion of two-finger parallel grippers into a continuous rotation to realize tasks like fastening screws. The tool is entirely mechanical. There is no need for external cable connections. The manuscript includes two parts. For one thing, it shows the details of the tool design, optimizes the tool’s dimensions and effective stroke lengths, and studies the contacts and forces to achieve stable grasping and screwing. For another, it presents the related manipulation and control policies, including recognizing the tool, changing tool poses, and completing screw fastening tasks. The designed tool, together with the related manipulation and control policies, are analyzed and verified in several real-world applications. The results show that the tool has satisfying mechanical properties. Robots with parallel grippers can robustly and flexibly use the tool to fasten screws. The tool can also be used collaboratively with other tools to finish difficult tasks. In the future, similar tools are expected to replace special-purpose end-effectors or tool changers for more flexible robot integration.
Large-carnivore response to recreational big-game hunting along the Yellowstone National Park and Absaroka-Beartooth Wilderness boundary

The Greater Yellowstone Ecosystem contains the rare combination of an intact guild of native large carnivores, their prey, and differing land management policies (National Park versus National Forest; no hunting versus hunting). Concurrent field studies on large car- nivores allowed us to investigate activities of humans and carnivores on Yellowstone National Park's (YNP) northern boundary. Prior to and during the backcountry big-game hunting season, we monitored movements of grizzly bears (Ursus arctos), wolves (Canis lupus), and cougars (Puma concolor) on the northern boundary ofYNP. Daily aerial teleme- try locations (September 1999), augmented with weekly telemetry locations (August and October 1999), were obtained for 3 grizzly bears, 7 wolves in 2 groups of 1 pack, and 3 cougars in 1 family group. Grizzly bears were more likely located inside the YNP bound- ary during the pre-hunt period and north of the boundary once hunting began. The cougar family tended to be found outside YNP during the pre-hunt period and moved inside YNP when hunting began. Wolves did not significantly change their movement patterns during the pre-hunt and hunting periods. Qualitative information on elk (Cervus elaphus) indicat- ed they moved into YNP once hunting started, suggesting that cougars followed living prey or responded to hunting activity, grizzly bears focused on dead prey (e.g., gut piles, crip- pled elk), and wolves may have taken advantage of both. Measures of association (Jacob's Index) were positive within carnivore species but inconclusive among species. Further col- laborative research and the use of new technologies such as Global Positioning System (GPS) telemetry collars will advance our ability to understand these species, the carnivore community and its interactions, and human influences on carnivores.
Stochastic Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Stochastic Variance Reduction via Accelerated Dual Averaging (SVR-ADA)}. In the nonstrongly convex and smooth setting, SVR-ADA can attain an $O\big(\frac{1}{n}\big)$-accurate solution in $O(n\log\log n)$ number of stochastic gradient evaluations, where $n$ is the number of samples; meanwhile, SVR-ADA matches the lower bound of this setting up to a $\log\log n$ factor. In the strongly convex and smooth setting, SVR-ADA matches the lower bound in the regime $n\le O(\kappa)$ while it improves the rate in the regime $n\gg \kappa$ to $O(n\log\log n +\frac{n\log(1/(n\epsilon))}{\log(n/\kappa)})$, where $\kappa$ is the condition number. SVR-ADA improves complexity of the best known methods without use of any additional strategy such as optimal black-box reduction, and it leads to a unified convergence analysis and simplified algorithm for both the nonstrongly convex and strongly convex settings. Through experiments on real datasets, we also show the superior performance of SVR-ADA over existing methods for large-scale machine learning problems.
Seasonal occurrence and diet of leopard seals (Hydrurga leptonyx) at Bird Island, South Georgia

Seasonal haul-out patterns and diet of individually marked leopard seals (Hydrurga leptonyx) were investigated at Bird Island, South Georgia during the 1983–96 winters. A total of 2956 leopard seal sightings were made, and 121 seals were tagged during the study, mainly between 1993 and 1996. Photographs of scars and pelage patterns were also used to identify a subset of these individuals across years, which provided no evidence of tag loss between or within years. Leopard seals were observed between April and November; the mean time between the first and last sightings in each year was 208 d (s d ± 48). Between 1993–96, eight seals were resident around the island for more than 100 d, and the longest recorded residence was 130 d. The proportion of tagged seals resighted was 0.35 and 0.17 in 1995 and 1996 respectively. Based on estimates of body length, <5% of the seals were juveniles (0–1 years) and >70% were not sexually mature. There was considerable inter-annual variation in abundance, with a maximum of 502 sightings during 1994, compared with a minimum of 21 during 1986 and 1989. Antarctic fur seals (Arctocephalus gazella) were the main prey item (58% of kills observed and 53% of scats). Other items included penguins (28% of kills observed and 20% of scats) and fish (24% of scats). Antarctic krill (Euphausia superba), southern elephant seals (Mirounga leonina) and seabirds other than penguins were also present in the diet in small quantities.
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems

In recommender systems such as news feed stream, it is essential to optimize the long-term utilities in the continuous user-system interaction processes. Previous works have proved the capability of reinforcement learning in this problem. However, there are many practical challenges to implement deep reinforcement learning in online systems, including low sample efficiency, uncontrollable risks, and excessive variances. To address these issues, we pro-pose a novel reinforcement learning method, namely model-based counterfactual advantage learning (MBCAL). The proposed method takes advantage of the characteristics of recommender systems and draws ideas from the model-based reinforcement learning method for higher sample efficiency. It has two components: an environment model that predicts the instant user behavior one-by-one in an auto-regressive form, and a future advantage model that predicts the future utility. To alleviate the impact of excessive variance when learning the future advantage model, we employ counterfactual comparisons derived from the environment model. In consequence, the proposed method possesses high sample efficiency and significantly lower variance; Also, it is able to use existing user logs to avoid the risks of starting from scratch. In contrast to its capability, its implementation cost is relatively low, which fits well with practical systems. Theoretical analysis and elaborate experiments are presented. Results show that the proposed method transcends the other supervised learning and RL-based methods in both sample efficiency and asymptotic performances.
Revisiting complexity and the bias-variance tradeoff

The recent success of high-dimensional models, such as deep neural networks (DNNs), has led many to question the validity of the bias-variance tradeoff principle in high dimensions. We reexamine it with respect to two key choices: the model class and the complexity measure. We argue that failing to suitably specify either one can falsely suggest that the tradeoff does not hold. This observation motivates us to seek a valid complexity measure, defined with respect to a reasonably good class of models. Building on Rissanen's principle of minimum description length (MDL), we propose a novel MDL-based complexity (MDL-COMP). We focus on the context of linear models, which have been recently used as a stylized tractable approximation to DNNs in high-dimensions. MDL-COMP is defined via an optimality criterion over the encodings induced by a good Ridge estimator class. We derive closed-form expressions for MDL-COMP and show that for a dataset with $n$ observations and $d$ parameters it is \emph{not always} equal to $d/n$, and is a function of the singular values of the design matrix and the signal-to-noise ratio. For random Gaussian design, we find that while MDL-COMP scales linearly with $d$ in low-dimensions ($d n$) the scaling is exponentially smaller, scaling as $\log d$. We hope that such a slow growth of complexity in high-dimensions can help shed light on the good generalization performance of several well-tuned high-dimensional models. Moreover, via an array of simulations and real-data experiments, we show that a data-driven Prac-MDL-COMP can inform hyper-parameter tuning for ridge regression in limited data settings, sometimes improving upon cross-validation.