Miracle-dz/UniFAR-FTUs huggingface dataset:
Demographic variation and population viability in a threatened Himalayan medicinal and aromatic herb Nardostachys grandiflora: matrix modelling of harvesting effects in two contrasting habitats

Summary 1 Despite significant interest in the sustainable use of medicinal plants, the population ecology of many medicinal plant species remains unstudied. Also, few demographic studies have compared effects of harvesting across habitats. We studied the demography of a highly threatened perennial Himalayan medicinal herb, Nardostachys grandiflora, in two habitats and examined effects of indigenous harvesting regimes on its population dynamics. 2 In a rocky-outcrop population in 1998, different levels of rhizome harvesting were applied directly by the local users. In meadows, we sampled an unharvested population and another population that was commercially harvested. Data on recruitment, growth and survival of individuals classified by size were gathered over 4 years. 3 Population growth rate (λ) was significantly higher in the unharvested meadow population than in control subplots of the outcrop population. Harvesting significantly reduced λ. Matrix model projections revealed that the size of outcrop populations would return to initial values within ≈5 years, only after 10% rhizome harvesting. In other treatments, recovery time varied from 17 (25% harvesting) to 33 (75%) years. In contrast, in the commercially harvested meadow population (with harvesting levels >25%), projected recovery time was 6 years. 4 Higher growth rates and faster recovery in meadow populations appear to be due to higher recruitment and faster vegetative growth. In outcrops, slow growth and low fecundity slow down recovery after harvesting. 5 Synthesis and application. Nardostachys grandiflora is extremely sensitive to harvesting of rhizomes, but this sensitivity is higher in outcrop than in meadow habitats. Given the constraints on its population growth and its high sensitivity to harvesting, N. grandiflora should be strictly managed, with low harvest rates (<10% in outcrop and <25% in meadow) and fairly long rotations (at least 5 years) between successive harvests. This work shows the importance of considering demographic variation across habitats when formulating specific management plans for threatened medicinal plants. It also demonstrates the importance of integrating local harvesting practices in ecological research for informing management. A large proportion of high-altitude perennial medicinal plants may be expected to be similarly sensitive to exploitation, requiring careful management to achieve sustainable harvesting.
DREAM: Deep Regret minimization with Advantage baselines and Model-free learning

We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash Equilibrium in two-player zero-sum games and to an extensive-form coarse correlated equilibrium in all other games. Our primary innovation is an effective algorithm that, in contrast to other regret-based deep learning algorithms, does not require access to a perfect simulator of the game to achieve good performance. We show that DREAM empirically achieves state-of-the-art performance among model-free algorithms in popular benchmark games, and is even competitive with algorithms that do use a perfect simulator.
Kinetic and Energetic Aspects of Inorganic Sulphur Compound Oxidation by Thiobacillus tepidarius

SUMMARY: Whole organisms of Thiobacillus tepidarius oxidize thiosulphate to sulphate with the obligatory formation of tetrathionate as an intermediate. Oxidation of thiosulphate to tetrathionate shows an apparent K m of about 120 μM and is relatively insensitive to FCCP (carbonyl cyanide p-trifluoromethoxyphenylhydrazone), HQNO (2-heptyl-4-hydroxyquinoline-N-oxide) or thiocyanate. Oxidation of tetrathionate to sulphate shows a K m of about 27 μM and is strongly inhibited by FCCP, HQNO, thiocyanate and gramicidin. Sulphite oxidation is also inhibited by FCCP and HQNO. Trithionate oxidation to sulphate occurred and showed unexplained dependence on the presence of sulphate ions. A H+/O quotient of about 4 for proton translocation driven by substrate oxidation was seen for each of thiosulphate, tetrathionate and sulphite. ATP synthesis coupled to thiosulphate oxidation was completely abolished by FCCP. The results obtained are consistent with the oxidation of thiosulphate (and probably trithionate) to tetrathionate in the periplasm of the cell, with HQNO-insensitive electron transport to cytochrome c, and with further oxidation of tetrathionate (and sulphite) to sulphate after FCCP-sensitive transport to the cytoplasmic side of the membrane. The latter oxidations involve HQNO-sensitive electron transport via cytochrome b. Inhibition of tetrathionate metabolism by thiocyanate and gramicidin would be consistent with tetrathionate transport by a S4O-/4H+ symport process. The proton translocation experiments indicate the mechanism of H+ extrusion to depend on electron transfer within the quinone/cytochromes bc segment of the respiratory chain, and does not involve a proton-pumping oxidase. The sulphur-compound-oxidizing system of T. tepidarius is shown to be quite different from that previously described for T. versutus.
Distributed Value Function Approximation for Collaborative Multiagent Reinforcement Learning

