An iterative procedure to obtain inverse response functions for thick-target correction of measured charged-particle spectra

A new method for correcting charged-particle spectra for thick target effects is described. Starting with a trial function, inverse response functions are found by an iterative procedure. The variances corresponding to the measured spectrum are treated similiarly and in parallel. Oscillations of the solution are avoided by rebinning the data to finer bins during a correction iteration and back to the original or wider binning after each iteration. This thick-target correction method has been used for data obtained with the MEDLEY facility at the The Svedberg Laboratory, Uppsala, Sweden, and is here presented in detail and demonstrated for two test cases.Comment: 14 pages, 8 figures, submitted to NIM

Distributed Edge Connectivity in Sublinear Time

We present the first sublinear-time algorithm for a distributed message-passing network sto compute its edge connectivity $\lambda$ exactly in the CONGEST model, as long as there are no parallel edges. Our algorithm takes $\tilde O(n^{1-1/353}D^{1/353}+n^{1-1/706})$ time to compute $\lambda$ and a cut of cardinality $\lambda$ with high probability, where $n$ and $D$ are the number of nodes and the diameter of the network, respectively, and $\tilde O$ hides polylogarithmic factors. This running time is sublinear in $n$ (i.e. $\tilde O(n^{1-\epsilon})$) whenever $D$ is. Previous sublinear-time distributed algorithms can solve this problem either (i) exactly only when $\lambda=O(n^{1/8-\epsilon})$ [Thurimella PODC'95; Pritchard, Thurimella, ACM Trans. Algorithms'11; Nanongkai, Su, DISC'14] or (ii) approximately [Ghaffari, Kuhn, DISC'13; Nanongkai, Su, DISC'14]. To achieve this we develop and combine several new techniques. First, we design the first distributed algorithm that can compute a $k$-edge connectivity certificate for any $k=O(n^{1-\epsilon})$ in time $\tilde O(\sqrt{nk}+D)$. Second, we show that by combining the recent distributed expander decomposition technique of [Chang, Pettie, Zhang, SODA'19] with techniques from the sequential deterministic edge connectivity algorithm of [Kawarabayashi, Thorup, STOC'15], we can decompose the network into a sublinear number of clusters with small average diameter and without any mincut separating a cluster (except the `trivial' ones). Finally, by extending the tree packing technique from [Karger STOC'96], we can find the minimum cut in time proportional to the number of components. As a byproduct of this technique, we obtain an $\tilde O(n)$-time algorithm for computing exact minimum cut for weighted graphs.Comment: Accepted at 51st ACM Symposium on Theory of Computing (STOC 2019

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14]

L19-IL2 immunocytokine in combination with the anti-syndecan-1 46F2SIP antibody format: A new targeted treatment approach in an ovarian carcinoma model

Epithelial ovarian cancer (EOC) is the fifth most common cancer affecting the female population. At present, different targeted treatment approaches may improve currently employed therapies leading either to the delay of tumor recurrence or to disease stabilization. In this study we show that syndecan-1 (SDC1) and tumor angiogenic-associated B-fibronectin isoform (B-FN) are involved in EOC progression and we describe the prominent role of SDC1 in the vasculogenic mimicry (VM) process. We also investigate a possible employment of L19-IL2, an immunocytokine specific for B-FN, and anti-SDC1 46F2SIP (small immuno protein) antibody in combination therapy in a human ovarian carcinoma model. A tumor growth reduction of 78% was obtained in the 46F2SIP/L19-IL2-treated group compared to the control group. We observed that combined treatment was effective in modulation of epithelial-mesenchymal transition (EMT) markers, loss of stemness properties of tumor cells, and in alleviating hypoxia. These effects correlated with reduction of VM structures in tumors from treated mice. Interestingly, the improved pericyte coverage in vascular structures suggested that combined therapy could be efficacious in induction of vessel normalization. These data could pave the way for a possible use of L19-IL2 combined with 46F2SIP antibody as a novel therapeutic strategy in EOC