Soil organic carbon in the rocky desert of northern Negev (Israel)

Purpose: So far, the soil organic carbon (SOC) literature is dominated by studies in the humid environments with huge stocks of vulnerable carbon. Limited attention has been given to dryland ecosystems despite being often considered to be highly sensitive to environmental change. Thus, there is insufficient research about the spatial patterns of SOC stocks and the interaction between soil depth, ecohydrology, geomorphic processes, and SOC stocks. This study aimed at identifying the relationship between surface characteristics, vegetation coverage, SOC, and SOC stocks in the arid northern Negev in Israel. Materials and methods: The study site Sede Boker is ideally suited because of well-researched but variable ecohydrology. For this purpose, we sampled five slope sections with different ecohydrologic characteristics (e.g., soil and vegetation) and calculate SOC stocks. To identify controlling factors of SOC stocks on rocky desert slopes, we compared soil properties, vegetation coverage, SOC concentration, and stocks between the five ecohydrologic units. Results and discussion: The results show that in Sede Boker, rocky desert slopes represent a significant SOC pool with a mean SOC stock of 0.58kgCm−2 averaged over the entire study area. The spatial variability of the soil coverage represents a strong control on SOC stocks, which varies between zero in uncovered areas and 1.54kgCm−2 on average in the soil-covered areas. Aspect-driven changes of solar radiation and thus of water availability are the dominant control of vegetation coverage and SOC stock in the study area. Conclusions: The data indicate that dryland soils contain a significant amount of SOC. The SOC varies between the ecohydrologic units, which reflect (1) aspect-driven differences, (2) microscale topography, (3) soil formation, and (4) vegetation coverage, which are of greatest importance for estimating SOC stocks in dryland

A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions

We design algorithms for minimizing $\max_{i\in[n]} f_i(x)$ over a $d$-dimensional Euclidean or simplex domain. When each $f_i$ is $1$-Lipschitz and $1$-smooth, our method computes an $\epsilon$-approximate solution using $\widetilde{O}(n \epsilon^{-1/3} + \epsilon^{-2})$ gradient and function evaluations, and $\widetilde{O}(n \epsilon^{-4/3})$ additional runtime. For large $n$, our evaluation complexity is optimal up to polylogarithmic factors. In the special case where each $f_i$ is linear -- which corresponds to finding a near-optimal primal strategy in a matrix game -- our method finds an $\epsilon$-approximate solution in runtime $\widetilde{O}(n (d/\epsilon)^{2/3} + nd + d\epsilon^{-2})$. For $n>d$ and $\epsilon=1/\sqrt{n}$ this improves over all existing first-order methods. When additionally $d = \omega(n^{8/11})$ our runtime also improves over all known interior point methods. Our algorithm combines three novel primitives: (1) A dynamic data structure which enables efficient stochastic gradient estimation in small $\ell_2$ or $\ell_1$ balls. (2) A mirror descent algorithm tailored to our data structure implementing an oracle which minimizes the objective over these balls. (3) A simple ball oracle acceleration framework suitable for non-Euclidean geometry

Phorbol Ester (TPA)-Induced Surface Membrane Alterations in B-Type Hairy Cell and Lymphocytic Leukemia Cells

This report documents phorbol ester (TPA)-induced changes in cell morphology, and in vitro growth patterns in 9 patients with hairy cell leukemia (HCL), 21 with B-type CLL and non-Hodgkin\u27s lymphoma in leukemic phase (NHL), and 10 with acute non lymphoblastic leukemia (ANLL). TPA caused cells from HCL to adhere strongly and produce elongated cytoplasmic extensions. Many of these cells had an appearance resembling fibroblasts, while others showed marked surface ruffling and spreading containing increased dense bodies, and phagolysosomal vacuoles as seen by transmission electron microscopy. This HCL in vitro growth pattern after TPA exposure differed from that seen in B-CLL and NHL cells, which only adhered moderately after 72 hours and readily detached in clumps. CLL and NHL-cells did not show ultrastructural features of macrophages but had either plasmacytic or HCL features. It is suggested that these different growth patterns may aid in distinguishing HCL from other B-cell neoplasias. The expression of surface markers, tartrate resistant acid phosphatase (TRAP) and Ig secretion were studied in some B-CLL, NHL and HCL cells after exposure to different concentrations of TPA for up to 6 days. Results showed that the documented changes were frequently both dose and time dependent and the most striking HCL-features were encountered after 6 days incubation with higher concentrations of TPA. However, individual variation from case to case was noted. Nevertheless, it seems that TPA induces neoplastic B-cells to mature into secreting plasmacytoid lymphocytes, and cells with features of HCL with variable expression of surface markers and TRAP

ReSQueing Parallel and Private Stochastic Convex Optimization

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. Given $n$ samples of Lipschitz loss functions, prior works [BFTT19, BFGT20, AFKT21, KLL21] established that if $n \gtrsim d \epsilon_{\text{dp}}^{-2}$, $(\epsilon_{\text{dp}}, \delta)$-differential privacy is attained at no asymptotic cost to the SCO utility. However, these prior works all required a superlinear number of gradient queries. We close this gap for sufficiently large $n \gtrsim d^2 \epsilon_{\text{dp}}^{-3}$, by using ReSQue to design an algorithm with near-linear gradient query complexity in this regime