In this article, we propose several novel distributed gradient-based temporal-difference algorithms for multiagent off-policy learning of linear approximation of the value function in Markov decision processes with strict information structure constraints, limiting interagent communications to small neighborhoods. The algorithms are composed of the following: first, local parameter updates based on the single-agent off-policy gradient temporal-difference learning algorithms, including the eligibility traces with state-dependent parameters and, second, linear stochastic time-varying consensus schemes, represented by directed graphs. The proposed algorithms differ in their form, definition of eligibility traces, selection of time scales, and the way of incorporating consensus iterations. The main contribution of this article is a convergence analysis based on the general properties of the underlying Feller–Markov processes and the stochastic time-varying consensus model. We prove under general assumptions that the parameter estimates generated by all the proposed algorithms weakly converge to the corresponding ordinary differential equations with precisely defined invariant sets. It is demonstrated how the adopted methodology can be applied to temporal-difference algorithms under weaker information structure constraints. The variance reduction effect of the proposed algorithms is demonstrated by formulating and analyzing an asymptotic stochastic differential equation. Specific guidelines for the communication network design are provided. The algorithms’ superior properties are illustrated by characteristic simulation results.
The ecology of a Mediterranean tortoise (Testudo hermanni): a declining population

In France, wild tortoises occur at low densities ( 10/ha). The population structure is heavily skewed in favour of old adults and recruitment is low. The population is declining due to habitat loss, both from fire and through the decline in traditional horticultural practices, leading to a reduction in nest site availability. The increase in nest density in the few suitable sites remaining leaves the eggs highly vulnerable to predation and losses of over 90% cannot be sustained. The active season is from March to November, but there is evidence of some activity during the hibernation period. Adult males maintain small, fixed home ranges, while females undertake distinct migrations to and from nesting sites in the spring and can switch to new home ranges. Juveniles are sedentary up to the age of 4 or 5 years, a.
Re-discussion about CT signs of novel coronavirus pneumonia

Objective To search for the characteristic CT signs of the new coronavirus pneumonia(NCP) and to explore its clinical significance from the perspective of imaging pathology. Methods CT images of 40 NCP patients in Hebei province were collected and a total of 459 lesions in 234 lung segments on 72 sides were analyzed. Meanwhile, CT images of totally 258 lesions in 79 lung segments on 28 sides of 20 non-NCP patients randomly selected and treated in our hospital were compared with those of NCP patients above. Results In the NCP group, the lesions were mostly located in the lower lobe of the two lungs (30 cases, accounting for 23. 44% respectively), with bilateral involvement (32 cases, accounting for 80.00%). The proportion of lesions involving 5 pulmonary lobes was relatively large, with little difference in the distribution of left and right lung, and only 1 case (2.50%) with pleural effusion. There were statistically significant differences between NCP group and non-NCP group in fan-shaped shape, peripheral distribution, pulmonary hyperemia or pulmonary edema, accompanied by vascular thickening and bronchial wall thickening. These suggested that the lesions of NCP were initially in the periphery and gradually infiltrate and fuses on the basis of fan-shaped structures. And the lesions were mostly accompanied by vascular thickening and pulmonary hyperemia, but with little bronchial wall thickening. Conclusion The CT signs of NCP are characteristic, and they may be more likely to invade blood vessels and cause vasculitis, which may lead to pulmonary edema and cardiopulmonary circulation disorder. Key words: novel coronavirus pneumonia; fan structure; cardio-pulmonary circulation disorder; computed tomography; pulmonary lobule
Possible biogeochemical response to the passage of Hurricane Fabian observed by satellites

Physical and biogeochemical changes induced by the Hurricane Fabian in the Northwest Atlantic in early September 2003 were observed using composite satellite images. After the passage of the hurricane, the mean sea surface temperature (SST) along the track decreased on average by about 1.3°C with maximum decrease of 10°C. At the same time, the mean Chl a concentration increased by about 42%. Entrainment of cold, nutrient-rich waters by vertical mixing induced by the hurricane seems to have enhanced the phytoplankton production. Asymmetric distribution of changes in SST and Chl a (strong intensity on the right side of the storm track) was observed from the satellite data. The storm-induced nitrate increase estimated from the satellite SST, using a local relationship between nitrate and temperature measurements was about 40% on average along the track of the storm. A numerical model study and climatological nutrient profile showed an increase in mixed-layer depth of 26 m and nitrate increase of about 0.2 μmol L -1 after the storm passage. In addition to altering the physicochemical conditions of the water column, physical forcing by the hurricane also changed the taxonomic composition of phytoplankton. It is inferred that the dominance of diatoms after the storm is a result of the increase in nutrient concentration within the mixed layer due to the wind forcing of the storm.
Bromus-Poa response to defoliation intensity and frequency under three soil moisture levels

Smooth brome (Bromus inermis Leyss.) and Kentucky bluegrass (Poa pratensis L.) are important herbage for livestock and wildlife in Aspen-Boreal ecosystems in central Alberta, but there is paucity of information on the relationship between soil moisture and defoliation regimes on herbage production in these ecosystems. In a greenhouse experiment, we evaluated the effect of Bromus-Poa defoliation frequencies (2 or 4 wk) and intensities (2.5, 7.5, or 15 cm above the soil surface) under three soil moisture regimes [field capacity (wet), 50% field capacity (moist), 20% field capacity (dry)] on dry matter (DM) yield. Crude protein (CP) content, crude protein yield (CPY) and neutral detergent fiber (NDF) were also determined for herbage harvested. Total accumulated shoot DM decreased under defoliation compared to the undefoliated control, was higher if plants were clipped every 4 wk, rather than 2 wk, and increased with increasing soil moisture availability. Defoliation regimes decreased root DM Compared to the .
Active Learning for Nonlinear System Identification with Guarantees

While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i.i. d. random inputs. Nonetheless, many interesting dynamical systems have continuous states and actions and can only be identified through a judicious choice of inputs. Motivated by practical settings, we study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. To estimate such systems in finite time identification methods must explore all directions in feature space. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning

We present Shapeshifter Networks (SSNs), a flexible neural network framework that improves performance and reduces memory requirements on a diverse set of scenarios over standard neural networks. Our approach is based on the observation that many neural networks are severely overparameterized, resulting in significant waste in computational resources as well as being susceptible to overfitting. SSNs address this by learning where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting. Specifically, we automatically construct parameter groups that identify where parameter sharing is most beneficial. Then, we map each group's weights to construct layers with learned combinations of candidates from a shared parameter pool. SSNs can share parameters across layers even when they have different sizes, perform different operations, and/or operate on features from different modalities. We evaluate our approach on a diverse set of tasks, including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1% of the parameters. We also apply SSNs to knowledge distillation, where we obtain state-of-the-art results when combined with traditional distillation methods.
Uncertainty in Gradient Boosting via Ensembles

Gradient boosting is a powerful machine learning technique that is particularly successful for tasks containing heterogeneous features and noisy data. While gradient boosting classification models return a distribution over class labels, regressions models typically yield only point predictions. However, for many practical, high-risk applications, it is also important to be able to quantify uncertainty in the predictions to avoid costly mistakes. In this work, we examine a probabilistic ensemble-based framework for deriving uncertainty estimates in the predictions of gradient boosting classification and regression models. Crucially, the proposed approach allows the total uncertainty to be decomposed into \textit{data uncertainty}, which comes from the complexity and noise in data distribution, and \textit{knowledge uncertainty}, coming from the lack of information about a given region of the feature space. Two approaches for generating ensembles are considered: Stochastic Gradient Boosting (SGB) and Stochastic Gradient Langevin Boosting (SGLB). Notably, SGLB also enables the generation of a \emph{virtual} ensemble via only one gradient boosting model, which significantly reduces complexity. Experiments on a range of regression and classification datasets show that ensembles of gradient boosting models yield improved predictive performance, and measures of uncertainty successfully enable detection of out-of-domain inputs.
Environmental Impact on Star-forming Galaxies in a z ∼ 0.9 Cluster during the Course of Galaxy Accretion

Galaxies change their properties as they assemble into clusters. In order to understand the physics behind that, we need to go back in time and observe directly what is occurring in galaxies as they fall into a cluster. We have conducted a narrowband and J-band imaging survey on a cluster CL1604-D at z = 0.923 using a new infrared instrument SWIMS installed at the Subaru Telescope. The narrowband filter, NB1261, matches to Hα emission from the cluster at z = 0.923. Combined with a wide range of existing data from various surveys, we have investigated galaxy properties in and around this cluster in great detail. We have identified 27 Hα emitters associated with the cluster. They have significant overlap with MIPS sources and are located exclusively in the star-forming regime on the rest-frame UVJ diagram. We have identified two groups of galaxies near the cluster in the 2D spatial distribution and the phase-space diagram, which are likely to be in-falling to the cluster main body. We have compared various physical properties of star-forming galaxies, such as specific star formation rates (burstiness) and morphologies (merger) as a function of environment, cluster center, older group, younger group, and the field. As a result, a global picture has emerged on how the galaxy properties are altered as they assemble into a denser region. This includes the occurrence of mergers, enhancement of star formation activity, excursion to the dusty starburst phase, and eventual quenching to a passive phase.
Studies in oil-seed crops: II. An assessment of the interrelationships between plant development and seed production in linseed (Linum usitatissimum)

Records of plant development in some twenty-five multifactorial experiments were collected with the object of analysing the effects of nutrient level, density, spatial arrangement and variety on seed production in linseed. By sampling techniques estimates were made of plant density, the extent of basal branching, the number of capsules per stem, the number of seeds per capsule and the seed weight. The expected plot yields, calculated from these data, were in good agreement with the harvested yields. In factorial trials, involving additions of nitrogen, phosphorus and potassium, only a change in the nitrogen level increased the yield of seed, and this gain was largely determined by an increase in the production of capsules and not by any alteration in either the number of seeds per capsule or the seed weight. Over a range of density of thirty to eighty plants per square foot the number of capsules produced per plant was inversely correlated with density. When the distance between drills was maintained at 6–7 in. and the density varied by changing the seed rate then—on an area basis—both capsule production and seed yield were rarely affected since the product of plant number and capsules per plant remained relatively constant. On the other hand, if the rate of seed delivery in the drill was kept the same and the distance between the drills was the variable for density then with a narrow width of drill (4 in.) the increase in plant density more than offset the fall in capsules per plant. Further experiments comparing drilling and crossdrilling did not wholly confirm the importance of spatial arrangement. There was, however, evidence that at densities over eighty plants per square foot, besides a further reduction in capsules per plant, the number of seeds per capsule was depressed. Varieties were characterized by differences in seed size, the number of seeds per capsule, capsules per plant and the ability to produce more capsules at high levels of nitrogen. The superiority of the yielding capacity of Royal could be ascribed to the high rate of capsule production. Such varietal differences in capsule production have not been reported previously, and since the number of capsules per plant is greatly influenced by varying density, the common practice of basing selection on spaced plants, may not give a true assessment of capsule formation at densities optimal for seed production.
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination

We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate encoder outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with <1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with <1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at this https URL.
Phytoplankton community structure derived from HPLC analysis of pigments in the Faroe-Shetland Channel during summer 1999: the distribution of taxonomic groups in relation to physical/chemical conditions in the photic zone

A study of the phytoplankton community in the Faroe-Shetland Channel was conducted in July 1999. Samples were collected at various depths in the photic zone along three transects (the northern entrance, the center and the southern entrance). Except for a few easterly stations where nitrate and silicate were below 1 µM, all nutrients (phosphate, silicate, ammonium, nitrite and nitrate) were non-limiting for phytoplankton growth. HPLC pigment analysis revealed a pronounced (>50%) dominance of Prymnesiophyceae at all stations. Their pigment ratio of diatoxanthin + diadinoxanthin/Chl a (DDX/Chl a) indicated that the phytoplankton community was controlled by light. Primary production in the delayed spring bloom varied from 1.2 to 1.8 g C m ‐2 day ‐1 along the northern transect. Along the other two transects, primary production ranged from 1.6 to 3.8 g C m ‐2 day ‐1 . Associated with the characteristics indicating the establishment of a bloom, the relative contribution of diatoms and Prymnesiophyceae increased, whereas that of Prasinophyceae, Cryptophyceae, Chrysophyceae and Cyanobacteriaceae decreased. With respect to their vertical distribution, Cyanobacteriaceae, Chrysophyceae and Dinophyceae tended to have a higher abundance, relative to other taxonomic groups, in the surface layers. The relative abundance of diatoms and Chlorophyceae increased with depth. The DDX/Chl a ratio of the Prymnesiophyceae decreased with depth, indicating that vertical mixing in the upper 30 m of the photic zone occurred less frequently than the time span of physiological acclimation of cellular pigment composition.